From 83b1abbf76b73bc872d5b5c21efbc0c9ab7e1a59 Mon Sep 17 00:00:00 2001
From: chenxiaodong <chenxiaodong@qibebt.ac.cn>
Date: Fri, 22 Nov 2024 10:03:31 +0800
Subject: [PATCH] =?UTF-8?q?=E8=AE=BA=E6=96=87=E6=8F=90=E4=BA=A4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                                    |     3 +
 AgentDDPG/DRL_10_plots/gurobi.svg             |  3930 +
 AgentDDPG/DRL_10_plots/rl.svg                 |  4526 ++
 AgentDDPG/actor.pth                           |   Bin 0 -> 545576 bytes
 AgentDDPG/loss_data.pkl                       |   Bin 0 -> 86 bytes
 AgentDDPG/reward_data.pkl                     |   Bin 0 -> 76 bytes
 AgentDDPG/test_data.pkl                       |   Bin 0 -> 10347 bytes
 AgentPPO/DRL_10_plots/gurobi.svg              |  4401 ++
 AgentPPO/DRL_10_plots/rl.svg                  |  4910 ++
 AgentPPO/DRL_llm_1015_plots/energy.png        |   Bin 0 -> 461371 bytes
 AgentPPO/DRL_llm_1015_plots/gurobi.svg        |  4441 ++
 AgentPPO/DRL_llm_1015_plots/melt.png          |   Bin 0 -> 525619 bytes
 AgentPPO/DRL_llm_1015_plots/rl.svg            |  5006 ++
 AgentPPO/DRL_llm_1015_plots/soc.png           |   Bin 0 -> 163328 bytes
 AgentPPO/DRL_ram_plots/gurobi.svg             |  4388 ++
 AgentPPO/actor_10.pth                         |   Bin 0 -> 546313 bytes
 AgentPPO/actor_llm_1015.pth                   |   Bin 0 -> 546391 bytes
 AgentPPO/actor_ram.pth                        |   Bin 0 -> 546326 bytes
 AgentPPO/loss_10.pkl                          |   Bin 0 -> 27092 bytes
 AgentPPO/loss_llm_1015.pkl                    |   Bin 0 -> 27092 bytes
 AgentPPO/loss_ram.pkl                         |   Bin 0 -> 20092 bytes
 AgentPPO/melt_llm.pkl                         |   Bin 0 -> 863 bytes
 AgentPPO/melt_lmppo.pkl                       |   Bin 0 -> 863 bytes
 AgentPPO/melt_milp.pkl                        |   Bin 0 -> 754 bytes
 AgentPPO/melt_ppo.pkl                         |   Bin 0 -> 863 bytes
 AgentPPO/melt_ram.pkl                         |   Bin 0 -> 863 bytes
 AgentPPO/reward_10.pkl                        |   Bin 0 -> 24815 bytes
 AgentPPO/reward_llm_1015.pkl                  |   Bin 0 -> 19465 bytes
 AgentPPO/reward_ram.pkl                       |   Bin 0 -> 28165 bytes
 AgentPPO/test_10.pkl                          |   Bin 0 -> 8442 bytes
 AgentPPO/test_llm_1015.pkl                    |   Bin 0 -> 9851 bytes
 AgentPPO/test_ram.pkl                         |   Bin 0 -> 8562 bytes
 AgentSAC/DRL_10_plots/gurobi.svg              |  3930 +
 AgentSAC/DRL_10_plots/rl.svg                  |  4474 ++
 AgentSAC/actor.pth                            |   Bin 0 -> 814994 bytes
 AgentSAC/loss_data.pkl                        |   Bin 0 -> 86 bytes
 AgentSAC/reward_data.pkl                      |   Bin 0 -> 76 bytes
 AgentSAC/test_data.pkl                        |   Bin 0 -> 10364 bytes
 AgentTD3/DRL_10_plots/gurobi.svg              |  3930 +
 AgentTD3/DRL_10_plots/rl.svg                  |  4526 ++
 AgentTD3/actor.pth                            |   Bin 0 -> 545576 bytes
 AgentTD3/loss_data.pkl                        |   Bin 0 -> 86 bytes
 AgentTD3/reward_data.pkl                      |   Bin 0 -> 76 bytes
 AgentTD3/test_data.pkl                        |   Bin 0 -> 10347 bytes
 DDPG.py                                       |   127 +
 PPO.py                                        |   374 +
 PPO_llm.py                                    |   472 +
 PPO_llm_melt.py                               |   136 +
 PPO_llm_melt_2.py                             |   200 +
 PPO_ramdom.py                                 |   371 +
 SAC.py                                        |   130 +
 TD3.py                                        |   127 +
 agent.py                                      |   313 +
 compare.png                                   |   Bin 0 -> 2071660 bytes
 data/houseload.csv                            |  8761 +++
 data/irradiance.csv                           |  8761 +++
 data/prices.csv                               |  8761 +++
 data/results.json                             | 61322 ++++++++++++++++
 data/results_day.json                         | 61322 ++++++++++++++++
 data/temper.csv                               |  8761 +++
 data/wind.csv                                 |  8761 +++
 data_manager.py                               |    70 +
 environment.py                                |   164 +
 excel.py                                      |    40 +
 generate_household_data.py                    |    35 +
 llm.txt                                       |    50 +
 llm_connect.py                                |    73 +
 llma/Reflexion.py                             |   122 +
 llma/constraint.py                            |   312 +
 llma/constraint_model.py                      |   360 +
 llma/data/adv_code.py                         |   112 +
 llma/data/adv_code_2.py                       |   130 +
 llma/data/code.py                             |   184 +
 llma/data/data.csv                            |  8761 +++
 llma/data/data.json                           |    14 +
 llma/data/desc.txt                            |    28 +
 llma/data/labels.json                         |     4 +
 .../data_level0.bin                           |   Bin 0 -> 12428000 bytes
 .../header.bin                                |   Bin 0 -> 100 bytes
 .../length.bin                                |   Bin 0 -> 4000 bytes
 .../link_lists.bin                            |     0
 .../rag/constraint_vector.db/chroma.sqlite3   |   Bin 0 -> 10002432 bytes
 llma/data/rag/constraints.pkl                 |   Bin 0 -> 351796 bytes
 .../data_level0.bin                           |   Bin 0 -> 12428000 bytes
 .../header.bin                                |   Bin 0 -> 100 bytes
 .../length.bin                                |   Bin 0 -> 4000 bytes
 .../link_lists.bin                            |     0
 .../chroma.sqlite3                            |   Bin 0 -> 3121152 bytes
 .../data_level0.bin                           |   Bin 0 -> 12428000 bytes
 .../header.bin                                |   Bin 0 -> 100 bytes
 .../length.bin                                |   Bin 0 -> 4000 bytes
 .../link_lists.bin                            |     0
 .../chroma.sqlite3                            |   Bin 0 -> 3776512 bytes
 llma/data/results_day.json                    | 61322 ++++++++++++++++
 llma/data/state_1_params.json                 |    58 +
 llma/data/state_2_objective.json              |    63 +
 llma/data/state_3_constraints.json            |   135 +
 llma/data/state_4_constraints_modeled.json    |   217 +
 llma/data/state_5_objective_modeled.json      |   216 +
 llma/data/state_6_code.json                   |   220 +
 llma/execute_code.py                          |    93 +
 llma/generate_code.py                         |    73 +
 llma/gurobi.py                                |   120 +
 llma/main.py                                  |   124 +
 llma/objective.py                             |    67 +
 llma/objective_model.py                       |    95 +
 llma/optimus_tools.py                         |   127 +
 llma/parameters.py                            |   127 +
 llma/rag/constraints_to_vector_db.ipynb       |   148 +
 llma/rag/extract_constraints_from_logs.ipynb  |   181 +
 llma/rag/query_vector_db.py                   |   225 +
 llma/rag/rag_utils.py                         |    25 +
 llma/results.json                             | 61322 ++++++++++++++++
 llma/target_code.py                           |   186 +
 llma/utils.py                                 |   154 +
 measure.py                                    |    34 +
 melt_test.py                                  |    11 +
 module.py                                     |   159 +
 net.py                                        |   136 +
 parameters.py                                 |    47 +
 plotDRL.py                                    |   328 +
 plott.py                                      |   126 +
 res/ddpg.txt                                  |   Bin 0 -> 184334 bytes
 res/ppo.txt                                   |  1000 +
 res/ppo_1016.txt                              |  1005 +
 res/ppo_llm.txt                               |  1000 +
 res/sac.txt                                   |  1000 +
 res/td3.txt                                   |  1000 +
 tools.py                                      |   290 +
 129 files changed, 359057 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 AgentDDPG/DRL_10_plots/gurobi.svg
 create mode 100644 AgentDDPG/DRL_10_plots/rl.svg
 create mode 100644 AgentDDPG/actor.pth
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 create mode 100644 AgentPPO/DRL_10_plots/gurobi.svg
 create mode 100644 AgentPPO/DRL_10_plots/rl.svg
 create mode 100644 AgentPPO/DRL_llm_1015_plots/energy.png
 create mode 100644 AgentPPO/DRL_llm_1015_plots/gurobi.svg
 create mode 100644 AgentPPO/DRL_llm_1015_plots/melt.png
 create mode 100644 AgentPPO/DRL_llm_1015_plots/rl.svg
 create mode 100644 AgentPPO/DRL_llm_1015_plots/soc.png
 create mode 100644 AgentPPO/DRL_ram_plots/gurobi.svg
 create mode 100644 AgentPPO/actor_10.pth
 create mode 100644 AgentPPO/actor_llm_1015.pth
 create mode 100644 AgentPPO/actor_ram.pth
 create mode 100644 AgentPPO/loss_10.pkl
 create mode 100644 AgentPPO/loss_llm_1015.pkl
 create mode 100644 AgentPPO/loss_ram.pkl
 create mode 100644 AgentPPO/melt_llm.pkl
 create mode 100644 AgentPPO/melt_lmppo.pkl
 create mode 100644 AgentPPO/melt_milp.pkl
 create mode 100644 AgentPPO/melt_ppo.pkl
 create mode 100644 AgentPPO/melt_ram.pkl
 create mode 100644 AgentPPO/reward_10.pkl
 create mode 100644 AgentPPO/reward_llm_1015.pkl
 create mode 100644 AgentPPO/reward_ram.pkl
 create mode 100644 AgentPPO/test_10.pkl
 create mode 100644 AgentPPO/test_llm_1015.pkl
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 create mode 100644 AgentSAC/DRL_10_plots/gurobi.svg
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 create mode 100644 AgentSAC/loss_data.pkl
 create mode 100644 AgentSAC/reward_data.pkl
 create mode 100644 AgentSAC/test_data.pkl
 create mode 100644 AgentTD3/DRL_10_plots/gurobi.svg
 create mode 100644 AgentTD3/DRL_10_plots/rl.svg
 create mode 100644 AgentTD3/actor.pth
 create mode 100644 AgentTD3/loss_data.pkl
 create mode 100644 AgentTD3/reward_data.pkl
 create mode 100644 AgentTD3/test_data.pkl
 create mode 100644 DDPG.py
 create mode 100644 PPO.py
 create mode 100644 PPO_llm.py
 create mode 100644 PPO_llm_melt.py
 create mode 100644 PPO_llm_melt_2.py
 create mode 100644 PPO_ramdom.py
 create mode 100644 SAC.py
 create mode 100644 TD3.py
 create mode 100644 agent.py
 create mode 100644 compare.png
 create mode 100644 data/houseload.csv
 create mode 100644 data/irradiance.csv
 create mode 100644 data/prices.csv
 create mode 100644 data/results.json
 create mode 100644 data/results_day.json
 create mode 100644 data/temper.csv
 create mode 100644 data/wind.csv
 create mode 100644 data_manager.py
 create mode 100644 environment.py
 create mode 100644 excel.py
 create mode 100644 generate_household_data.py
 create mode 100644 llm.txt
 create mode 100644 llm_connect.py
 create mode 100644 llma/Reflexion.py
 create mode 100644 llma/constraint.py
 create mode 100644 llma/constraint_model.py
 create mode 100644 llma/data/adv_code.py
 create mode 100644 llma/data/adv_code_2.py
 create mode 100644 llma/data/code.py
 create mode 100644 llma/data/data.csv
 create mode 100644 llma/data/data.json
 create mode 100644 llma/data/desc.txt
 create mode 100644 llma/data/labels.json
 create mode 100644 llma/data/rag/constraint_vector.db/c614fe78-a1fb-434b-ae4e-2bc2d43eaf04/data_level0.bin
 create mode 100644 llma/data/rag/constraint_vector.db/c614fe78-a1fb-434b-ae4e-2bc2d43eaf04/header.bin
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 create mode 100644 llma/data/results_day.json
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 create mode 100644 llma/data/state_2_objective.json
 create mode 100644 llma/data/state_3_constraints.json
 create mode 100644 llma/data/state_4_constraints_modeled.json
 create mode 100644 llma/data/state_5_objective_modeled.json
 create mode 100644 llma/data/state_6_code.json
 create mode 100644 llma/execute_code.py
 create mode 100644 llma/generate_code.py
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 create mode 100644 llma/main.py
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 create mode 100644 llma/objective_model.py
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 create mode 100644 llma/rag/query_vector_db.py
 create mode 100644 llma/rag/rag_utils.py
 create mode 100644 llma/results.json
 create mode 100644 llma/target_code.py
 create mode 100644 llma/utils.py
 create mode 100644 measure.py
 create mode 100644 melt_test.py
 create mode 100644 module.py
 create mode 100644 net.py
 create mode 100644 parameters.py
 create mode 100644 plotDRL.py
 create mode 100644 plott.py
 create mode 100644 res/ddpg.txt
 create mode 100644 res/ppo.txt
 create mode 100644 res/ppo_1016.txt
 create mode 100644 res/ppo_llm.txt
 create mode 100644 res/sac.txt
 create mode 100644 res/td3.txt
 create mode 100644 tools.py

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diff --git a/AgentDDPG/DRL_10_plots/gurobi.svg b/AgentDDPG/DRL_10_plots/gurobi.svg
new file mode 100644
index 0000000..3aede27
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+      <use xlink:href="#ArialMT-6e" x="133.398438"/>
+      <use xlink:href="#ArialMT-31" x="189.013672"/>
+     </g>
+    </g>
+    <g id="patch_258">
+     <path d="M 395.609115 340.83986 
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+    </g>
+    <g id="text_45">
+     <!-- Gen2 -->
+     <g style="fill: #262626" transform="translate(429.209115 340.83986) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-65" x="77.783203"/>
+      <use xlink:href="#ArialMT-6e" x="133.398438"/>
+      <use xlink:href="#ArialMT-32" x="189.013672"/>
+     </g>
+    </g>
+    <g id="patch_259">
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+    </g>
+    <g id="text_46">
+     <!-- Gen3 -->
+     <g style="fill: #262626" transform="translate(429.209115 355.414235) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-65" x="77.783203"/>
+      <use xlink:href="#ArialMT-6e" x="133.398438"/>
+      <use xlink:href="#ArialMT-33" x="189.013672"/>
+     </g>
+    </g>
+    <g id="patch_260">
+     <path d="M 395.609115 370.13486 
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+    </g>
+    <g id="text_47">
+     <!-- ESS charge -->
+     <g style="fill: #262626" transform="translate(429.209115 370.13486) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-45" d="M 506 0 
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+       <path id="ArialMT-67" d="M 319 -275 
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+M 784 1719 
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+      </defs>
+      <use xlink:href="#ArialMT-45"/>
+      <use xlink:href="#ArialMT-53" x="66.699219"/>
+      <use xlink:href="#ArialMT-53" x="133.398438"/>
+      <use xlink:href="#ArialMT-20" x="200.097656"/>
+      <use xlink:href="#ArialMT-63" x="227.880859"/>
+      <use xlink:href="#ArialMT-68" x="277.880859"/>
+      <use xlink:href="#ArialMT-61" x="333.496094"/>
+      <use xlink:href="#ArialMT-72" x="389.111328"/>
+      <use xlink:href="#ArialMT-67" x="422.412109"/>
+      <use xlink:href="#ArialMT-65" x="478.027344"/>
+     </g>
+    </g>
+    <g id="patch_261">
+     <path d="M 395.609115 384.99611 
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+    </g>
+    <g id="text_48">
+     <!-- ESS discharge -->
+     <g style="fill: #262626" transform="translate(429.209115 384.99611) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-45"/>
+      <use xlink:href="#ArialMT-53" x="66.699219"/>
+      <use xlink:href="#ArialMT-53" x="133.398438"/>
+      <use xlink:href="#ArialMT-20" x="200.097656"/>
+      <use xlink:href="#ArialMT-64" x="227.880859"/>
+      <use xlink:href="#ArialMT-69" x="283.496094"/>
+      <use xlink:href="#ArialMT-73" x="305.712891"/>
+      <use xlink:href="#ArialMT-63" x="355.712891"/>
+      <use xlink:href="#ArialMT-68" x="405.712891"/>
+      <use xlink:href="#ArialMT-61" x="461.328125"/>
+      <use xlink:href="#ArialMT-72" x="516.943359"/>
+      <use xlink:href="#ArialMT-67" x="550.244141"/>
+      <use xlink:href="#ArialMT-65" x="605.859375"/>
+     </g>
+    </g>
+    <g id="patch_262">
+     <path d="M 395.609115 399.71111 
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+    </g>
+    <g id="text_49">
+     <!-- Pv Gen -->
+     <g style="fill: #262626" transform="translate(429.209115 399.71111) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-76" d="M 1344 0 
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+      </defs>
+      <use xlink:href="#ArialMT-50"/>
+      <use xlink:href="#ArialMT-76" x="66.699219"/>
+      <use xlink:href="#ArialMT-20" x="116.699219"/>
+      <use xlink:href="#ArialMT-47" x="144.482422"/>
+      <use xlink:href="#ArialMT-65" x="222.265625"/>
+      <use xlink:href="#ArialMT-6e" x="277.880859"/>
+     </g>
+    </g>
+    <g id="patch_263">
+     <path d="M 395.609115 414.285485 
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+    </g>
+    <g id="text_50">
+     <!-- Wind Gen -->
+     <g style="fill: #262626" transform="translate(429.209115 414.285485) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-57"/>
+      <use xlink:href="#ArialMT-69" x="94.384766"/>
+      <use xlink:href="#ArialMT-6e" x="116.601562"/>
+      <use xlink:href="#ArialMT-64" x="172.216797"/>
+      <use xlink:href="#ArialMT-20" x="227.832031"/>
+      <use xlink:href="#ArialMT-47" x="255.615234"/>
+      <use xlink:href="#ArialMT-65" x="333.398438"/>
+      <use xlink:href="#ArialMT-6e" x="389.013672"/>
+     </g>
+    </g>
+    <g id="patch_264">
+     <path d="M 395.609115 429.00611 
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+    </g>
+    <g id="text_51">
+     <!-- Grid import -->
+     <g style="fill: #262626" transform="translate(429.209115 429.00611) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-70" d="M 422 -1272 
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+      </defs>
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-72" x="77.783203"/>
+      <use xlink:href="#ArialMT-69" x="111.083984"/>
+      <use xlink:href="#ArialMT-64" x="133.300781"/>
+      <use xlink:href="#ArialMT-20" x="188.916016"/>
+      <use xlink:href="#ArialMT-69" x="216.699219"/>
+      <use xlink:href="#ArialMT-6d" x="238.916016"/>
+      <use xlink:href="#ArialMT-70" x="322.216797"/>
+      <use xlink:href="#ArialMT-6f" x="377.832031"/>
+      <use xlink:href="#ArialMT-72" x="433.447266"/>
+      <use xlink:href="#ArialMT-74" x="466.748047"/>
+     </g>
+    </g>
+    <g id="patch_265">
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+    </g>
+    <g id="text_52">
+     <!-- Grid export -->
+     <g style="fill: #262626" transform="translate(429.209115 443.726735) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-78" d="M 47 0 
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+      </defs>
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-72" x="77.783203"/>
+      <use xlink:href="#ArialMT-69" x="111.083984"/>
+      <use xlink:href="#ArialMT-64" x="133.300781"/>
+      <use xlink:href="#ArialMT-20" x="188.916016"/>
+      <use xlink:href="#ArialMT-65" x="216.699219"/>
+      <use xlink:href="#ArialMT-78" x="272.314453"/>
+      <use xlink:href="#ArialMT-70" x="322.314453"/>
+      <use xlink:href="#ArialMT-6f" x="377.929688"/>
+      <use xlink:href="#ArialMT-72" x="433.544922"/>
+      <use xlink:href="#ArialMT-74" x="466.845703"/>
+     </g>
+    </g>
+   </g>
+  </g>
+  <g id="axes_4">
+   <g id="patch_266">
+    <path d="M 618.58349 509.240865 
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+   </g>
+   <g id="matplotlib.axis_7">
+    <g id="xtick_16">
+     <g id="line2d_45">
+      <path d="M 636.139894 509.240865 
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+     </g>
+     <g id="text_53">
+      <!-- 0 -->
+      <g style="fill: #262626" transform="translate(633.081378 526.614459) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_17">
+     <g id="line2d_46">
+      <path d="M 699.292429 509.240865 
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+     </g>
+     <g id="text_54">
+      <!-- 5 -->
+      <g style="fill: #262626" transform="translate(696.233913 526.614459) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-35"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_18">
+     <g id="line2d_47">
+      <path d="M 762.444964 509.240865 
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+     </g>
+     <g id="text_55">
+      <!-- 10 -->
+      <g style="fill: #262626" transform="translate(756.327932 526.614459) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_19">
+     <g id="line2d_48">
+      <path d="M 825.597498 509.240865 
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+     </g>
+     <g id="text_56">
+      <!-- 15 -->
+      <g style="fill: #262626" transform="translate(819.480467 526.614459) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_20">
+     <g id="line2d_49">
+      <path d="M 888.750033 509.240865 
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+     </g>
+     <g id="text_57">
+      <!-- 20 -->
+      <g style="fill: #262626" transform="translate(882.633002 526.614459) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_58">
+     <!-- Time [h] -->
+     <g style="fill: #262626" transform="translate(762.472448 541.390084) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-54"/>
+      <use xlink:href="#ArialMT-69" x="57.333984"/>
+      <use xlink:href="#ArialMT-6d" x="79.550781"/>
+      <use xlink:href="#ArialMT-65" x="162.851562"/>
+      <use xlink:href="#ArialMT-20" x="218.466797"/>
+      <use xlink:href="#ArialMT-5b" x="246.25"/>
+      <use xlink:href="#ArialMT-68" x="274.033203"/>
+      <use xlink:href="#ArialMT-5d" x="329.648438"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_8">
+    <g id="ytick_20">
+     <g id="line2d_50">
+      <path d="M 618.58349 492.291954 
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+     </g>
+     <g id="text_59">
+      <!-- 0 -->
+      <g style="fill: #262626" transform="translate(602.966458 496.228751) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_21">
+     <g id="line2d_51">
+      <path d="M 618.58349 446.424971 
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+     </g>
+     <g id="text_60">
+      <!-- 100 -->
+      <g style="fill: #262626" transform="translate(590.732396 450.361768) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_22">
+     <g id="line2d_52">
+      <path d="M 618.58349 400.557987 
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+     </g>
+     <g id="text_61">
+      <!-- 200 -->
+      <g style="fill: #262626" transform="translate(590.732396 404.494784) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_23">
+     <g id="line2d_53">
+      <path d="M 618.58349 354.691004 
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+     </g>
+     <g id="text_62">
+      <!-- 300 -->
+      <g style="fill: #262626" transform="translate(590.732396 358.627801) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-33"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_24">
+     <g id="line2d_54">
+      <path d="M 618.58349 308.82402 
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+     </g>
+     <g id="text_63">
+      <!-- 400 -->
+      <g style="fill: #262626" transform="translate(590.732396 312.760817) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-34"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_64">
+     <!-- Power [kWh] -->
+     <g style="fill: #262626" transform="translate(584.347396 434.778175) rotate(-90) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-50"/>
+      <use xlink:href="#ArialMT-6f" x="66.699219"/>
+      <use xlink:href="#ArialMT-77" x="122.314453"/>
+      <use xlink:href="#ArialMT-65" x="194.53125"/>
+      <use xlink:href="#ArialMT-72" x="250.146484"/>
+      <use xlink:href="#ArialMT-20" x="283.447266"/>
+      <use xlink:href="#ArialMT-5b" x="311.230469"/>
+      <use xlink:href="#ArialMT-6b" x="339.013672"/>
+      <use xlink:href="#ArialMT-57" x="389.013672"/>
+      <use xlink:href="#ArialMT-68" x="483.398438"/>
+      <use xlink:href="#ArialMT-5d" x="539.013672"/>
+     </g>
+    </g>
+   </g>
+   <g id="patch_267">
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+      <use xlink:href="#ArialMT-5d" x="329.648438"/>
+     </g>
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+   </g>
+   <g id="matplotlib.axis_4">
+    <g id="ytick_5">
+     <g id="line2d_15">
+      <path d="M 618.58349 209.32305 
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+     <g id="text_18">
+      <!-- 17.5 -->
+      <g style="fill: #262626" transform="translate(587.676458 213.259847) scale(0.11 -0.11)">
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+       <use xlink:href="#ArialMT-37" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-35" x="139.013672"/>
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+    </g>
+    <g id="ytick_6">
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+     <g id="text_19">
+      <!-- 20.0 -->
+      <g style="fill: #262626" transform="translate(587.676458 185.730898) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-30" x="139.013672"/>
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+    <g id="ytick_7">
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+     <g id="text_20">
+      <!-- 22.5 -->
+      <g style="fill: #262626" transform="translate(587.676458 158.201949) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-32" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-35" x="139.013672"/>
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+    <g id="ytick_8">
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+     <g id="text_21">
+      <!-- 25.0 -->
+      <g style="fill: #262626" transform="translate(587.676458 130.673) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-30" x="139.013672"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_9">
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+     <g id="text_22">
+      <!-- 27.5 -->
+      <g style="fill: #262626" transform="translate(587.676458 103.144051) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-37" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-35" x="139.013672"/>
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+    <g id="ytick_10">
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+     <g id="text_23">
+      <!-- 30.0 -->
+      <g style="fill: #262626" transform="translate(587.676458 75.615101) scale(0.11 -0.11)">
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+       <use xlink:href="#ArialMT-33"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-30" x="139.013672"/>
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+      <!-- 32.5 -->
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+       <use xlink:href="#ArialMT-32" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
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+     <g id="text_25">
+      <!-- 35.0 -->
+      <g style="fill: #262626" transform="translate(587.676458 20.557203) scale(0.11 -0.11)">
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+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-30" x="139.013672"/>
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+    <g id="text_26">
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+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
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+       <use xlink:href="#ArialMT-32" x="55.615234"/>
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+      <!-- 35.0 -->
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+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-2e" x="111.230469"/>
+       <use xlink:href="#ArialMT-30" x="139.013672"/>
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+    </g>
+    <g id="text_52">
+     <!-- ESS discharge -->
+     <g style="fill: #262626" transform="translate(428.901458 381.915245) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-45"/>
+      <use xlink:href="#ArialMT-53" x="66.699219"/>
+      <use xlink:href="#ArialMT-53" x="133.398438"/>
+      <use xlink:href="#ArialMT-20" x="200.097656"/>
+      <use xlink:href="#ArialMT-64" x="227.880859"/>
+      <use xlink:href="#ArialMT-69" x="283.496094"/>
+      <use xlink:href="#ArialMT-73" x="305.712891"/>
+      <use xlink:href="#ArialMT-63" x="355.712891"/>
+      <use xlink:href="#ArialMT-68" x="405.712891"/>
+      <use xlink:href="#ArialMT-61" x="461.328125"/>
+      <use xlink:href="#ArialMT-72" x="516.943359"/>
+      <use xlink:href="#ArialMT-67" x="550.244141"/>
+      <use xlink:href="#ArialMT-65" x="605.859375"/>
+     </g>
+    </g>
+    <g id="patch_262">
+     <path d="M 395.301458 396.630245 
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+L 395.301458 388.230245 
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+" style="fill: #8172b3; stroke: #ffffff; stroke-linejoin: miter"/>
+    </g>
+    <g id="text_53">
+     <!-- Pv Gen -->
+     <g style="fill: #262626" transform="translate(428.901458 396.630245) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-76" d="M 1344 0 
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+L 1388 1331 
+Q 1503 1009 1600 663 
+Q 1675 925 1809 1294 
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+L 3125 3319 
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+      </defs>
+      <use xlink:href="#ArialMT-50"/>
+      <use xlink:href="#ArialMT-76" x="66.699219"/>
+      <use xlink:href="#ArialMT-20" x="116.699219"/>
+      <use xlink:href="#ArialMT-47" x="144.482422"/>
+      <use xlink:href="#ArialMT-65" x="222.265625"/>
+      <use xlink:href="#ArialMT-6e" x="277.880859"/>
+     </g>
+    </g>
+    <g id="patch_263">
+     <path d="M 395.301458 411.20462 
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+    </g>
+    <g id="text_54">
+     <!-- Wind Gen -->
+     <g style="fill: #262626" transform="translate(428.901458 411.20462) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-57"/>
+      <use xlink:href="#ArialMT-69" x="94.384766"/>
+      <use xlink:href="#ArialMT-6e" x="116.601562"/>
+      <use xlink:href="#ArialMT-64" x="172.216797"/>
+      <use xlink:href="#ArialMT-20" x="227.832031"/>
+      <use xlink:href="#ArialMT-47" x="255.615234"/>
+      <use xlink:href="#ArialMT-65" x="333.398438"/>
+      <use xlink:href="#ArialMT-6e" x="389.013672"/>
+     </g>
+    </g>
+    <g id="patch_264">
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+    </g>
+    <g id="text_55">
+     <!-- Grid import -->
+     <g style="fill: #262626" transform="translate(428.901458 425.925245) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-70" d="M 422 -1272 
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+Q 1572 3394 1897 3394 
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+      </defs>
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-72" x="77.783203"/>
+      <use xlink:href="#ArialMT-69" x="111.083984"/>
+      <use xlink:href="#ArialMT-64" x="133.300781"/>
+      <use xlink:href="#ArialMT-20" x="188.916016"/>
+      <use xlink:href="#ArialMT-69" x="216.699219"/>
+      <use xlink:href="#ArialMT-6d" x="238.916016"/>
+      <use xlink:href="#ArialMT-70" x="322.216797"/>
+      <use xlink:href="#ArialMT-6f" x="377.832031"/>
+      <use xlink:href="#ArialMT-72" x="433.447266"/>
+      <use xlink:href="#ArialMT-74" x="466.748047"/>
+     </g>
+    </g>
+    <g id="patch_265">
+     <path d="M 395.301458 440.64587 
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+    </g>
+    <g id="text_56">
+     <!-- Grid export -->
+     <g style="fill: #262626" transform="translate(428.901458 440.64587) scale(0.12 -0.12)">
+      <defs>
+       <path id="ArialMT-78" d="M 47 0 
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+Q 1719 2375 1834 2534 
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+      </defs>
+      <use xlink:href="#ArialMT-47"/>
+      <use xlink:href="#ArialMT-72" x="77.783203"/>
+      <use xlink:href="#ArialMT-69" x="111.083984"/>
+      <use xlink:href="#ArialMT-64" x="133.300781"/>
+      <use xlink:href="#ArialMT-20" x="188.916016"/>
+      <use xlink:href="#ArialMT-65" x="216.699219"/>
+      <use xlink:href="#ArialMT-78" x="272.314453"/>
+      <use xlink:href="#ArialMT-70" x="322.314453"/>
+      <use xlink:href="#ArialMT-6f" x="377.929688"/>
+      <use xlink:href="#ArialMT-72" x="433.544922"/>
+      <use xlink:href="#ArialMT-74" x="466.845703"/>
+     </g>
+    </g>
+   </g>
+  </g>
+  <g id="axes_4">
+   <g id="patch_266">
+    <path d="M 618.275833 506.16 
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+   </g>
+   <g id="matplotlib.axis_7">
+    <g id="xtick_16">
+     <g id="line2d_52">
+      <path d="M 635.832238 506.16 
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+     </g>
+     <g id="text_57">
+      <!-- 0 -->
+      <g style="fill: #262626" transform="translate(632.773722 523.533594) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_17">
+     <g id="line2d_53">
+      <path d="M 698.984773 506.16 
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+     </g>
+     <g id="text_58">
+      <!-- 5 -->
+      <g style="fill: #262626" transform="translate(695.926257 523.533594) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-35"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_18">
+     <g id="line2d_54">
+      <path d="M 762.137307 506.16 
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+" clip-path="url(#pef2b700f58)" style="fill: none; stroke: #cccccc; stroke-linecap: round"/>
+     </g>
+     <g id="text_59">
+      <!-- 10 -->
+      <g style="fill: #262626" transform="translate(756.020276 523.533594) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_19">
+     <g id="line2d_55">
+      <path d="M 825.289842 506.16 
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+     </g>
+     <g id="text_60">
+      <!-- 15 -->
+      <g style="fill: #262626" transform="translate(819.172811 523.533594) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="xtick_20">
+     <g id="line2d_56">
+      <path d="M 888.442377 506.16 
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+     </g>
+     <g id="text_61">
+      <!-- 20 -->
+      <g style="fill: #262626" transform="translate(882.325346 523.533594) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_62">
+     <!-- Time [h] -->
+     <g style="fill: #262626" transform="translate(762.164792 538.309219) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-54"/>
+      <use xlink:href="#ArialMT-69" x="57.333984"/>
+      <use xlink:href="#ArialMT-6d" x="79.550781"/>
+      <use xlink:href="#ArialMT-65" x="162.851562"/>
+      <use xlink:href="#ArialMT-20" x="218.466797"/>
+      <use xlink:href="#ArialMT-5b" x="246.25"/>
+      <use xlink:href="#ArialMT-68" x="274.033203"/>
+      <use xlink:href="#ArialMT-5d" x="329.648438"/>
+     </g>
+    </g>
+   </g>
+   <g id="matplotlib.axis_8">
+    <g id="ytick_24">
+     <g id="line2d_57">
+      <path d="M 618.275833 502.595136 
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+     </g>
+     <g id="text_63">
+      <!-- −50 -->
+      <g style="fill: #262626" transform="translate(590.117083 506.531933) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-2212"/>
+       <use xlink:href="#ArialMT-35" x="58.398438"/>
+       <use xlink:href="#ArialMT-30" x="114.013672"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_25">
+     <g id="line2d_58">
+      <path d="M 618.275833 477.384285 
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+     </g>
+     <g id="text_64">
+      <!-- 0 -->
+      <g style="fill: #262626" transform="translate(602.658802 481.321082) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-30"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_26">
+     <g id="line2d_59">
+      <path d="M 618.275833 452.173434 
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+     </g>
+     <g id="text_65">
+      <!-- 50 -->
+      <g style="fill: #262626" transform="translate(596.541771 456.11023) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-35"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_27">
+     <g id="line2d_60">
+      <path d="M 618.275833 426.962582 
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+     </g>
+     <g id="text_66">
+      <!-- 100 -->
+      <g style="fill: #262626" transform="translate(590.42474 430.899379) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_28">
+     <g id="line2d_61">
+      <path d="M 618.275833 401.751731 
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+     </g>
+     <g id="text_67">
+      <!-- 150 -->
+      <g style="fill: #262626" transform="translate(590.42474 405.688528) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_29">
+     <g id="line2d_62">
+      <path d="M 618.275833 376.54088 
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+     </g>
+     <g id="text_68">
+      <!-- 200 -->
+      <g style="fill: #262626" transform="translate(590.42474 380.477676) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_30">
+     <g id="line2d_63">
+      <path d="M 618.275833 351.330028 
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+     </g>
+     <g id="text_69">
+      <!-- 250 -->
+      <g style="fill: #262626" transform="translate(590.42474 355.266825) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-32"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_31">
+     <g id="line2d_64">
+      <path d="M 618.275833 326.119177 
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+     </g>
+     <g id="text_70">
+      <!-- 300 -->
+      <g style="fill: #262626" transform="translate(590.42474 330.055974) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-33"/>
+       <use xlink:href="#ArialMT-30" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="ytick_32">
+     <g id="line2d_65">
+      <path d="M 618.275833 300.908326 
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+     </g>
+     <g id="text_71">
+      <!-- 350 -->
+      <g style="fill: #262626" transform="translate(590.42474 304.845123) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-33"/>
+       <use xlink:href="#ArialMT-35" x="55.615234"/>
+       <use xlink:href="#ArialMT-30" x="111.230469"/>
+      </g>
+     </g>
+    </g>
+    <g id="text_72">
+     <!-- Power [kWh] -->
+     <g style="fill: #262626" transform="translate(583.732083 431.69731) rotate(-90) scale(0.12 -0.12)">
+      <use xlink:href="#ArialMT-50"/>
+      <use xlink:href="#ArialMT-6f" x="66.699219"/>
+      <use xlink:href="#ArialMT-77" x="122.314453"/>
+      <use xlink:href="#ArialMT-65" x="194.53125"/>
+      <use xlink:href="#ArialMT-72" x="250.146484"/>
+      <use xlink:href="#ArialMT-20" x="283.447266"/>
+      <use xlink:href="#ArialMT-5b" x="311.230469"/>
+      <use xlink:href="#ArialMT-6b" x="339.013672"/>
+      <use xlink:href="#ArialMT-57" x="389.013672"/>
+      <use xlink:href="#ArialMT-68" x="483.398438"/>
+      <use xlink:href="#ArialMT-5d" x="539.013672"/>
+     </g>
+    </g>
+   </g>
+   <g id="patch_267">
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diff --git a/AgentPPO/actor_ram.pth b/AgentPPO/actor_ram.pth
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diff --git a/AgentPPO/loss_ram.pkl b/AgentPPO/loss_ram.pkl
new file mode 100644
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diff --git a/AgentPPO/melt_ppo.pkl b/AgentPPO/melt_ppo.pkl
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diff --git a/AgentPPO/melt_ram.pkl b/AgentPPO/melt_ram.pkl
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diff --git a/AgentPPO/reward_ram.pkl b/AgentPPO/reward_ram.pkl
new file mode 100644
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diff --git a/DDPG.py b/DDPG.py
new file mode 100644
index 0000000..bb41d30
--- /dev/null
+++ b/DDPG.py
@@ -0,0 +1,127 @@
+import pickle
+import pandas as pd
+import torch
+
+from agent import AgentDDPG
+from environment import ESSEnv
+from tools import *
+
+
+def update_buffer(_trajectory):
+    ten_state = torch.as_tensor([item[0] for item in _trajectory], dtype=torch.float32)
+    ary_other = torch.as_tensor([item[1] for item in _trajectory])
+    ary_other[:, 0] = ary_other[:, 0]  # ten_reward
+    ary_other[:, 1] = (1.0 - ary_other[:, 1]) * gamma  # ten_mask = (1.0 - ary_done) * gamma
+
+    buffer.extend_buffer(ten_state, ary_other)
+
+    _steps = ten_state.shape[0]
+    _r_exp = ary_other[:, 0].mean()  # other = (reward, mask, action)
+    return _steps, _r_exp
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    '''record real unbalance'''
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentDDPG()
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.agent.cri_target = True
+        args.env = ESSEnv()
+        args.init_before_training(if_main=True)
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_per_or_gae)
+        '''init replay buffer'''
+        buffer = ReplayBuffer(max_len=args.max_memo, state_dim=env.state_space.shape[0],
+                              action_dim=env.action_space.shape[0])
+        '''start training'''
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size
+        target_step = args.target_step  # how manysteps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        num_episode = args.num_episode
+        agent.state = env.reset()
+        '''collect data, train and update network'''
+        # args.train = False
+        # args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        if args.train:
+            collect_data = True
+            while collect_data:
+                print(f'buffer:{buffer.now_len}')
+                with torch.no_grad():
+                    trajectory = agent.explore_env(env, target_step)
+                    steps, r_exp = update_buffer(trajectory)
+                    buffer.update_now_len()
+                if buffer.now_len >= 10000:
+                    collect_data = False
+            for i_episode in range(num_episode):
+                critic_loss, actor_loss = agent.update_net(buffer, batch_size, repeat_times, soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}, buffer_length: {buffer.now_len}')
+                if i_episode % 10 == 0:  # target_step
+                    with torch.no_grad():
+                        trajectory = agent.explore_env(env, target_step)
+                        steps, r_exp = update_buffer(trajectory)
+
+    loss_record_path = f'{args.cwd}/loss_data.pkl'
+    reward_record_path = f'{args.cwd}/reward_data.pkl'
+    act_save_path = f'{args.cwd}/actor.pth'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        record = test_one_episode(env, agent.act, agent.device)
+        eval_data = pd.DataFrame(record['system_info'])
+        eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery',
+                             'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_data.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+    if args.plot_on:
+        from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = '10'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        plot_optimization_result(base_result, plot_dir)
+        plot_evaluation_information(args.cwd + '/' + 'test_data.pkl', plot_dir)
+    '''compare the different cost get from gurobi and DDPG'''
+    ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
+    print('rl_cost:', sum(eval_data['operation_cost']))
+    print('gurobi_cost:', sum(base_result['step_cost']))
+    print('ration:', ration)
diff --git a/PPO.py b/PPO.py
new file mode 100644
index 0000000..682e78f
--- /dev/null
+++ b/PPO.py
@@ -0,0 +1,374 @@
+import os
+import pickle
+from copy import deepcopy
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from environment import ESSEnv
+from tools import get_episode_return, test_one_episode, optimization_base_result
+
+
+class ActorPPO(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, action_dim))
+        # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
+        self.a_logstd = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
+        self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))
+
+    def forward(self, state):
+        return self.net(state).tanh()  # action.tanh() limit the data output of action
+
+    def get_action(self, state):
+        a_avg = self.forward(state)  # too big for the action
+        a_std = self.a_logstd.exp()
+
+        noise = torch.randn_like(a_avg)
+        action = a_avg + noise * a_std
+        return action, noise
+
+    def get_logprob_entropy(self, state, action):
+        a_avg = self.forward(state)
+        a_std = self.a_logstd.exp()
+
+        delta = ((a_avg - action) / a_std).pow(2) * 0.5
+        logprob = -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # new_logprob
+
+        dist_entropy = (logprob.exp() * logprob).mean()  # policy entropy
+        return logprob, dist_entropy
+
+    def get_old_logprob(self, _action, noise):  # noise = action - a_noise
+        delta = noise.pow(2) * 0.5
+        return -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # old_logprob
+
+
+class CriticAdv(nn.Module):
+    def __init__(self, mid_dim, state_dim, _action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, 1))
+
+    def forward(self, state):
+        return self.net(state)  # Advantage value
+
+
+def smooth_rewards(rewards, window=10):
+    rewards = rewards.unsqueeze(0).unsqueeze(0)  # 将 rewards 转为 [1, 1, len] 的形状以适应 conv1d
+    kernel = torch.ones(1, 1, window, device=rewards.device) / window  # 创建一个均匀的滑动平均核
+    smoothed_rewards = F.conv1d(rewards, kernel, padding='valid')  # 滑动平均
+    smoothed_rewards = smoothed_rewards.squeeze(0).squeeze(0)  # 去掉多余的维度
+
+    # 保持与原始奖励序列相同的长度，将前 window-1 个奖励保持不变
+    return torch.cat((rewards[0, 0, :window-1], smoothed_rewards))
+
+
+class AgentPPO:
+    def __init__(self):
+        super().__init__()
+        self.state = None
+        self.device = None
+        self.action_dim = None
+        self.get_obj_critic = None
+
+        self.criterion = torch.nn.SmoothL1Loss()
+        self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None
+        self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
+
+        '''init modify'''
+        self.ClassCri = CriticAdv
+        self.ClassAct = ActorPPO
+
+        self.ratio_clip = 0.2  # ratio.clamp(1 - clip, 1 + clip)
+        self.lambda_entropy = 0.02  # could be 0.01~0.05
+        self.lambda_gae_adv = 0.98  # could be 0.95~0.99, (Generalized Advantage Estimation. ICLR.2016.)
+        self.get_reward_sum = None  # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+        self.trajectory_list = None
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=True, gpu_id=0):
+        self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
+        self.trajectory_list = list()
+        # choose whether to use gae or not
+        self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+
+        self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
+        self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
+        self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
+        self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
+
+        self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)
+        self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri
+
+    def select_action(self, state):
+        states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        actions, noises = self.act.get_action(states)
+        return actions[0].detach().cpu().numpy(), noises[0].detach().cpu().numpy()
+
+    def explore_env(self, env, target_step):
+        state = self.state  # sent state to agent and then agent sent state to method
+        trajectory_temp = list()
+        last_done = 0
+        for i in range(target_step):
+            action, noise = self.select_action(state)
+            state, next_state, reward, done, = env.step(np.tanh(action))
+            trajectory_temp.append((state, reward, done, action, noise))
+            if done:
+                state = env.reset()
+                last_done = i
+            else:
+                state = next_state
+        self.state = state
+
+        '''splice list'''
+        # store 0 trajectory information to list
+        trajectory_list = self.trajectory_list + trajectory_temp[:last_done + 1]
+        self.trajectory_list = trajectory_temp[last_done:]
+        return trajectory_list
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
+        """put data extract and update network together"""
+        with torch.no_grad():
+            buf_len = buffer[0].shape[0]
+            # decompose buffer data
+            buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
+
+            '''get buf_r_sum, buf_logprob'''
+            bs = 4096
+            buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
+            buf_value = torch.cat(buf_value, dim=0)
+            buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)
+
+            buf_r_sum, buf_advantage = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value)  # detach()
+            # normalize advantage
+            buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
+            buf_advantage = smooth_rewards(buf_advantage, window=10)
+            del buf_noise, buffer[:]
+
+        '''PPO: Surrogate objective of Trust Region'''
+        obj_critic = obj_actor = None
+        for _ in range(int(buf_len / batch_size * repeat_times)):
+            indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
+
+            state = buf_state[indices]
+            action = buf_action[indices]
+            r_sum = buf_r_sum[indices]
+            logprob = buf_logprob[indices]
+            advantage = buf_advantage[indices]
+
+            new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action)  # it is obj_actor
+            ratio = (new_logprob - logprob.detach()).exp()
+            surrogate1 = advantage * ratio
+            surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
+            obj_surrogate = -torch.min(surrogate1, surrogate2).mean()
+            obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
+            self.optim_update(self.act_optim, obj_actor)  # update actor
+
+            value = self.cri(state).squeeze(1)  # critic network predicts the reward_sum (Q value) of state
+            # use smoothloss L1 to evaluate the value loss
+            # obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)
+            obj_critic = self.criterion(value, r_sum)
+            self.optim_update(self.cri_optim, obj_critic)  # calculate and update the back propogation of value loss
+            # choose whether to use soft update
+            self.soft_update(self.cri_target, self.cri, soft_update_tau) if self.cri_target is not self.cri else None
+
+        a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1))
+        return obj_critic.item(), obj_actor.item(), a_std_log.mean().item()  # logging_tuple
+
+    def get_reward_sum_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # reward sum
+
+        pre_r_sum = 0
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+        buf_advantage = buf_r_sum - (buf_mask * buf_value[:, 0])
+        return buf_r_sum, buf_advantage
+
+    def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # old policy value
+        buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # advantage value
+
+        pre_r_sum = 0.0
+        pre_advantage = 0.0  # advantage value of previous step
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])
+            pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
+        return buf_r_sum, buf_advantage
+
+    @staticmethod
+    def optim_update(optimizer, objective):
+        optimizer.zero_grad()
+        objective.backward()
+        optimizer.step()
+
+    @staticmethod
+    def soft_update(target_net, current_net, tau):
+        for tar, cur in zip(target_net.parameters(), current_net.parameters()):
+            tar.data.copy_(cur.data.__mul__(tau) + tar.data.__mul__(1.0 - tau))
+
+
+class Arguments:
+    def __init__(self, agent=None, env=None):
+        self.agent = agent  # Deep Reinforcement Learning algorithm
+        self.env = env  # the environment for training
+        self.cwd = None  # current work directory. None means set automatically
+        self.if_remove = False  # remove the cwd folder? (True, False, None:ask me)
+        self.visible_gpu = '0'  # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
+        self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
+
+        '''Arguments for training'''
+        self.num_episode = 1000  # to control the train episodes for PPO
+        self.gamma = 0.995  # discount factor of future rewards
+        self.learning_rate = 1e-4  # 1e-4 2 ** -14 2e-4
+        self.soft_update_tau = 2 ** -8  # 1e-3 2 ** -8
+
+        self.net_dim = 256  # the network width
+        self.batch_size = 4096  # num of transitions sampled from replay buffer.
+        self.repeat_times = 2 ** 5  # collect target_step, then update network
+        self.target_step = 4096  # repeatedly update network to keep critic's loss small
+        self.max_memo = self.target_step  # capacity of replay buffer
+        self.if_gae_or_raw = True  # GAE for on-policy sparse reward: Generalized Advantage Estimation.
+
+        '''Arguments for evaluate'''
+        self.random_seed = 1234
+        # self.random_seed_list = [1234, 2234, 3234, 4234, 5234]
+        self.random_seed_list = [1567]
+        self.train = True
+        self.save_network = True
+        self.test_network = True
+        self.save_test_data = True
+        self.compare_with_gurobi = True
+        self.plot_on = True
+
+    def init_before_training(self):
+        if self.cwd is None:
+            agent_name = self.agent.__class__.__name__
+            self.cwd = f'./{agent_name}'
+
+        np.random.seed(self.random_seed)
+        torch.manual_seed(self.random_seed)
+        torch.set_num_threads(self.num_threads)
+        torch.set_default_dtype(torch.float32)
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
+
+
+def update_buffer(_trajectory):
+    _trajectory = list(map(list, zip(*_trajectory)))  # 2D-list transpose, here cut the trajectory into 5 parts 
+    ten_state = torch.as_tensor(_trajectory[0])  # tensor state here
+    ten_reward = torch.as_tensor(_trajectory[1], dtype=torch.float32)
+    # _trajectory[2] = done, 将 done 替换为掩码，节省内存
+    ten_mask = (1.0 - torch.as_tensor(_trajectory[2], dtype=torch.float32)) * gamma
+    ten_action = torch.as_tensor(_trajectory[3])
+    ten_noise = torch.as_tensor(_trajectory[4], dtype=torch.float32)
+
+    buffer[:] = (ten_state, ten_action, ten_noise, ten_reward, ten_mask)  # list store tensors
+
+    _steps = ten_reward.shape[0]  # how many steps are collected in all trajectories
+    _r_exp = ten_reward.mean()  # the mean reward
+    return _steps, _r_exp
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentPPO()
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.agent.cri_target = True
+        args.env = ESSEnv()
+        args.init_before_training()
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_gae_or_raw)
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size  # how much data should be used to update net
+        target_step = args.target_step  # how manysteps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        num_episode = args.num_episode
+        agent.state = env.reset()
+        '''init buffer'''
+        buffer = list()
+        '''init training parameters'''
+        args.train = False
+        args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        # args.plot_on = False
+        if args.train:
+            for i_episode in range(num_episode):
+                with torch.no_grad():
+                    trajectory_list = agent.explore_env(env, target_step)
+                    steps, r_exp = update_buffer(trajectory_list)
+                critic_loss, actor_loss, entropy_loss = agent.update_net(buffer, batch_size, repeat_times,
+                                                                         soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                loss_record['entropy_loss'].append(entropy_loss)
+
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}')
+
+    act_save_path = f'{args.cwd}/actor_10.pth'
+    loss_record_path = f'{args.cwd}/loss_10.pkl'
+    reward_record_path = f'{args.cwd}/reward_10.pkl'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        record = test_one_episode(env, agent.act, agent.device)
+        eval_data = pd.DataFrame(record['system_info'])
+        eval_data.columns = ['time_step', 'price', 'load', 'action', 'real_action', 'soc', 'battery',
+                             'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_10.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+    if args.plot_on:
+        from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = '10'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        plot_optimization_result(base_result, plot_dir)
+        plot_evaluation_information(args.cwd + '/' + 'test_10.pkl', plot_dir)
+    '''compare the different cost get from gurobi and PPO'''
+    ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
+    print('rl_cost:', sum(eval_data['operation_cost']))
+    print('gurobi_cost:', sum(base_result['step_cost']))
+    print('ration:', ration)
diff --git a/PPO_llm.py b/PPO_llm.py
new file mode 100644
index 0000000..cf1d875
--- /dev/null
+++ b/PPO_llm.py
@@ -0,0 +1,472 @@
+import json
+import os
+import pickle
+from copy import deepcopy
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+from data_manager import *
+from environment import ESSEnv
+from tools import optimization_base_result
+
+
+def load_llm_actions(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+    return data
+
+
+class ActorPPO(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, action_dim))
+        # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
+        self.a_logstd = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
+        self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))
+
+    def forward(self, state):
+        return self.net(state).tanh()  # action.tanh() limit the data output of action
+
+    def get_action(self, state):
+        a_avg = self.forward(state)  # too big for the action
+        a_std = self.a_logstd.exp()
+
+        noise = torch.randn_like(a_avg)
+        action = a_avg + noise * a_std
+        return action, noise
+
+    def get_logprob_entropy(self, state, action):
+        a_avg = self.forward(state)
+        a_std = self.a_logstd.exp()
+
+        delta = ((a_avg - action) / a_std).pow(2) * 0.5
+        logprob = -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # new_logprob
+
+        dist_entropy = (logprob.exp() * logprob).mean()  # policy entropy
+        return logprob, dist_entropy
+
+    def get_old_logprob(self, _action, noise):  # noise = action - a_noise
+        delta = noise.pow(2) * 0.5
+        return -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # old_logprob
+
+
+class CriticAdv(nn.Module):
+    def __init__(self, mid_dim, state_dim, _action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, 1)
+                                 )
+
+    def forward(self, state):
+        return self.net(state)  # Advantage value
+
+
+class AgentPPO:
+    def __init__(self):
+        super().__init__()
+        self.state = None
+        self.device = None
+        self.action_dim = None
+        self.get_obj_critic = None
+        self.current_index = 0
+
+        self.criterion = torch.nn.SmoothL1Loss()
+        self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None
+        self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
+
+        self.ClassCri = CriticAdv
+        self.ClassAct = ActorPPO
+
+        self.ratio_clip = 0.2  # ratio.clamp(1 - clip, 1 + clip)
+        self.lambda_entropy = 0.02  # could be 0.01~0.05
+        self.lambda_gae_adv = 0.98  # could be 0.95~0.99, GAE (Generalized Advantage Estimation. ICLR.2016.)
+        self.get_reward_sum = None  # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+        self.trajectory_list = None
+        self.llm_actions = load_llm_actions('data/results.json')
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0):
+        self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
+        self.trajectory_list = list()
+        # choose whether to use gae or not
+        self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+
+        self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
+        self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
+        self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
+        self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
+
+        self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)
+        self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri
+
+    def select_action(self, state):
+        states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        actions, noises = self.act.get_action(states)
+        return actions[0].detach().cpu().numpy(), noises[0].detach().cpu().numpy()
+
+    def explore_env(self, env, target_step, index):
+        state = self.state  # sent state to agent and then agent sent state to method
+        trajectory_temp = list()
+        last_done = 0
+        for i in range(target_step):
+            action, noise = self.select_action(state)
+            llm_action = self.llm_actions[index]
+            action = [0.95 * action[i] + 0.05 * llm_action[i] for i in range(5)]
+            state, next_state, reward, done, = env.step(np.tanh(action))
+            trajectory_temp.append((state, reward, done, action, noise))
+            if done:
+                state = env.reset()
+                last_done = i
+            else:
+                state = next_state
+        self.state = state
+
+        '''splice list'''
+        # store 0 trajectory information to list
+        trajectory_list = self.trajectory_list + trajectory_temp[:last_done + 1]
+        self.trajectory_list = trajectory_temp[last_done:]
+        return trajectory_list
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
+        """put data extract and update network together"""
+        with torch.no_grad():
+            buf_len = buffer[0].shape[0]
+            # decompose buffer data
+            buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
+
+            '''get buf_r_sum, buf_logprob'''
+            bs = 4096
+            buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
+            buf_value = torch.cat(buf_value, dim=0)
+            buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)
+
+            buf_r_sum, buf_advantage = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value)  # detach()
+            # normalize advantage
+            buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
+            del buf_noise, buffer[:]
+
+        '''PPO: Surrogate objective of Trust Region'''
+        obj_critic = obj_actor = None
+        for _ in range(int(buf_len / batch_size * repeat_times)):
+            indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
+
+            state = buf_state[indices]
+            action = buf_action[indices]
+            r_sum = buf_r_sum[indices]
+            logprob = buf_logprob[indices]
+            advantage = buf_advantage[indices]
+
+            new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action)  # it's obj_actor
+            ratio = (new_logprob - logprob.detach()).exp()
+            surrogate1 = advantage * ratio
+            surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
+            obj_surrogate = -torch.min(surrogate1, surrogate2).mean()
+            obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
+            self.optim_update(self.act_optim, obj_actor)  # update actor
+
+            value = self.cri(state).squeeze(1)  # critic network predicts the reward_sum (Q value) of state
+            # use smoothloss L1 to evaluate the value loss
+            # obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)
+            obj_critic = self.criterion(value, r_sum)
+            self.optim_update(self.cri_optim, obj_critic)  # calculate and update the back propogation of value loss
+            # choose whether to use soft update
+            self.soft_update(self.cri_target, self.cri, soft_update_tau) if self.cri_target is not self.cri else None
+
+        a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1))
+        return obj_critic.item(), obj_actor.item(), a_std_log.mean().item()  # logging_tuple
+
+    def get_reward_sum_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # reward sum
+
+        pre_r_sum = 0
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+        buf_advantage = buf_r_sum - (buf_mask * buf_value[:, 0])
+        return buf_r_sum, buf_advantage
+
+    def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # old policy value
+        buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # advantage value
+
+        pre_r_sum = 0.0
+        pre_advantage = 0.0  # advantage value of previous step
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])  # fix a bug here
+            pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
+        return buf_r_sum, buf_advantage
+
+    @staticmethod
+    def optim_update(optimizer, objective):
+        optimizer.zero_grad()
+        objective.backward()
+        optimizer.step()
+
+    @staticmethod
+    def soft_update(target_net, current_net, tau):
+        for tar, cur in zip(target_net.parameters(), current_net.parameters()):
+            tar.data.copy_(cur.data.__mul__(tau) + tar.data.__mul__(1.0 - tau))
+
+
+class Arguments:
+    def __init__(self, agent=None, env=None):
+        self.agent = agent  # Deep Reinforcement Learning algorithm
+        self.env = env  # the environment for training
+        self.cwd = None  # current work directory. None means set automatically
+        self.visible_gpu = '0'  # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
+        self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
+
+        '''Arguments for training'''
+        self.num_episode = 1000  # to control the train episodes for PPO
+        self.gamma = 0.995  # discount factor of future rewards
+        self.learning_rate = 1e-4  # 2e-4 / 6e-5 / 2 ** -4
+        self.soft_update_tau = 2 ** -8  # 5e-3 / 4e-4 / 2 ** -8 1e-3
+
+        self.net_dim = 256  # the network width
+        self.batch_size = 4096  # num of transitions sampled from replay buffer.
+        self.repeat_times = 2 ** 5  # collect target_step, then update network
+        self.target_step = 4096  # repeatedly update network to keep critic's loss small
+        self.max_memo = self.target_step  # capacity of replay buffer
+        self.if_gae_or_raw = True  # GAE for on-policy sparse reward: Generalized Advantage Estimation.
+
+        '''Arguments for evaluate'''
+        self.random_seed = 1234  # initialize random seed in self.init_before_training()
+        # self.random_seed_list = [1234, 2234, 3234]
+        self.random_seed_list = [3234]
+        self.train = True
+        self.save_network = True
+        self.test_network = True
+        self.save_test_data = True
+        self.compare_with_gurobi = True
+        self.plot_on = True
+
+    def init_before_training(self):
+        if self.cwd is None:
+            agent_name = self.agent.__class__.__name__
+            self.cwd = f'./{agent_name}'
+
+        np.random.seed(self.random_seed)
+        torch.manual_seed(self.random_seed)
+        torch.set_num_threads(self.num_threads)
+        torch.set_default_dtype(torch.float32)
+
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
+
+
+def update_buffer(_trajectory):
+    _trajectory = list(map(list, zip(*_trajectory)))  # 2D-list transpose, here cut the trajectory into 5 parts 
+    ten_state = torch.as_tensor(_trajectory[0], dtype=torch.float32)  # tensor state here
+    ten_reward = torch.as_tensor(_trajectory[1], dtype=torch.float32)
+    # _trajectory[2] = done, replace done by mask, save memory
+    ten_mask = (1.0 - torch.as_tensor(_trajectory[2], dtype=torch.float32)) * gamma
+    ten_action = torch.as_tensor(_trajectory[3], dtype=torch.float32)
+    ten_noise = torch.as_tensor(_trajectory[4], dtype=torch.float32)
+
+    buffer[:] = (ten_state, ten_action, ten_noise, ten_reward, ten_mask)  # list store tensors
+
+    _steps = ten_reward.shape[0]  # how many steps are collected in all trajectories
+    _r_exp = ten_reward.mean()  # the mean reward
+    return _steps, _r_exp
+
+
+def test_one_episode(env, act, device, index):
+    """to get evaluate information, here record the unbalance of after taking action"""
+    record_state = []
+    record_action = []
+    record_reward = []
+    record_unbalance = []
+    record_system_info = []  # [time, price, netload, action, real action, soc, output*4, unbalance, cost]
+    record_init_info = []  # include month,day,time,intial soc
+    env.TRAIN = False
+    state = env.reset()
+    record_init_info.append([env.month, env.day, env.current_time, env.battery.current_capacity])
+    print(f'current testing month is {env.month}, day is {env.day},initial_soc is {env.battery.current_capacity}')
+    llm_actions = load_llm_actions('data/results.json')
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        rl_action = a_tensor.detach().cpu().numpy()[0]
+        llm_action = llm_actions[index]
+        action = [0.95 * rl_action[i] + 0.05 * llm_action[i] for i in range(5)]
+        real_action = action
+        state, next_state, reward, done = env.step(action)
+
+        record_system_info.append([state[0], state[1], state[3] + env.wind.current_power, action, real_action,
+                                   env.battery.SOC(), env.battery.energy_change, next_state[4], next_state[5],
+                                   next_state[6], env.solar.current_power, env.wind.current_power, env.unbalance,
+                                   env.operation_cost, reward])
+        record_state.append(state)
+        record_action.append(real_action)
+        record_reward.append(reward)
+        record_unbalance.append(env.unbalance)
+        state = next_state
+    # add information of last step dg1, dh2, dg3, soc, tem, irr
+    record_system_info[-1][7:12] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
+                                    env.final_step_outputs[4], env.final_step_outputs[5]]
+    record_system_info[-1][5] = env.final_step_outputs[3]
+    record = {'init_info': record_init_info, 'system_info': record_system_info, 'state': record_state,
+              'action': record_action, 'reward': record_reward, 'unbalance': record_unbalance}
+    return record
+
+
+def test_llm(env, index):
+    record_system_info = []
+    record_init_info = []
+    env.TRAIN = False
+    record_init_info.append([env.month, env.day, env.current_time, env.battery.current_capacity])
+    llm_actions = load_llm_actions('data/results.json')
+
+    cumulative_reward = 0
+    for i in range(24):
+        action = llm_actions[index + i]
+        real_action = action
+        state, next_state, reward, done = env.step(action)
+        record_system_info.append([state[0], state[1], state[3] + env.wind.current_power, action, real_action,
+                                   env.battery.SOC(), env.battery.energy_change, next_state[4], next_state[5],
+                                   next_state[6], env.solar.current_power, env.wind.current_power, env.unbalance,
+                                   env.operation_cost, reward])
+        cumulative_reward += reward
+        if done:
+            break
+
+    record_system_info[-1][7:12] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
+                                    env.final_step_outputs[4], env.final_step_outputs[5]]
+    record_system_info[-1][5] = env.final_step_outputs[3]
+    record = {'system_info': record_system_info, 'cumulative_reward': cumulative_reward}
+    return record
+
+
+def get_episode_return(env, act, device, index):
+    episode_reward = 0.0
+    episode_unbalance = 0.0
+    state = env.reset()
+    llm_actions = load_llm_actions('data/results.json')
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        rl_action = a_tensor.detach().cpu().numpy()[0]
+        llm_action = llm_actions[index]
+        action = [0.95 * rl_action[i] + 0.05 * llm_action[i] for i in range(5)]
+        state, next_state, reward, done, = env.step(action)
+        state = next_state
+        episode_reward += reward
+        episode_unbalance += env.real_unbalance
+        if done:
+            break
+    return episode_reward, episode_unbalance
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0,1'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentPPO()
+        args.agent.cri_target = True
+        agent_name = f'{args.agent.__class__.__name__}'
+
+        args.env = ESSEnv()
+        args.init_before_training()
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_gae_or_raw)
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size  # how much data should be used to update net
+        target_step = args.target_step  # how many steps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        agent.state = env.reset()
+        '''init buffer'''
+        buffer = list()
+        '''init training parameters'''
+        num_episode = args.num_episode
+        args.train = False
+        args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        # args.plot_on = False
+        if args.train:
+            for i_episode in range(num_episode):
+                with torch.no_grad():
+                    index = (sum(Constant.MONTHS_LEN[:env.month - 1]) + env.day - 1) * 24 + env.current_time
+                    trajectory_list = agent.explore_env(env, target_step, index)
+                    steps, r_exp = update_buffer(trajectory_list)
+                critic_loss, actor_loss, entropy_loss = agent.update_net(buffer, batch_size, repeat_times,
+                                                                         soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                loss_record['entropy_loss'].append(entropy_loss)
+
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device, index)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}')
+
+    act_save_path = f'{args.cwd}/actor_llm_1015.pth'
+    loss_record_path = f'{args.cwd}/loss_llm_1015.pkl'
+    reward_record_path = f'{args.cwd}/reward_llm_1015.pkl'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        index = (sum(Constant.MONTHS_LEN[:env.month - 1]) + env.day - 1) * 24 + env.current_time
+        record = test_one_episode(env, agent.act, agent.device, index)
+        # re = test_llm(env, index)
+        rl_data = pd.DataFrame(record['system_info'])
+        # llm_data = pd.DataFrame(re['system_info'])
+        rl_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery',
+                           'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_llm_1015.pkl'
+        # test_llm_save_path = f'{args.cwd}/test_only_llm_1015.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+        # with open(test_llm_save_path, 'wb') as f:
+        #     pickle.dump(re, f)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+
+    if args.plot_on:
+        from plotDRL import *
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = 'llm_1015'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        # plot_optimization_result(base_result, plot_dir)
+        # plot_evaluation_information(args.cwd + '/' + 'test_llm_1015.pkl', plot_dir)
+        plot_soc(base_result, args.cwd + '/' + 'test_llm_1015.pkl', plot_dir)
+        plot_energy(base_result, args.cwd + '/' + 'test_llm_1015.pkl', plot_dir)
+
+        '''compare the different cost get from gurobi and PPO'''
+        print('rl_cost:', sum(rl_data['operation_cost']))
+        print('gurobi_cost:', sum(base_result['step_cost']))
+        print('ration:', sum(rl_data['operation_cost']) / sum(base_result['step_cost']))
diff --git a/PPO_llm_melt.py b/PPO_llm_melt.py
new file mode 100644
index 0000000..3344962
--- /dev/null
+++ b/PPO_llm_melt.py
@@ -0,0 +1,136 @@
+import random
+from PPO_llm import *
+from plotDRL import *
+
+
+def generate_test_dates(num):
+    dates = []
+    months_days = {1: 31, 2: 28, 3: 31, 4: 30, 5: 31, 6: 30, 7: 31, 8: 31, 9: 30, 10: 31, 11: 30, 12: 31}
+    while len(dates) < num:
+        month = random.randint(1, 12)
+        day = random.randint(20, months_days[month])
+        if (month, day) not in dates:
+            dates.append((month, day))
+    return dates
+
+
+def test_llm(env, llm_actions, index):
+    sum_reward = 0
+    sum_unbalance = 0
+    for i in range(24):
+        action = llm_actions[index + i]
+        state, next_state, reward, done = env.step(action)
+        sum_reward += reward
+        sum_unbalance += env.real_unbalance
+    record = {'reward': [sum_reward], 'unbalance': [sum_unbalance]}
+    return record
+
+
+def test_one_episode(env, act, device):
+    state = env.reset()
+    sum_reward = 0
+    sum_unbalance = 0
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        action = a_tensor.detach().cpu().numpy()[0]
+        # rl_action = a_tensor.detach().cpu().numpy()[0]
+        # llm_action = llm_actions[index + i]
+        # action = 0.95 * np.array(rl_action) + 0.05 * np.array(llm_action)
+        state, next_state, reward, done = env.step(action)
+        state = next_state
+        sum_reward += reward
+        sum_unbalance += env.real_unbalance
+    record = {'reward': [sum_reward], 'unbalance': [sum_unbalance]}
+    return record
+
+
+def run_multiple_tests(env, args, agent1, agent2, num_tests=10):
+    all_ppo = pd.DataFrame()
+    all_llm = pd.DataFrame()
+    all_lmppo = pd.DataFrame()
+    all_milp = pd.DataFrame()
+
+    test_dates = generate_test_dates(num_tests)
+    llm_actions = load_llm_actions('data/results.json')
+
+    for month, day in test_dates:
+        print(f'current testing month is {month}, day is {day}')
+        index = (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24
+
+        ppo_res = test_one_episode(env, agent1.act, agent1.device)
+        base_res = optimization_base_result(env, month, day)
+        llm_res = test_llm(env, llm_actions, index)
+        lmppo_res = test_one_episode(env, agent2.act, agent2.device)
+
+        ppo_data = pd.DataFrame(ppo_res)
+        m_reward = - base_res['step_cost'].sum()/1e3
+        milp_data = pd.DataFrame({'reward': [m_reward]})
+        llm_data = pd.DataFrame(llm_res)
+        lmppo_data = pd.DataFrame(lmppo_res)
+
+        all_ppo = pd.concat([all_ppo, ppo_data], ignore_index=True)
+        all_milp = pd.concat([all_milp, milp_data], ignore_index=True)
+        all_llm = pd.concat([all_llm, llm_data], ignore_index=True)
+        all_lmppo = pd.concat([all_lmppo, lmppo_data], ignore_index=True)
+
+    test_ppo_path = f'{args.cwd}/melt_ppo.pkl'
+    test_llm_path = f'{args.cwd}/melt_llm.pkl'
+    test_lmppo_path = f'{args.cwd}/melt_lmppo.pkl'
+    test_milp_path = f'{args.cwd}/melt_milp.pkl'
+
+    with open(test_ppo_path, 'wb') as tf:
+        pickle.dump(all_ppo, tf)
+    with open(test_milp_path, 'wb') as tf:
+        pickle.dump(all_milp, tf)
+    with open(test_llm_path, 'wb') as f:
+        pickle.dump(all_llm, f)
+    with open(test_lmppo_path, 'wb') as tf:
+        pickle.dump(all_lmppo, tf)
+
+    plot_args = PlotArgs()
+    plot_args.feature_change = 'llm_1015'
+    args.cwd = agent_name
+    plot_dir = make_dir(args.cwd, plot_args.feature_change)
+    plot_melt(test_ppo_path, test_llm_path, test_lmppo_path, test_milp_path, plot_dir)
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent1 = args.agent2 = AgentPPO()
+        args.agent1.cri_target = args.agent2.cri_target = True
+        agent_name = f'{args.agent1.__class__.__name__}'
+
+        args.env = ESSEnv()
+        args.init_before_training()
+
+        agent1 = args.agent1
+        agent2 = args.agent2
+        env = args.env
+        agent1.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                    args.if_gae_or_raw)
+        agent2.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                    args.if_gae_or_raw)
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size
+        target_step = args.target_step
+        repeat_times = args.repeat_times
+        soft_update_tau = args.soft_update_tau
+        agent1.state = agent2.state = env.reset()
+        buffer = list()
+        num_episode = args.num_episode
+        # args.test_network = False
+
+    if args.test_network:
+        args.cwd = agent_name
+        ppo_act_save_path = f'{args.cwd}/actor_10.pth'
+        lmppo_act_save_path = f'{args.cwd}/actor_llm_1015.pth'
+        agent1.act.load_state_dict(torch.load(ppo_act_save_path))
+        agent2.act.load_state_dict(torch.load(lmppo_act_save_path))
+        print('parameters have been reload and test')
+        env.TRAIN = False
+        run_multiple_tests(env, args, agent1, agent2, num_tests=12)
diff --git a/PPO_llm_melt_2.py b/PPO_llm_melt_2.py
new file mode 100644
index 0000000..65f8b84
--- /dev/null
+++ b/PPO_llm_melt_2.py
@@ -0,0 +1,200 @@
+import random
+from PPO_llm import *
+from plotDRL import *
+
+test_day = 9  # 测试天数
+
+
+def test_ram_episode(env):
+    state = env.reset()
+    sum_reward = 0
+    sum_unbalance = 0
+    for i in range(24):
+        action = np.random.uniform(env.action_space.low, env.action_space.high)
+        state, next_state, reward, done = env.step(action)
+        state = next_state
+        sum_reward += reward
+        sum_unbalance += env.real_unbalance
+    record = {'reward': [sum_reward], 'unbalance': [sum_unbalance]}
+    return record
+
+
+def test_llm(env, llm_actions, index):
+    sum_reward = 0
+    sum_unbalance = 0
+    for i in range(24):
+        action = llm_actions[index + i]
+        state, next_state, reward, done = env.step(action)
+        sum_reward += reward
+        sum_unbalance += env.real_unbalance
+    record = {'reward': [sum_reward], 'unbalance': [sum_unbalance]}
+    return record
+
+
+def test_one_episode(env, act, device):
+    state = env.reset()
+    sum_reward = 0
+    sum_unbalance = 0
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        action = a_tensor.detach().cpu().numpy()[0]
+        # rl_action = a_tensor.detach().cpu().numpy()[0]
+        # llm_action = llm_actions[index + i]
+        # action = 0.95 * np.array(rl_action) + 0.05 * np.array(llm_action)
+        state, next_state, reward, done = env.step(action)
+        state = next_state
+        sum_reward += reward
+        sum_unbalance += env.real_unbalance
+    record = {'reward': [sum_reward], 'unbalance': [sum_unbalance]}
+    return record
+
+
+def generate_test_dates():
+    # months_days = {1: 31, 2: 28, 3: 31, 4: 30, 5: 31, 6: 30, 7: 31, 8: 31, 9: 30, 10: 31, 11: 30, 12: 31}
+    dates = []
+    for month in range(1, 13):  # 遍历12个月
+        start_day = 20
+        # days_in_month = months_days[month]  # 确保每个月至少有9天
+        # if days_in_month >= start_day + 9:  # 生成8个连续的天
+
+        for day in range(start_day, start_day + test_day):
+            dates.append((month, day))
+    return dates
+
+
+def run_multiple_tests(env, args, agent1, agent2):
+    all_ppo = pd.DataFrame()
+    all_llm = pd.DataFrame()
+    all_lmppo = pd.DataFrame()
+    all_milp = pd.DataFrame()
+    all_ram = pd.DataFrame()
+
+    test_dates = generate_test_dates()  # 获取测试日期
+    llm_actions = load_llm_actions('data/results_day.json')
+
+    # 对每个日期进行测试
+    for idx in range(0, len(test_dates), test_day):
+        current_dates = test_dates[idx:idx + test_day]
+        print(f'Start testing for {current_dates[0]} to {current_dates[-1]}')
+
+        ppo_rewards = []
+        ppo_unbalances = []
+        llm_rewards = []
+        llm_unbalances = []
+        lmppo_rewards = []
+        lmppo_unbalances = []
+        milp_rewards = []
+        ram_rewards = []
+        ram_unbalances = []
+
+        # 对当前这x天的日期进行逐个测试
+        for month, day in current_dates:
+            index = (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24
+
+            ppo_res = test_one_episode(env, agent1.act, agent1.device)
+            base_res = optimization_base_result(env, month, day)
+            llm_res = test_llm(env, llm_actions, index)
+            lmppo_res = test_one_episode(env, agent2.act, agent2.device)
+            ram_res = test_ram_episode(env)
+
+            # 存储每一天的结果
+            ppo_rewards.append(ppo_res['reward'][0])
+            ppo_unbalances.append(ppo_res['unbalance'][0])
+            llm_rewards.append(llm_res['reward'][0])
+            llm_unbalances.append(llm_res['unbalance'][0])
+            lmppo_rewards.append(lmppo_res['reward'][0])
+            lmppo_unbalances.append(lmppo_res['unbalance'][0])
+            m_reward = - base_res['step_cost'].sum() / 1e3
+            milp_rewards.append(m_reward)
+            ram_rewards.append(ram_res['reward'][0])
+            ram_unbalances.append(ram_res['unbalance'][0])
+
+        # 计算x天的平均结果
+        avg_ppo_reward = sum(ppo_rewards) / len(ppo_rewards)
+        avg_ppo_unbalance = sum(ppo_unbalances) / len(ppo_unbalances)
+        avg_llm_reward = sum(llm_rewards) / len(llm_rewards)
+        avg_llm_unbalance = sum(llm_unbalances) / len(llm_unbalances)
+        avg_lmppo_reward = sum(lmppo_rewards) / len(lmppo_rewards)
+        avg_lmppo_unbalance = sum(lmppo_unbalances) / len(lmppo_unbalances)
+        avg_milp_reward = sum(milp_rewards) / len(milp_rewards)
+        avg_ram_reward = sum(ram_rewards) / len(ram_rewards)
+        avg_ram_unbalance = sum(ram_unbalances) / len(ram_unbalances)
+
+        # 保存每组x天的平均结果
+        ppo_data = pd.DataFrame({'reward': [avg_ppo_reward], 'unbalance': [avg_ppo_unbalance]})
+        llm_data = pd.DataFrame({'reward': [avg_llm_reward], 'unbalance': [avg_llm_unbalance]})
+        lmppo_data = pd.DataFrame({'reward': [avg_lmppo_reward], 'unbalance': [avg_lmppo_unbalance]})
+        milp_data = pd.DataFrame({'reward': [avg_milp_reward]})
+        ram_data = pd.DataFrame({'reward': [avg_ram_reward], 'unbalance': [avg_ram_unbalance]})
+
+        all_ppo = pd.concat([all_ppo, ppo_data], ignore_index=True)
+        all_llm = pd.concat([all_llm, llm_data], ignore_index=True)
+        all_lmppo = pd.concat([all_lmppo, lmppo_data], ignore_index=True)
+        all_milp = pd.concat([all_milp, milp_data], ignore_index=True)
+        all_ram = pd.concat([all_ram, ram_data], ignore_index=True)
+
+    # 保存结果
+    test_ppo_path = f'{args.cwd}/melt_ppo.pkl'
+    test_llm_path = f'{args.cwd}/melt_llm.pkl'
+    test_lmppo_path = f'{args.cwd}/melt_lmppo.pkl'
+    test_milp_path = f'{args.cwd}/melt_milp.pkl'
+    test_ram_path = f'{args.cwd}/melt_ram.pkl'
+
+    with open(test_ppo_path, 'wb') as tf:
+        pickle.dump(all_ppo, tf)
+    with open(test_milp_path, 'wb') as tf:
+        pickle.dump(all_milp, tf)
+    with open(test_llm_path, 'wb') as f:
+        pickle.dump(all_llm, f)
+    with open(test_lmppo_path, 'wb') as tf:
+        pickle.dump(all_lmppo, tf)
+    with open(test_ram_path, 'wb') as tf:
+        pickle.dump(all_ram, tf)
+
+    plot_args = PlotArgs()
+    plot_args.feature_change = 'llm_1015'
+    args.cwd = agent_name
+    plot_dir = make_dir(args.cwd, plot_args.feature_change)
+    plot_melt(test_ppo_path, test_llm_path, test_lmppo_path, test_milp_path, test_ram_path, plot_dir)
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent1 = args.agent2 = AgentPPO()
+        args.agent1.cri_target = args.agent2.cri_target = True
+        agent_name = f'{args.agent1.__class__.__name__}'
+
+        args.env = ESSEnv()
+        args.init_before_training()
+
+        agent1 = args.agent1
+        agent2 = args.agent2
+        env = args.env
+        agent1.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                    args.if_gae_or_raw)
+        agent2.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                    args.if_gae_or_raw)
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size
+        target_step = args.target_step
+        repeat_times = args.repeat_times
+        soft_update_tau = args.soft_update_tau
+        agent1.state = agent2.state = env.reset()
+        buffer = list()
+        num_episode = args.num_episode
+        # args.test_network = False
+
+    if args.test_network:
+        args.cwd = agent_name
+        ppo_act_save_path = f'{args.cwd}/actor_10.pth'
+        lmppo_act_save_path = f'{args.cwd}/actor_llm_1015.pth'
+        agent1.act.load_state_dict(torch.load(ppo_act_save_path))
+        agent2.act.load_state_dict(torch.load(lmppo_act_save_path))
+        print('parameters have been reload and test')
+        env.TRAIN = False
+        run_multiple_tests(env, args, agent1, agent2)
diff --git a/PPO_ramdom.py b/PPO_ramdom.py
new file mode 100644
index 0000000..63db23b
--- /dev/null
+++ b/PPO_ramdom.py
@@ -0,0 +1,371 @@
+import os
+import pickle
+from copy import deepcopy
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from environment import ESSEnv
+from tools import get_episode_return, test_one_episode, optimization_base_result
+
+
+class ActorPPO(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, action_dim))
+        # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
+        self.a_logstd = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
+        self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))
+
+    def forward(self, state):
+        return self.net(state).tanh()  # action.tanh() limit the data output of action
+
+    def get_action(self, state):
+        a_avg = self.forward(state)  # too big for the action
+        a_std = self.a_logstd.exp()
+
+        noise = torch.randn_like(a_avg)
+        action = a_avg + noise * a_std
+        return action, noise
+
+    def get_logprob_entropy(self, state, action):
+        a_avg = self.forward(state)
+        a_std = self.a_logstd.exp()
+
+        delta = ((a_avg - action) / a_std).pow(2) * 0.5
+        logprob = -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # new_logprob
+
+        dist_entropy = (logprob.exp() * logprob).mean()  # policy entropy
+        return logprob, dist_entropy
+
+    def get_old_logprob(self, _action, noise):  # noise = action - a_noise
+        delta = noise.pow(2) * 0.5
+        return -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1)  # old_logprob
+
+
+class CriticAdv(nn.Module):
+    def __init__(self, mid_dim, state_dim, _action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, 1))
+
+    def forward(self, state):
+        return self.net(state)  # Advantage value
+
+
+def smooth_rewards(rewards, window=10):
+    rewards = rewards.unsqueeze(0).unsqueeze(0)  # 将 rewards 转为 [1, 1, len] 的形状以适应 conv1d
+    kernel = torch.ones(1, 1, window, device=rewards.device) / window  # 创建一个均匀的滑动平均核
+    smoothed_rewards = F.conv1d(rewards, kernel, padding='valid')  # 滑动平均
+    smoothed_rewards = smoothed_rewards.squeeze(0).squeeze(0)  # 去掉多余的维度
+
+    # 保持与原始奖励序列相同的长度，将前 window-1 个奖励保持不变
+    return torch.cat((rewards[0, 0, :window-1], smoothed_rewards))
+
+
+class AgentPPO:
+    def __init__(self):
+        super().__init__()
+        self.state = None
+        self.device = None
+        self.action_dim = None
+        self.get_obj_critic = None
+
+        self.criterion = torch.nn.SmoothL1Loss()
+        self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None
+        self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
+
+        '''init modify'''
+        self.ClassCri = CriticAdv
+        self.ClassAct = ActorPPO
+
+        self.ratio_clip = 0.2  # ratio.clamp(1 - clip, 1 + clip)
+        self.lambda_entropy = 0.02  # could be 0.01~0.05
+        self.lambda_gae_adv = 0.98  # could be 0.95~0.99, (Generalized Advantage Estimation. ICLR.2016.)
+        self.get_reward_sum = None  # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+        self.trajectory_list = None
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=True, gpu_id=0):
+        self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
+        self.trajectory_list = list()
+        # choose whether to use gae or not
+        self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+
+        self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
+        self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
+        self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
+        self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
+
+        self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)
+        self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri
+
+    def select_action(self, state):
+        # states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        # _, noises = self.act.get_action(states)
+        actions = np.random.uniform(env.action_space.low, env.action_space.high)
+        return actions, 0
+
+    def explore_env(self, env, target_step):
+        state = self.state  # sent state to agent and then agent sent state to method
+        trajectory_temp = list()
+        last_done = 0
+        for i in range(target_step):
+            action, noise = self.select_action(state)
+            state, next_state, reward, done, = env.step(np.tanh(action))
+            trajectory_temp.append((state, reward, done, action, noise))
+            if done:
+                state = env.reset()
+                last_done = i
+            else:
+                state = next_state
+        self.state = state
+
+        '''splice list'''
+        # store 0 trajectory information to list
+        trajectory_list = self.trajectory_list + trajectory_temp[:last_done + 1]
+        self.trajectory_list = trajectory_temp[last_done:]
+        return trajectory_list
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
+        """put data extract and update network together"""
+        with torch.no_grad():
+            buf_len = buffer[0].shape[0]
+            # decompose buffer data
+            buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
+
+            '''get buf_r_sum, buf_logprob'''
+            bs = 4096
+            buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
+            buf_value = torch.cat(buf_value, dim=0)
+
+            # 不需要获取logprob，因为我们不再优化actor网络
+            buf_logprob = torch.zeros_like(buf_value)  # 随机策略不会有有效的logprob
+
+            buf_r_sum, buf_advantage = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value)
+            # normalize advantage
+            buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
+            buf_advantage = smooth_rewards(buf_advantage, window=10)
+            del buf_noise, buffer[:]
+
+        '''PPO: Surrogate objective of Trust Region'''
+        obj_critic = obj_actor = None
+
+        # 由于使用随机策略，更新网络的过程被跳过
+        # 如果没有需要更新的策略网络，则只需要进行critic网络的简单优化
+        for _ in range(int(buf_len / batch_size * repeat_times)):
+            indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
+
+            state = buf_state[indices]
+            action = buf_action[indices]
+            r_sum = buf_r_sum[indices]
+            logprob = buf_logprob[indices]
+            advantage = buf_advantage[indices]
+
+            # 只更新critic网络
+            value = self.cri(state).squeeze(1)  # critic network预测状态的总奖励（Q值）
+            obj_critic = self.criterion(value, r_sum)  # 计算value loss
+            self.optim_update(self.cri_optim, obj_critic)  # 更新critic网络的参数
+            # 选择是否使用软更新
+            self.soft_update(self.cri_target, self.cri, soft_update_tau) if self.cri_target is not self.cri else None
+
+        a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1))
+        return obj_critic.item(), 0, a_std_log.mean().item()  # 未更新actor网络，因此obj_actor为0
+
+    def get_reward_sum_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # reward sum
+
+        pre_r_sum = 0
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+        buf_advantage = buf_r_sum - (buf_mask * buf_value[:, 0])
+        return buf_r_sum, buf_advantage
+
+    def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # old policy value
+        buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # advantage value
+
+        pre_r_sum = 0.0
+        pre_advantage = 0.0  # advantage value of previous step
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])
+            pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
+        return buf_r_sum, buf_advantage
+
+    @staticmethod
+    def optim_update(optimizer, objective):
+        optimizer.zero_grad()
+        objective.backward()
+        optimizer.step()
+
+    @staticmethod
+    def soft_update(target_net, current_net, tau):
+        for tar, cur in zip(target_net.parameters(), current_net.parameters()):
+            tar.data.copy_(cur.data.__mul__(tau) + tar.data.__mul__(1.0 - tau))
+
+
+class Arguments:
+    def __init__(self, agent=None, env=None):
+        self.agent = agent  # Deep Reinforcement Learning algorithm
+        self.env = env  # the environment for training
+        self.cwd = None  # current work directory. None means set automatically
+        self.if_remove = False  # remove the cwd folder? (True, False, None:ask me)
+        self.visible_gpu = '0'  # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
+        self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
+
+        '''Arguments for training'''
+        self.num_episode = 1000  # to control the train episodes for PPO
+        self.gamma = 0.995  # discount factor of future rewards
+        self.learning_rate = 1e-4  # 1e-4 2 ** -14 2e-4
+        self.soft_update_tau = 2 ** -8  # 1e-3 2 ** -8
+
+        self.net_dim = 256  # the network width
+        self.batch_size = 4096  # num of transitions sampled from replay buffer.
+        self.repeat_times = 2 ** 5  # collect target_step, then update network
+        self.target_step = 4096  # repeatedly update network to keep critic's loss small
+        self.max_memo = self.target_step  # capacity of replay buffer
+        self.if_gae_or_raw = True  # GAE for on-policy sparse reward: Generalized Advantage Estimation.
+
+        '''Arguments for evaluate'''
+        self.random_seed = 1234
+        # self.random_seed_list = [1234, 2234, 3234, 4234, 5234]
+        self.random_seed_list = [1]
+        self.train = True
+        self.save_network = True
+        self.test_network = True
+        self.save_test_data = True
+        self.compare_with_gurobi = True
+        self.plot_on = True
+
+    def init_before_training(self):
+        if self.cwd is None:
+            agent_name = self.agent.__class__.__name__
+            self.cwd = f'./{agent_name}'
+
+        np.random.seed(self.random_seed)
+        torch.manual_seed(self.random_seed)
+        torch.set_num_threads(self.num_threads)
+        torch.set_default_dtype(torch.float32)
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
+
+
+def update_buffer(_trajectory):
+    _trajectory = list(map(list, zip(*_trajectory)))  # 2D-list transpose, here cut the trajectory into 5 parts 
+    ten_state = torch.as_tensor(_trajectory[0])  # tensor state here
+    ten_reward = torch.as_tensor(_trajectory[1], dtype=torch.float32)
+    # _trajectory[2] = done, 将 done 替换为掩码，节省内存
+    ten_mask = (1.0 - torch.as_tensor(_trajectory[2], dtype=torch.float32)) * gamma
+    ten_action = torch.as_tensor(_trajectory[3])
+    ten_noise = torch.as_tensor(_trajectory[4], dtype=torch.float32)
+
+    buffer[:] = (ten_state, ten_action, ten_noise, ten_reward, ten_mask)  # list store tensors
+
+    _steps = ten_reward.shape[0]  # how many steps are collected in all trajectories
+    _r_exp = ten_reward.mean()  # the mean reward
+    return _steps, _r_exp
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentPPO()
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.agent.cri_target = True
+        args.env = ESSEnv()
+        args.init_before_training()
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_gae_or_raw)
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size  # how much data should be used to update net
+        target_step = args.target_step  # how manysteps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        num_episode = args.num_episode
+        agent.state = env.reset()
+        '''init buffer'''
+        buffer = list()
+        '''init training parameters'''
+        # args.train = False
+        # args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        # args.plot_on = False
+        if args.train:
+            for i_episode in range(num_episode):
+                with torch.no_grad():
+                    trajectory_list = agent.explore_env(env, target_step)
+                    steps, r_exp = update_buffer(trajectory_list)
+                critic_loss, actor_loss, entropy_loss = agent.update_net(buffer, batch_size, repeat_times,
+                                                                         soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                loss_record['entropy_loss'].append(entropy_loss)
+
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}')
+
+    act_save_path = f'{args.cwd}/actor_ram.pth'
+    loss_record_path = f'{args.cwd}/loss_ram.pkl'
+    reward_record_path = f'{args.cwd}/reward_ram.pkl'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        record = test_one_episode(env, agent.act, agent.device)
+        eval_data = pd.DataFrame(record['system_info'])
+        eval_data.columns = ['time_step', 'price', 'load', 'action', 'real_action', 'soc', 'battery',
+                             'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_ram.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+    if args.plot_on:
+        from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = 'ram'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        plot_optimization_result(base_result, plot_dir)
+        plot_evaluation_information(args.cwd + '/' + 'test_ram.pkl', plot_dir)
+    '''compare the different cost get from gurobi and PPO'''
+    ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
+    print('rl_cost:', sum(eval_data['operation_cost']))
+    print('gurobi_cost:', sum(base_result['step_cost']))
+    print('ration:', ration)
diff --git a/SAC.py b/SAC.py
new file mode 100644
index 0000000..d180a29
--- /dev/null
+++ b/SAC.py
@@ -0,0 +1,130 @@
+import pickle
+import pandas as pd
+import torch
+
+from agent import AgentSAC
+from environment import ESSEnv
+from tools import *
+
+
+def update_buffer(_trajectory):
+    ten_state = torch.as_tensor([item[0] for item in _trajectory], dtype=torch.float32)
+    ary_other = torch.as_tensor([item[1] for item in _trajectory])
+    ary_other[:, 0] = ary_other[:, 0]  # ten_reward
+    ary_other[:, 1] = (1.0 - ary_other[:, 1]) * gamma  # ten_mask = (1.0 - ary_done) * gamma
+
+    buffer.extend_buffer(ten_state, ary_other)
+
+    _steps = ten_state.shape[0]
+    _r_exp = ary_other[:, 0].mean()  # other = (reward, mask, action)
+    return _steps, _r_exp
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentSAC()
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.agent.cri_target = True
+        args.env = ESSEnv()
+        args.init_before_training(if_main=True)
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_per_or_gae)
+        '''init replay buffer'''
+        buffer = ReplayBuffer(max_len=args.max_memo, state_dim=env.state_space.shape[0],
+                              action_dim=env.action_space.shape[0])
+        '''start training'''
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size  # how much data should be used to update net
+        target_step = args.target_step  # how manysteps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        agent.state = env.reset()
+        '''collect data and train and update network'''
+        num_episode = args.num_episode
+        '''here record real unbalance'''
+        # args.train = False
+        # args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        if args.train:
+            collect_data = True
+            while collect_data:
+                print(f'buffer:{buffer.now_len}')
+                with torch.no_grad():
+                    trajectory = agent.explore_env(env, target_step)
+                    steps, r_exp = update_buffer(trajectory)
+                    buffer.update_now_len()
+                if buffer.now_len >= 10000:
+                    collect_data = False
+            for i_episode in range(num_episode):
+                critic_loss, actor_loss, entropy_loss = agent.update_net(buffer, batch_size, repeat_times,
+                                                                         soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                loss_record['entropy_loss'].append(entropy_loss)
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance:{episode_unbalance}, buffer_length: {buffer.now_len}')
+                if i_episode % 10 == 0:
+                    # target_step
+                    with torch.no_grad():
+                        trajectory = agent.explore_env(env, target_step)
+                        steps, r_exp = update_buffer(trajectory)
+
+    act_save_path = f'{args.cwd}/actor.pth'
+    loss_record_path = f'{args.cwd}/loss_data.pkl'
+    reward_record_path = f'{args.cwd}/reward_data.pkl'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        record = test_one_episode(env, agent.act, agent.device)
+        eval_data = pd.DataFrame(record['system_info'])
+        eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery',
+                             'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_data.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+    if args.plot_on:
+        from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = '10'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        plot_optimization_result(base_result, plot_dir)
+        plot_evaluation_information(args.cwd + '/' + 'test_data.pkl', plot_dir)
+    '''compare the different cost get from gurobi and SAC'''
+    ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
+    print('rl_cost:', sum(eval_data['operation_cost']))
+    print('gurobi_cost:', sum(base_result['step_cost']))
+    print('ration:', ration)
diff --git a/TD3.py b/TD3.py
new file mode 100644
index 0000000..103bae3
--- /dev/null
+++ b/TD3.py
@@ -0,0 +1,127 @@
+import pickle
+import pandas as pd
+import torch
+
+from agent import AgentTD3
+from environment import ESSEnv
+from tools import *
+
+
+def update_buffer(_trajectory):
+    ten_state = torch.as_tensor([item[0] for item in _trajectory], dtype=torch.float32)
+    ary_other = torch.as_tensor([item[1] for item in _trajectory])
+    ary_other[:, 0] = ary_other[:, 0]  # ten_reward
+    ary_other[:, 1] = (1.0 - ary_other[:, 1]) * gamma  # ten_mask = (1.0 - ary_done) * gamma
+
+    buffer.extend_buffer(ten_state, ary_other)
+
+    _steps = ten_state.shape[0]
+    _r_exp = ary_other[:, 0].mean()  # other = (reward, mask, action)
+    return _steps, _r_exp
+
+
+if __name__ == '__main__':
+    args = Arguments()
+    reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
+    loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
+    args.visible_gpu = '0'
+    for seed in args.random_seed_list:
+        args.random_seed = seed
+        args.agent = AgentTD3()
+        agent_name = f'{args.agent.__class__.__name__}'
+        args.agent.cri_target = True
+        args.env = ESSEnv()
+        args.init_before_training(if_main=True)
+        '''init agent and environment'''
+        agent = args.agent
+        env = args.env
+        agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate,
+                   args.if_per_or_gae)
+        '''init replay buffer'''
+        buffer = ReplayBuffer(max_len=args.max_memo, state_dim=env.state_space.shape[0],
+                              action_dim=env.action_space.shape[0])
+        '''start training'''
+        cwd = args.cwd
+        gamma = args.gamma
+        batch_size = args.batch_size  # how much data should be used to update net
+        target_step = args.target_step  # how manysteps of one episode should stop
+        repeat_times = args.repeat_times  # how many times should update for one batch size data
+        soft_update_tau = args.soft_update_tau
+        agent.state = env.reset()
+        '''collect data and train and update network'''
+        num_episode = args.num_episode
+        # args.train = False
+        # args.save_network = False
+        # args.test_network = False
+        # args.save_test_data = False
+        # args.compare_with_gurobi = False
+        if args.train:
+            collect_data = True
+            while collect_data:
+                print(f'buffer:{buffer.now_len}')
+                with torch.no_grad():
+                    trajectory = agent.explore_env(env, target_step)
+                    steps, r_exp = update_buffer(trajectory)
+                    buffer.update_now_len()
+                if buffer.now_len >= 10000:
+                    collect_data = False
+            for i_episode in range(num_episode):
+                critic_loss, actor_loss = agent.update_net(buffer, batch_size, repeat_times, soft_update_tau)
+                loss_record['critic_loss'].append(critic_loss)
+                loss_record['actor_loss'].append(actor_loss)
+                with torch.no_grad():
+                    episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
+                    reward_record['mean_episode_reward'].append(episode_reward)
+                    reward_record['unbalance'].append(episode_unbalance)
+                print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}, buffer_length: {buffer.now_len}')
+                if i_episode % 10 == 0:
+                    # target_step
+                    with torch.no_grad():
+                        trajectory = agent.explore_env(env, target_step)
+                        steps, r_exp = update_buffer(trajectory)
+
+    loss_record_path = f'{args.cwd}/loss_data.pkl'
+    reward_record_path = f'{args.cwd}/reward_data.pkl'
+    act_save_path = f'{args.cwd}/actor.pth'
+
+    if args.save_network:
+        with open(loss_record_path, 'wb') as tf:
+            pickle.dump(loss_record, tf)
+        with open(reward_record_path, 'wb') as tf:
+            pickle.dump(reward_record, tf)
+        torch.save(agent.act.state_dict(), act_save_path)
+        print('actor parameters have been saved')
+
+    if args.test_network:
+        args.cwd = agent_name
+        agent.act.load_state_dict(torch.load(act_save_path))
+        print('parameters have been reload and test')
+        record = test_one_episode(env, agent.act, agent.device)
+        eval_data = pd.DataFrame(record['system_info'])
+        eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery',
+                             'gen1', 'gen2', 'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    if args.save_test_data:
+        test_data_save_path = f'{args.cwd}/test_data.pkl'
+        with open(test_data_save_path, 'wb') as tf:
+            pickle.dump(record, tf)
+
+    '''compare with gurobi data and results'''
+    if args.compare_with_gurobi:
+        month = record['init_info'][0][0]
+        day = record['init_info'][0][1]
+        initial_soc = record['init_info'][0][3]
+        base_result = optimization_base_result(env, month, day, initial_soc)
+    if args.plot_on:
+        from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
+
+        plot_args = PlotArgs()
+        plot_args.feature_change = '10'
+        args.cwd = agent_name
+        plot_dir = make_dir(args.cwd, plot_args.feature_change)
+        plot_optimization_result(base_result, plot_dir)
+        plot_evaluation_information(args.cwd + '/' + 'test_data.pkl', plot_dir)
+    '''compare the different cost get from gurobi and TD3'''
+    ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
+    print('rl_cost:', sum(eval_data['operation_cost']))
+    print('gurobi_cost:', sum(base_result['step_cost']))
+    print('ration:', ration)
diff --git a/agent.py b/agent.py
new file mode 100644
index 0000000..b263f19
--- /dev/null
+++ b/agent.py
@@ -0,0 +1,313 @@
+import numpy.random as rd
+import os
+import json
+from copy import deepcopy
+from net import *
+
+
+class AgentBase:
+    def __init__(self):
+        self.state = None
+        self.device = None
+        self.action_dim = None
+        self.if_off_policy = None
+        self.explore_noise = None
+        self.trajectory_list = None
+
+        self.criterion = torch.nn.SmoothL1Loss()
+        self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None
+        self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, _if_per_or_gae=False, gpu_id=0):
+        # 显式调用self.init()进行多进程
+        self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
+        self.action_dim = action_dim
+
+        self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
+        self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
+        self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
+        self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
+
+        self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)
+        self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri
+        del self.ClassCri, self.ClassAct
+
+    def select_action(self, state) -> np.ndarray:
+        states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        action = self.act(states)[0]
+        action = (action + torch.randn_like(action) * self.explore_noise).clamp(-1, 1)
+        return action.detach().cpu().numpy()
+
+    def explore_env(self, env, target_step):
+        state = self.state
+        trajectory = list()
+        for _ in range(target_step):
+            action = self.select_action(state)
+            state, next_state, reward, done, = env.step(action)
+            trajectory.append((state, (reward, done, *action)))
+            state = env.reset() if done else next_state
+        self.state = state
+        return trajectory
+
+    @staticmethod
+    def optim_update(optimizer, objective):
+        optimizer.zero_grad()
+        objective.backward()
+        optimizer.step()
+
+    @staticmethod
+    def soft_update(target_net, current_net, tau):
+        for tar, cur in zip(target_net.parameters(), current_net.parameters()):
+            tar.data.copy_(cur.data * tau + tar.data * (1.0 - tau))
+
+    def save_or_load_agent(self, cwd, if_save):
+        def load_torch_file(model_or_optim, _path):
+            state_dict = torch.load(_path, map_location=lambda storage, loc: storage)
+            model_or_optim.load_state_dict(state_dict)
+
+        name_obj_list = [('actor', self.act), ('act_target', self.act_target), ('act_optim', self.act_optim),
+                         ('critic', self.cri), ('cri_target', self.cri_target), ('cri_optim', self.cri_optim), ]
+        name_obj_list = [(name, obj) for name, obj in name_obj_list if obj is not None]
+        if if_save:
+            for name, obj in name_obj_list:
+                save_path = f"{cwd}/{name}.pth"
+                torch.save(obj.state_dict(), save_path)
+        else:
+            for name, obj in name_obj_list:
+                save_path = f"{cwd}/{name}.pth"
+                load_torch_file(obj, save_path) if os.path.isfile(save_path) else None
+
+
+class AgentDDPG(AgentBase):
+    def __init__(self):
+        super().__init__()
+        self.explore_noise = 0.1
+        self.if_use_cri_target = self.if_use_act_target = True
+        self.ClassCri = Critic
+        self.ClassAct = Actor
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> (float, float):
+        buffer.update_now_len()
+        obj_critic = obj_actor = None
+        for _ in range(int(buffer.now_len / batch_size * repeat_times)):
+            obj_critic, state = self.get_obj_critic(buffer, batch_size)  # critic loss
+            self.optim_update(self.cri_optim, obj_critic)
+            self.soft_update(self.cri_target, self.cri, soft_update_tau)
+
+            action_pg = self.act(state)  # policy gradient
+            obj_actor = -self.cri(state, action_pg).mean()  # actor loss, makes it bigger
+            self.optim_update(self.act_optim, obj_actor)
+            self.soft_update(self.act_target, self.act, soft_update_tau)
+        return obj_actor.item(), obj_critic.item()
+
+    def get_obj_critic(self, buffer, batch_size) -> (torch.Tensor, torch.Tensor):
+        with torch.no_grad():
+            reward, mask, action, state, next_s = buffer.sample_batch(batch_size)
+            next_q = self.cri_target(next_s, self.act_target(next_s))
+            q_label = reward + mask * next_q
+        q_value = self.cri(state, action)
+        obj_critic = self.criterion(q_value, q_label)
+        return obj_critic, state
+
+
+class AgentTD3(AgentBase):
+    def __init__(self):
+        super().__init__()
+        self.explore_noise = 0.1  # standard deviation of exploration noise
+        self.policy_noise = 0.2  # standard deviation of policy noise
+        self.update_freq = 2  # delay update frequency
+        self.if_use_cri_target = self.if_use_act_target = True
+        self.ClassCri = CriticTwin
+        self.ClassAct = Actor
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> tuple:
+        buffer.update_now_len()
+        obj_critic = obj_actor = None
+        for update_c in range(int(buffer.now_len / batch_size * repeat_times)):
+            obj_critic, state = self.get_obj_critic(buffer, batch_size)
+            self.optim_update(self.cri_optim, obj_critic)
+
+            action_pg = self.act(state)  # policy gradient
+            obj_actor = -self.cri_target(state, action_pg).mean()  # use cri_target instead of cri for stable training
+            self.optim_update(self.act_optim, obj_actor)
+            if update_c % self.update_freq == 0:  # delay update
+                self.soft_update(self.cri_target, self.cri, soft_update_tau)
+                self.soft_update(self.act_target, self.act, soft_update_tau)
+        return obj_critic.item() / 2, obj_actor.item()
+
+    def get_obj_critic(self, buffer, batch_size) -> (torch.Tensor, torch.Tensor):
+        with torch.no_grad():
+            reward, mask, action, state, next_s = buffer.sample_batch(batch_size)
+            next_a = self.act_target.get_action(next_s, self.policy_noise)  # policy noise
+            next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a))  # twin critics
+            q_label = reward + mask * next_q
+
+        q1, q2 = self.cri.get_q1_q2(state, action)
+        obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label)  # twin critics
+        return obj_critic, state
+
+
+class AgentSAC(AgentBase):
+    def __init__(self):
+        super().__init__()
+        self.ClassCri = CriticTwin
+        self.ClassAct = ActorSAC
+        self.if_use_cri_target = True
+        self.if_use_act_target = False
+
+        self.alpha_log = None
+        self.alpha_optim = None
+        self.target_entropy = None
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, _if_use_per=False, gpu_id=0, env_num=1):
+        super().init(net_dim, state_dim, action_dim, learning_rate, _if_use_per, gpu_id)
+
+        self.alpha_log = torch.tensor((-np.log(action_dim) * np.e,), dtype=torch.float32,
+                                      requires_grad=True, device=self.device)
+        self.alpha_optim = torch.optim.Adam((self.alpha_log,), lr=learning_rate)
+        self.target_entropy = np.log(action_dim)
+
+    def select_action(self, state):
+        states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        actions = self.act.get_action(states)
+        return actions.detach().cpu().numpy()[0]
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
+        buffer.update_now_len()
+        alpha = self.alpha_log.exp().detach()
+        obj_critic = obj_actor = None
+        for _ in range(int(buffer.now_len * repeat_times / batch_size)):
+            '''objective of critic (loss function of critic)'''
+            with torch.no_grad():
+                reward, mask, action, state, next_s = buffer.sample_batch(batch_size)
+                next_a, next_log_prob = self.act_target.get_action_logprob(next_s)
+                next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a))
+                q_label = reward + mask * (next_q + next_log_prob * alpha)
+            q1, q2 = self.cri.get_q1_q2(state, action)
+            obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label)
+            self.optim_update(self.cri_optim, obj_critic)
+            self.soft_update(self.cri_target, self.cri, soft_update_tau)
+
+            '''objective of alpha (temperature parameter automatic adjustment)'''
+            action_pg, log_prob = self.act.get_action_logprob(state)  # policy gradient
+            obj_alpha = (self.alpha_log * (log_prob - self.target_entropy).detach()).mean()
+            self.optim_update(self.alpha_optim, obj_alpha)
+
+            '''objective of actor'''
+            alpha = self.alpha_log.exp().detach()
+            with torch.no_grad():
+                self.alpha_log[:] = self.alpha_log.clamp(-20, 2)
+            obj_actor = -(torch.min(*self.cri_target.get_q1_q2(state, action_pg)) + log_prob * alpha).mean()
+            self.optim_update(self.act_optim, obj_actor)
+
+            self.soft_update(self.act_target, self.act, soft_update_tau)
+        return obj_critic.item(), obj_actor.item(), alpha.item()
+
+
+class AgentPPO(AgentBase):
+    def __init__(self):
+        super().__init__()
+        self.ClassCri = CriticAdv
+        self.ClassAct = ActorPPO
+
+        self.if_off_policy = False
+        self.ratio_clip = 0.2  # ratio.clamp(1 - clip, 1 + clip)
+        self.lambda_entropy = 0.02  # could be 0.01~0.05
+        self.lambda_gae_adv = 0.98  # could be 0.95~0.99, GAE (Generalized Advantage Estimation. ICLR.2016.)
+        self.get_reward_sum = None  # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+
+    def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, if_use_gae=False, gpu_id=0, env_num=1):
+        super().init(net_dim, state_dim, action_dim, learning_rate, if_use_gae, gpu_id)
+        self.trajectory_list = list()
+        self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
+
+    def select_action(self, state):
+        states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
+        actions, noises = self.act.get_action(states)
+        return actions[0].detach().cpu().numpy(), noises[0].detach().cpu().numpy()
+
+    def explore_env(self, env, target_step):
+        state = self.state
+        trajectory_temp = list()
+        last_done = 0
+        for i in range(target_step):
+            action, noise = self.select_action(state)
+            next_state, reward, done, _ = env.step(np.tanh(action))
+            trajectory_temp.append((state, reward, done, action, noise))
+            if done:
+                state = env.reset()
+                last_done = i
+            else:
+                state = next_state
+        self.state = state
+
+        '''splice list'''
+        trajectory_list = self.trajectory_list + trajectory_temp[:last_done + 1]
+        self.trajectory_list = trajectory_temp[last_done:]
+        return trajectory_list
+
+    def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
+        with torch.no_grad():
+            buf_len = buffer[0].shape[0]
+            buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
+
+            '''get buf_r_sum, buf_logprob'''
+            bs = 2 ** 10
+            buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
+            buf_value = torch.cat(buf_value, dim=0)
+            buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)
+
+            buf_r_sum, buf_advantage = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value)  # detach()
+            buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
+            del buf_noise, buffer[:]
+
+        '''PPO: Surrogate objective of Trust Region'''
+        obj_critic = obj_actor = None
+        for _ in range(int(buf_len / batch_size * repeat_times)):
+            indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
+
+            state = buf_state[indices]
+            action = buf_action[indices]
+            r_sum = buf_r_sum[indices]
+            logprob = buf_logprob[indices]
+            advantage = buf_advantage[indices]
+
+            new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action)  # it's obj_actor
+            ratio = (new_logprob - logprob.detach()).exp()
+            surrogate1 = advantage * ratio
+            surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
+            obj_surrogate = -torch.min(surrogate1, surrogate2).mean()
+            obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
+            self.optim_update(self.act_optim, obj_actor)
+
+            value = self.cri(state).squeeze(1)  # critic network predicts the reward_sum (Q value) of state
+            obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)
+            self.optim_update(self.cri_optim, obj_critic)
+            self.soft_update(self.cri_target, self.cri, soft_update_tau) if self.cri_target is not self.cri else None
+
+        a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1))
+        return obj_critic.item(), obj_actor.item(), a_std_log.mean().item()  # logging_tuple
+
+    def get_reward_sum_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # reward sum
+
+        pre_r_sum = 0
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+        buf_advantage = buf_r_sum - (buf_mask * buf_value[:, 0])
+        return buf_r_sum, buf_advantage
+
+    def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value):
+        """tensor, tensor """
+        buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # old policy value
+        buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device)  # advantage value
+
+        pre_r_sum = 0
+        pre_advantage = 0  # advantage value of previous step
+        for i in range(buf_len - 1, -1, -1):
+            buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
+            pre_r_sum = buf_r_sum[i]
+            buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])
+            pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
+        return buf_r_sum, buf_advantage
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diff --git a/data/houseload.csv b/data/houseload.csv
new file mode 100644
index 0000000..7c60bb6
--- /dev/null
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diff --git a/data/irradiance.csv b/data/irradiance.csv
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diff --git a/data/prices.csv b/data/prices.csv
new file mode 100644
index 0000000..f4d7576
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diff --git a/data/results_day.json b/data/results_day.json
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\ No newline at end of file
diff --git a/data/temper.csv b/data/temper.csv
new file mode 100644
index 0000000..80dc424
--- /dev/null
+++ b/data/temper.csv
@@ -0,0 +1,8761 @@
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diff --git a/data/wind.csv b/data/wind.csv
new file mode 100644
index 0000000..ababdf0
--- /dev/null
+++ b/data/wind.csv
@@ -0,0 +1,8761 @@
+date_time,wind_speed
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diff --git a/data_manager.py b/data_manager.py
new file mode 100644
index 0000000..2913fce
--- /dev/null
+++ b/data_manager.py
@@ -0,0 +1,70 @@
+class Constant:
+    MONTHS_LEN = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
+
+
+class DataManager:
+    def __init__(self) -> None:
+        self.Prices = []
+        self.Load_Consumption = []
+        self.Temperature = []
+        self.Irradiance = []
+        self.Wind = []
+        self.LLM = []
+
+    def add_price_element(self, element): self.Prices.append(element)
+
+    def add_load_element(self, element): self.Load_Consumption.append(element)
+
+    def add_temperature_element(self, element): self.Temperature.append(element)
+
+    def add_irradiance_element(self, element): self.Irradiance.append(element)
+
+    def add_wind_element(self, element): self.Wind.append(element)
+
+    def add_llm_element(self, element): self.LLM.append(element)
+
+    # get current time data based on given month day, and day_time
+    def get_price_data(self, month, day, day_time):
+        return self.Prices[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time]
+
+    def get_load_cons_data(self, month, day, day_time):
+        return self.Load_Consumption[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time]
+
+    def get_temperature_data(self, month, day, day_time):
+        return self.Temperature[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time]
+
+    def get_irradiance_data(self, month, day, day_time):
+        return self.Irradiance[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time]
+
+    def get_wind_data(self, month, day, day_time):
+        return self.Wind[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time]
+
+    def get_llm_data(self, month, day, day_time):
+        index = (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + day_time
+        # print(f"Index: {index}, LLM length: {len(self.LLM)}, Month: {month}, Day: {day}, Day_time: {day_time}")
+        return self.LLM[index]
+
+    # get series data for one episode
+    def get_series_price_data(self, month, day):
+        return self.Prices[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+                           (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
+
+    def get_series_load_cons_data(self, month, day):
+        return self.Load_Consumption[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+                                     (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
+
+    def get_series_temperature_data(self, month, day):
+        return self.Temperature[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+                                (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
+
+    def get_series_irradiance_data(self, month, day):
+        return self.Irradiance[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+                               (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
+
+    def get_series_wind_data(self, month, day):
+        return self.Wind[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+                         (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
+
+    # def get_series_llm_data(self, month, day):
+    #     return self.LLM[(sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24:
+    #                     (sum(Constant.MONTHS_LEN[:month - 1]) + day - 1) * 24 + 24]
diff --git a/environment.py b/environment.py
new file mode 100644
index 0000000..099afef
--- /dev/null
+++ b/environment.py
@@ -0,0 +1,164 @@
+import gym
+import pandas as pd
+from data_manager import *
+from module import *
+from parameters import *
+
+
+class ESSEnv(gym.Env):
+    def __init__(self, **kwargs):
+        super(ESSEnv, self).__init__()
+        self.excess = None
+        self.shedding = None
+        self.unbalance = None
+        self.real_unbalance = None
+        self.operation_cost = None
+        self.current_output = None
+        self.final_step_outputs = None
+        self.data_manager = DataManager()
+        self._load_year_data()
+        self.month = 1
+        self.day = 1
+        self.TRAIN = True
+        self.current_time = None
+        self.episode_length = 24
+        self.penalty_coefficient = 50  # 约束惩罚系数
+        self.sell_coefficient = 0.1  # 售出利润系数
+        self.battery_parameters = kwargs.get('battery_parameters', battery_parameters)
+        self.dg_parameters = kwargs.get('dg_parameters', dg_parameters)
+        self.solar_parameters = kwargs.get('solar_parameters', solar_parameters)
+        self.wind_parameters = kwargs.get('wind_parameters', wind_parameters)
+
+        self.grid = Grid()
+        self.battery = Battery(self.battery_parameters)
+        self.dg1 = DG(self.dg_parameters['gen_1'])
+        self.dg2 = DG(self.dg_parameters['gen_2'])
+        self.dg3 = DG(self.dg_parameters['gen_3'])
+        self.solar = Solar(self.solar_parameters)
+        self.wind = Wind(self.wind_parameters)
+
+        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(5,), dtype=np.float32)  # 已增加调节电压动作
+        self.state_space = gym.spaces.Box(low=0, high=1, shape=(10,), dtype=np.float32)
+
+    def reset(self, *args):
+        self.month = np.random.randint(1, 13)  # choose 12 month
+        if self.TRAIN:
+            self.day = np.random.randint(1, 20)
+        else:
+            self.day = np.random.randint(20, Constant.MONTHS_LEN[self.month - 1])
+        self.current_time = 0
+        self.battery.reset()
+        self.dg1.reset()
+        self.dg2.reset()
+        self.dg3.reset()
+        self.solar.reset()
+        self.wind.reset()
+        return self._build_state()
+
+    def _build_state(self):
+        soc = self.battery.SOC()
+        dg1_output = self.dg1.current_output
+        dg2_output = self.dg2.current_output
+        dg3_output = self.dg3.current_output
+        time_step = self.current_time
+
+        price = self.data_manager.get_price_data(self.month, self.day, self.current_time)
+        houseload = self.data_manager.get_load_cons_data(self.month, self.day, self.current_time)
+        temperature = self.data_manager.get_temperature_data(self.month, self.day, self.current_time)
+        irradiance = self.data_manager.get_irradiance_data(self.month, self.day, self.current_time)
+        windspeed = self.data_manager.get_wind_data(self.month, self.day, self.current_time)
+
+        wind_gen = self.wind.step(windspeed)
+        netload = houseload - wind_gen
+
+        obs = np.concatenate((np.float32(time_step), np.float32(price), np.float32(soc), np.float32(netload),
+                              np.float32(dg1_output), np.float32(dg2_output), np.float32(dg3_output),
+                              np.float32(temperature), np.float32(irradiance), np.float32(windspeed)), axis=None)
+        return obs
+
+    def step(self, action):  # state transition: current_obs->take_action->get_reward->get_finish->next_obs
+        # 在每个组件中添加动作
+        current_obs = self._build_state()
+        temperature = current_obs[7]
+        irradiance = current_obs[8]
+        self.wind.current_power = current_obs[9]
+        self.battery.step(action[0])  # 执行状态转换，电池当前容量也改变
+        self.dg1.step(action[1])
+        self.dg2.step(action[2])
+        self.dg3.step(action[3])
+        self.solar.step(temperature, irradiance, action[4])
+        self.current_output = np.array((self.dg1.current_output, self.dg2.current_output, self.dg3.current_output,
+                                       -self.battery.energy_change, self.solar.current_power, self.wind.current_power))
+        actual_production = sum(self.current_output)
+        price = current_obs[1]
+        netload = current_obs[3] - self.solar.output_change
+        unbalance = actual_production - netload
+
+        # reward = 0.0
+        excess_penalty = 0
+        deficient_penalty = 0
+        sell_benefit, buy_cost = 0, 0
+        self.excess, self.shedding = 0, 0
+        if unbalance >= 0:  # 过剩
+            if unbalance <= self.grid.exchange_ability:
+                sell_benefit = self.grid.get_cost(price, unbalance) * self.sell_coefficient
+            else:
+                sell_benefit = self.grid.get_cost(price, self.grid.exchange_ability) * self.sell_coefficient
+                # real unbalance：超电网限值
+                self.excess = unbalance - self.grid.exchange_ability
+                excess_penalty = self.excess * self.penalty_coefficient
+        else:  # unbalance <0, 缺少惩罚
+            if abs(unbalance) <= self.grid.exchange_ability:
+                buy_cost = self.grid.get_cost(price, abs(unbalance))
+            else:
+                buy_cost = self.grid.get_cost(price, self.grid.exchange_ability)
+                self.shedding = abs(unbalance) - self.grid.exchange_ability
+                deficient_penalty = self.shedding * self.penalty_coefficient
+        battery_cost = self.battery.get_cost(self.battery.energy_change)
+        dg1_cost = self.dg1.get_cost(self.dg1.current_output)
+        dg2_cost = self.dg2.get_cost(self.dg2.current_output)
+        dg3_cost = self.dg3.get_cost(self.dg3.current_output)
+        solar_cost = self.solar.get_cost(self.solar.current_power)
+        wind_cost = self.wind.gen_cost(self.wind.current_power)
+
+        self.operation_cost = (battery_cost + dg1_cost + dg2_cost + dg3_cost + solar_cost + wind_cost
+                               + excess_penalty + deficient_penalty - sell_benefit + buy_cost)
+        reward = - self.operation_cost / 1e3
+        self.unbalance = unbalance
+        self.real_unbalance = self.shedding + self.excess
+        final_step_outputs = [self.dg1.current_output, self.dg2.current_output, self.dg3.current_output,
+                              self.battery.current_capacity, self.solar.current_power, self.wind.current_power]
+        self.current_time += 1
+        finish = (self.current_time == self.episode_length)
+        if finish:
+            self.final_step_outputs = final_step_outputs
+            self.current_time = 0
+            next_obs = self.reset()
+        else:
+            next_obs = self._build_state()
+        return current_obs, next_obs, float(reward), finish
+
+    def _load_year_data(self):
+        price_df = pd.read_csv('data/prices.csv', sep=',')
+        load_df = pd.read_csv('data/houseload.csv', sep=',')
+        irradiance_df = pd.read_csv('data/irradiance.csv', sep=',')
+        temperature_df = pd.read_csv('data/temper.csv', sep=',')
+        wind_df = pd.read_csv('data/wind.csv', sep=',')
+
+        price = price_df['price'].to_numpy(dtype=float)
+        load = load_df['houseload'].to_numpy(dtype=float)
+        irradiance = irradiance_df['irradiance'].to_numpy(dtype=float)
+        temperature = temperature_df['t2m'].to_numpy(dtype=float)
+        wind = wind_df['wind_speed'].to_numpy(dtype=float)
+
+        '''重新设计价格和发电量以及需求的大小'''
+        def process_elements(elements, transform_function, add_function):
+            for element in elements:
+                transformed_element = transform_function(element)
+                add_function(transformed_element)
+
+        process_elements(price, lambda x: max(x, 0.5), self.data_manager.add_price_element)
+        process_elements(load, lambda x: x * 3, self.data_manager.add_load_element)
+        process_elements(irradiance, lambda x: x, self.data_manager.add_irradiance_element)
+        process_elements(temperature, lambda x: x, self.data_manager.add_temperature_element)
+        process_elements(wind, lambda x: x, self.data_manager.add_wind_element)
diff --git a/excel.py b/excel.py
new file mode 100644
index 0000000..f4625d3
--- /dev/null
+++ b/excel.py
@@ -0,0 +1,40 @@
+import pandas as pd
+from fuzzywuzzy import fuzz
+
+# 读取两个Excel文件
+df1 = pd.read_excel(r'C:\Users\97532\Desktop\陕西、福建_陈晓东\福建省.xlsx')
+df2 = pd.read_excel(r'C:\Users\97532\Desktop\陕西、福建_陈晓东\福建省_许可.xlsx')
+
+# 打印df2的列名,确保列名正确
+print("原始列名:", df2.columns)
+df2.columns = df2.columns.str.strip()
+# 确保列数据类型为字符串
+df1['电厂名称'] = df1['电厂名称'].astype(str)
+df2['电厂名称/项目名称'] = df2['电厂名称/项目名称'].astype(str)
+df1['发电装机容量'] = df1['发电装机容量'].astype(float)
+df2['总装机容量'] = df2['总装机容量'].astype(float)
+
+
+# 定义一个函数来进行模糊匹配
+def fuzzy_match(row, df2):
+    best_match = None
+    highest_score = 0
+    for index2, row2 in df2.iterrows():
+        score = fuzz.partial_ratio(row['电厂名称'], row2['电厂名称/项目名称'])
+        if score > highest_score and abs(row['发电装机容量'] - row2['总装机容量']) < 0.01:
+            highest_score = score
+            best_match = row2
+    return best_match
+
+
+# 进行模糊匹配并填充数据
+for index1, row1 in df1.iterrows():
+    match_row = fuzzy_match(row1, df2)
+    if match_row is not None:
+        df1.at[index1, '单位名称'] = match_row['单位名称']
+        df1.at[index1, '发电许可证编号'] = match_row['发电许可证编号']
+        df1.at[index1, '电厂名称/项目名称'] = match_row['电厂名称/项目名称']
+        df1.at[index1, '机组类型'] = match_row['机组类型']
+# 将结果保存回Excel文件
+df1.to_excel(r'C:\Users\97532\Desktop\陕西、福建_陈晓东\福建省_填充后_模糊匹配.xlsx', index=False)
+print("数据填充已保存")
diff --git a/generate_household_data.py b/generate_household_data.py
new file mode 100644
index 0000000..1b8cc91
--- /dev/null
+++ b/generate_household_data.py
@@ -0,0 +1,35 @@
+import pandas as pd
+import numpy as np
+import os
+from nltk import word_tokenize
+
+
+def func(i, j):
+    directory_path = "//data/residential"
+    entries = os.listdir(directory_path)[i:j]
+    folders = [entry for entry in entries if os.path.join(directory_path, entry)]
+    entries = [os.path.join(directory_path, folder) for folder in folders]
+    combined_data = pd.DataFrame()
+
+    for file_path in entries:
+        current_data = pd.read_csv(file_path, encoding='utf-8')
+        tokenize_col = current_data.columns
+        tokenized_sentences_list = [word_tokenize(text) for text in tokenize_col]
+
+        occurrences_electricity = np.array(
+            [(i, j) for i, sublist in enumerate(tokenized_sentences_list) for j, word in enumerate(sublist) if
+             word == 'Electricity'])[:, 0]
+
+        Electricity_consumption_wind_solar = current_data[tokenize_col[occurrences_electricity]].sum(axis=1)
+        current_data = Electricity_consumption_wind_solar.to_frame("Household_load")
+
+        combined_data = pd.concat([combined_data, current_data], axis=1)
+
+    Series_type = combined_data.sum(axis=1)
+    Series_type.to_csv('houseload.csv', index=False)
+    total_energy = np.array(Series_type).ravel()
+    return total_energy
+
+
+if __name__ == '__main__':
+    func(0, 38)
diff --git a/llm.txt b/llm.txt
new file mode 100644
index 0000000..5696c5c
--- /dev/null
+++ b/llm.txt
@@ -0,0 +1,50 @@
+Ŀǰ����Դϵͳ�ṹ��Ҫ��̫���ܷ���վ�����ܷ���վ��������͵��ģ��ͨ����������סլ�����ӡ����������ĺͷ��ŵĶ��գ�
+As����صij��״̬������Ag1�������1�Ĺ��ʶ�����Ag2�������2�Ĺ��ʶ�����Ag3�������3�Ĺ��ʶ�����Av��̫���ܵĵ�ѹ������
+Sp�����״̬��Sc����غɵ�״̬��Sl��סլ����״̬��Sg1�������1����״̬��Sg2�������2����״̬��Sg3�������3����״̬��St���¶�״̬��Si��̫�������״̬��Sw������״̬��
+Pc����س�ŵ繦�ʣ�Pg1�������1���ʣ�Pg2�������2���ʣ�Pg3�������3���ʣ�Pso��̫���ܹ��ʣ�Pw�����ܹ��ʣ�
+Cb����سɱ���Cg1�������1�ɱ���Cg2�������2�ɱ���Cg3�������3�ɱ���Cs��̫���ܳɱ���Cw�����ܳɱ���Rs���������룻Cp������ɱ���Ee����ʣ��Դ��Es����ȱ��Դ��Pe����ʣ�ͷ���Ps����ȱ�ͷ���
+һ�����Ʒ�����Ŀ�꣺
+1������ʹ��̫���ܺͷ��ܷ��磬���޷�����������ʱ����ʹ�÷�����͵�ع��硣
+2���ڹ���ʱ���Ƕ��ֹ�����Դ��̫���ܡ����ܡ�������͵�أ����ۺ�ÿ������ģ��ijɱ�����������һʱ�̵�סլ�õ縺�أ���ʹ���������С����
+3�����ٳ������������޶�Ľ��ף��Ա��⹫����������Ӱ��סլ�õ���ȶ��ԡ�
+4����󻯽�����������С����Դ��ƽ�⡣
+��������״̬�ռ䣺��һ������Ϊ9���������������²��֣�
+Sp���������ṩ��Sc����ʼΪ0.4��ʼ�ս���0.2��0.8��Sl���������ṩ��
+Sg1����ʼ����Ϊ0��Sg2����ʼ����Ϊ0��Sg3����ʼ����Ϊ0��
+St���������ṩ��Si���������ṩ��Sw���������ṩ��
+����Sc��Sg1��Sg2��Sg3ʼ�ղ�С��0��
+�������������ռ䣺һ������Ϊ5��������x��ʾ��ǰʱ���ȡֵ, �䷶Χ��-1��1��ÿ��ʱ�̵�ֵ��������һʱ�̻����Ͻ��б仯�ģ�Ag1��Ag2��Ag3�ֱ��ʾ����ڸ������˲ʱ����100��100��200�ı仯ֵ��
+1��Ac�����������Ϊ500����ʼ�������Ϊ500*0.4=200����������仯100*x��
+2��Ag1���������仯100*x��
+3��Ag2���������仯100*x��
+4��Ag3���������仯200*x��
+5��Av��̫���ܵ�ѹ��Ϊ(1+x)����
+�ɸ�������ͨ���ı书�ʿ��Ʒ�����Ŀ��أ����ر�ʱ����Sg=0������ʱ������PgԼ�������ݳɱ���ͨ��ǰһ̨�����������ʲ���������ʣ�ฺ��ʱ���ſ��������������
+�ġ����������㹫ʽ
+1��Pc = max(0.2, min(0.8, Sc + 0.2 * action[0] * 0.9))������0.8��0.2Ϊ��غɵ�״̬�����ޣ�
+2��Pg1 = max(0, min(150, 100 * action[1] + Sg1))������150��0Ϊ�����1����ʱ�����������ޣ�
+3��Pg2 = max(0, min(375, 100 * action[2] + Sg2))������375��0Ϊ�����2����ʱ�����������ޣ�
+4��Pg3 = max(0, min(500, 200 * action[3] + Sg3))������500��0Ϊ�����2����ʱ�����������ޣ�
+5��Pso = max(0, (0.2 * Si + 0.05 * St - 9.25) * (1 + action[4]))������̫���ܷ��繦�ʲ�С��0��
+6��Pw����Ws�й�
+if 3 <= Ws < 8, Pw = Ws^3 * 172.2625 / 1000
+elif 8 <= Ws < 12, Pw = 64 * 172.2625 / 125
+else Pw = 0
+�塢�ɱ�����Դƽ�����
+P = Pc + Pg1 + Pg2 + Pg3 + Pso + Pw
+if P >= Sl , Ee = P �C Sl, else Es = Sl - P
+1��Cb = 0.01 * Asoc + 0.1 * Sc
+2��Cg1 = 0.0034 * Pg1^2 + 3 * Pg1 + 30
+3��Cg2 = 0.001 * Pg2^2 + 10 * Pg2 + 40
+4��Cg3 = 0.001 * Pg3^2 + 15 * Pg3 + 70
+5��Cs = 0.01 * Pso
+6��Cw = 0.01 * Pw
+7��Rs = 0.5 * Sp * Ee
+8��Cp = Sp * Es
+9��Pe = (Ee - 100) * 50
+10��Ps = (Es - 100) * 50
+��ΪRs��Pe��Cp��Ps��Ӧ��Դ��ʣ����Դ��ȱ�����ֻ��ͬʱ����һ�顣
+������������
+Reward = - (Cb + Cg1 + Cg2 + Cg3 + Cs + Cw + Pe + Ps - Rs + Cp) / 1000
+�ߡ�����������
+��ȡ���ݲ�ʹ��������ʽ����ÿ��ʱ�̵�״̬������ʹ������ʵ��Ż�����������ÿ��ʱ�̵Ķ������Ʒ�����ÿСʱ����һ�β�����һ������Ϊ5�����飬��ʾAc��Ag1��Ag2��Ag3��Av�Ķ������ߡ����ص�����ӦΪ����Json��ʽ��[{x1, x2, x3, x4, x5}, {x1, x2, x3, x4, x5}, ..., {x1, x2, x3, x4, x5}]
diff --git a/llm_connect.py b/llm_connect.py
new file mode 100644
index 0000000..4e9a563
--- /dev/null
+++ b/llm_connect.py
@@ -0,0 +1,73 @@
+import pandas as pd
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_openai.chat_models import ChatOpenAI
+
+
+def get_session_history(session_id: str) -> BaseChatMessageHistory:
+    if session_id not in store:
+        store[session_id] = ChatMessageHistory()
+    return store[session_id]
+
+
+def read_txt_file(file_path: str) -> str:
+    with open(file_path, "r", encoding='ISO-8859-1') as file:
+        content = file.read()
+    return content
+
+
+def read_csv_file(file_path: str) -> pd.DataFrame:
+    return pd.read_csv(file_path)
+
+
+def format_csv_data(df: pd.DataFrame, start: int, end: int) -> str:
+    formatted_data = []
+    for index, row in df.iloc[start:end].iterrows():
+        formatted_data.append(
+            f"时刻{index + 1}: Sp={row['price']}, Sl={row['load']},"
+            f"St={row['temperature']}, Si={row['irradiance']}, Sw={row['wind_speed']}"
+        )
+    return "\n".join(formatted_data)
+
+
+system_content = read_txt_file('./llm.txt')
+df = read_csv_file('./data.csv')
+llm = ChatOpenAI(
+    streaming=True,
+    verbose=True,
+    openai_api_key="none",
+    # openai_api_base="http://0.0.0.0:5049/v1/models",
+    openai_api_base="http://0.0.0.0:8501",
+    model_name="Qwen1.5-32b"
+)
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_content,),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", "{input}"),
+    ]
+)
+runnable = prompt | llm
+store = {}
+with_message_history = RunnableWithMessageHistory(
+    runnable,
+    get_session_history,
+    input_messages_key="input",
+    history_messages_key="history",
+)
+
+num_hours = len(df)
+for i in range(num_hours):
+    start = i
+    end = start + 1
+    csv_data_chunk = format_csv_data(df, start, end)
+
+    result = with_message_history.invoke(
+        {"input": f"数据如下：\n{csv_data_chunk}"},
+        config={
+            "configurable": {"session_id": "cxd"}
+        },
+    )
+    print(f"Hour {i + 1}:\n{result.content}\n")
diff --git a/llma/Reflexion.py b/llma/Reflexion.py
new file mode 100644
index 0000000..ec222be
--- /dev/null
+++ b/llma/Reflexion.py
@@ -0,0 +1,122 @@
+import json
+import os
+import subprocess
+
+from utils import get_response
+
+prompt_template = """
+You are an expert operations research analyst. Your task is to generate Gurobi code to solve the following optimization problem:
+
+{problem_description}
+
+The code should save the final optimal value in a file named 'ref_optimal_value.txt'.
+First, reason about the problem and model it. Then, generate gurobipy code to solve it. Put the code between two '=====' lines, like this:
+
+=====
+import ...
+...
+=====
+
+- Generate the complete code, including the model definition, variables, constraints, objective function, and optimization. It must be runnable.
+- Do not generate anything after the second '====='.
+- Take a deep breath and think step by step.
+"""
+
+reflection_template = """
+You are an expert operations research analyst. You have been given the task to generate Gurobi code to solve an optimization problem. You have generated the following Gurobi code:
+
+{generated_code}
+
+You have been updating the code for these errors (the last one is the most recent one):
+
+{feedback}
+
+Based on this feedback, suggest improvements to the Gurobi code.
+First, reason about the problem and model it. Then, generate gurobipy code to solve it. Put the code between two '=====' lines, like this:
+
+=====
+import ...
+...
+=====
+
+- The code should save the final optimal value in a file named 'ref_optimal_value.txt'.
+- Generate the complete code, including the model definition, variables, constraints, objective function, and optimization. It must be runnable.
+- Do not generate anything after the second '====='.
+- Take a deep breath and think step by step.
+"""
+
+
+def extract_code(text):
+    ind1 = text.find("=====")
+    ind2 = text.find("=====", ind1 + 5)
+
+    code = text[ind1 + 5: ind2].strip()
+    code = code.replace("```python", "").replace("```", "").strip()
+
+    return code
+
+
+def execute_code(file_path):
+    try:
+        # Using Python's subprocess to execute the code as a separate process
+        result = subprocess.run(["python", file_path], capture_output=True, text=True, check=True)
+        # save result in a file
+        with open(os.path.join(os.path.dirname(file_path), "ref_optimal_value.txt"), "w") as f:
+            f.write(f"Optimal Revenue: {result.stdout}\n")
+        return result.stdout, "Success"
+    except subprocess.CalledProcessError as e:
+        return e.stderr, "Error"
+
+
+def main(problem_description, dir, max_iter=3):
+    feedback = ""
+    current_prompt = prompt_template.format(problem_description=problem_description)
+
+    print(current_prompt)
+    print("====================\n\n\n\n")
+    for iteration in range(max_iter):
+        response = get_response(current_prompt, model="llama3-70b-8192")
+        code = extract_code(response)
+
+        # Save the code to a file
+        code_filename = f"generated_code_{iteration}.py"
+        code_file_path = os.path.join(dir, "ref_codes", code_filename)
+        with open(code_file_path, "w") as f:
+            f.write(code)
+
+        # Execute the code
+        output, status = execute_code(code_file_path)
+
+        # Save error output (if any)
+        error_filename = f"error_{iteration}.txt"
+        if status == "Error":
+            with open(os.path.join(dir, "ref_codes", error_filename), "w") as f:
+                f.write(output)
+
+        # Print status and update the prompt if needed
+        if status == "Success":
+            print("Code executed successfully. Output:\n", output)
+            break
+        else:
+            feedback += "\n" + output
+            current_prompt = reflection_template.format(
+                generated_code=code, feedback=feedback
+            )
+            print(f"Iteration {iteration + 1}: Error encountered. Debugging...")
+    else:
+        print("Max iterations reached with errors remaining.")
+
+
+if __name__ == "__main__":
+    dir = "data/"
+    if not os.path.exists(os.path.join(dir, "ref_codes")):
+        os.makedirs(os.path.join(dir, "ref_codes"))
+
+    with open(os.path.join(dir, "desc.txt"), "r") as f:
+        desc = f.read()
+
+    with open(os.path.join(dir, "data.json"), "r") as f:
+        data = json.load(f)
+
+    desc = desc + "\n" + json.dumps(data, indent=4)
+    main(desc, dir, max_iter=3)
diff --git a/llma/constraint.py b/llma/constraint.py
new file mode 100644
index 0000000..f3dd4ac
--- /dev/null
+++ b/llma/constraint.py
@@ -0,0 +1,312 @@
+import json
+import re
+import pandas as pd
+from rag.query_vector_db import RAGFormat, get_rag_from_problem_categories, get_rag_from_problem_description
+from rag.rag_utils import RAGMode, constraint_path
+from utils import extract_list_from_end, get_response, extract_json_from_end
+
+prompt_constraints = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+{rag}-----
+{description}
+-----
+
+And here's a list of parameters that we have extracted from the description:
+
+{params}
+
+Your task is to identify and extract constraints from the description. The constraints are the conditions that must be satisfied by the variables. Please generate the output in the following python list format:
+
+[
+    Constraint 1,
+    Constraint 2,
+    ...
+]
+
+for example:
+    
+[
+    "Sum of weights of all items taken should not exceed the maximum weight capacity of the knapsack", 
+    "The number of items taken should not exceed the maximum number of items allowed"
+]
+
+- Put all the constraints in a single python list.
+- Do not generate anything after the python list.
+- Include implicit non-negativity constraints if necessary.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+prompt_constraints_q = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Here is a list of parameters that someone has extracted from the description:
+
+{params}
+
+Consider this potential constraint: {targetConstraint}
+
+{question}
+
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+prompt_constraints_redundant = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Here is a list of parameters that someone has extracted from the description:
+
+{params}
+
+and here is a list of constraints that someone has extracted from the description:
+
+{constraints}
+
+- Is there any redundancy in the list? Can any of the constraints be removed? Can any pair of constraints be combined into a single one? If so, please provide your reasoning for each one. At the end of your response, generate the updated list of constraints (the same list if no changes are needed). Use this python list format:
+
+[
+    "Constraint 1",
+    "Constraint 2",
+    ...
+]
+
+- Do not generate anything after the list.
+
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+prompt_constraint_feedback = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Here is a list of parameters that someone has extracted from the description:
+
+{params}
+
+Here is a list of variables defined:
+
+{vars}
+
+Here is a list of constraints that someone has extracted from the description:
+
+{extracted_constraints}
+
+Your colleague is suggesting that the following constraint should be added to the list:
+
+{new_constraint}
+
+Here is its explanation:
+
+{new_constraint_explanation}
+
+Do you want to keep this constraint?
+
+- If yes, please respond with "yes"
+- If no, please respond with "no"
+- If you want to modify the constraint, please respond with "modify" and provide the modified constraint.
+
+At the end of your response, generate a json file with this format:
+{{
+    "action": "yes", "no", or "modify",
+    "updatedConstraint": The updated constraint if the action is "modify", otherwise null
+}}
+
+Please take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+
+- Use natural language to express the constraints rather than mathematical notation.
+- Do not generate anything after the json file.
+"""
+
+
+def extract_score_constraint(desc, text, params, vars, constraints, c, logger, model):
+    match = re.search(r"\d out of 5", text.lower())
+    if match:
+        score = int(match.group()[0])
+        if score > 3:
+            if logger:
+                logger.log("---")
+                logger.log(f"The confidence score is {score}, which is high enough.")
+                logger.log("---")
+            return True, constraints
+        else:
+            ask_LLM = True  # can pass this as an argument to the function instead of hardcoding it
+            if logger:
+                logger.log("---")
+                logger.log(f"The confidence score is {score}, which is not high enough.")
+                # logger.log(f"Asking the {"LLM" if ask_LLM else "user"} for feedback.")
+                if ask_LLM:
+                    logger.log("Asking the LLM for feedback.")
+                else:
+                    logger.log("Asking the user for feedback.")
+                logger.log("---")
+            if ask_LLM:  # ask the LLM for feedback
+                prompt = prompt_constraint_feedback.format(
+                    description=desc,
+                    params=json.dumps(params, indent=4),
+                    vars=json.dumps(vars, indent=4),
+                    extracted_constraints=json.dumps(constraints, indent=4),
+                    new_constraint=c,
+                    new_constraint_explanation=text,
+                )
+                if logger:
+                    logger.log("Prompting LLM for feedback:\n")
+                    logger.log(prompt)
+                llm_response = get_response(prompt, model=model)
+                if logger:
+                    logger.log("---")
+                    logger.log(f"Response: {llm_response}")
+                    logger.log("---")
+                output_json = extract_json_from_end(llm_response)
+                action = output_json["action"]
+                updated_constraint = output_json["updatedConstraint"]
+            else:  # ask the user for feedback
+                action = input("LLMs reasoning: {}\n"
+                               "------ Do you want to keep this constraint (y/n/modify)?: \n "
+                               "{} \n------ ".format(text, c))
+
+            if action.lower().startswith("y"):
+                return True, constraints
+            elif action.lower().startswith("n"):
+                constraints.remove(c)
+                return True, constraints
+            elif action.lower().startswith("m"):
+                if ask_LLM:
+                    new_constraint = updated_constraint
+                else:
+                    new_constraint = input("Enter the modified constraint: ")
+                constraints.remove(c)
+                constraints.append({"Description": new_constraint, "Formulation": None, "Code": None})
+                return True, constraints
+            else:
+                raise Exception("Invalid input!")
+    else:
+        return False, None
+
+
+def logic_check(text, constraints, c):
+    try:
+        json = extract_json_from_end(text)
+        if json["action"] == "REMOVE":
+            constraints.remove(c)
+            return True, constraints
+        elif json["action"] == "MODIFY":
+            constraints.remove(c)
+            constraints.append(json["updatedConstraint"])
+            return True, constraints
+        elif json["action"] == "KEEP":
+            return True, constraints
+        else:
+            return False, None
+    except:
+        return False, None
+
+
+qs = [
+    (
+        """
+    - Is it actually a constraint? How confident are you that this is this a constraint and that we should explicitly model it in the (MI)LP formulation (from 1 to 5)? 
+    - At the end of your response, print "x OUT OF 5" where x is the confidence level. Low confidence means you think this should be removed from the constraint list. Do not generate anything after that. 
+    """,
+        extract_score_constraint,
+    ),
+]
+
+
+def get_constraints(desc, params, model, check=False, constraints=None, logger=None,
+                    rag_mode: RAGMode | None = None, labels: dict | None = None):
+    if isinstance(rag_mode, RAGMode):
+        constraint_df = pd.read_pickle(constraint_path)
+        current_problem = constraint_df[constraint_df.description == desc]
+        if not current_problem.empty:
+            problem_name = current_problem.iloc[0].problem_name
+        else:
+            problem_name = None
+        match rag_mode:
+            case RAGMode.PROBLEM_DESCRIPTION | RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                rag = get_rag_from_problem_description(desc, RAGFormat.PROBLEM_DESCRIPTION_CONSTRAINTS, top_k=5)
+            case RAGMode.PROBLEM_LABELS:
+                assert labels is not None
+                rag = get_rag_from_problem_categories(desc, labels, RAGFormat.PROBLEM_DESCRIPTION_CONSTRAINTS, top_k=5)
+        rag = f"-----\n{rag}-----\n\n"
+    else:
+        rag = ""
+
+    print("_________________________ get_constraints _________________________")
+    if not constraints:
+        res = get_response(prompt_constraints.format(
+                description=desc,
+                params=json.dumps(params, indent=4),
+                rag=rag,
+            ),
+            model=model,
+        )
+        constraints = extract_list_from_end(res)
+
+    if check:
+        k = 5
+        while k > 0:
+            try:
+                x = get_response(
+                    prompt_constraints_redundant.format(
+                        description=desc,
+                        params=json.dumps(params, indent=4),
+                        constraints=json.dumps(constraints, indent=4),
+                    ),
+                    model=model,
+                )
+                if logger:
+                    logger.log("----")
+                    logger.log(x)
+                    logger.log("----")
+                lst = extract_list_from_end(x)
+
+                constraints = lst
+                break
+            except:
+                k -= 1
+                if k == 0:
+                    raise Exception("Failed to extract constraints")
+
+        if logger:
+            logger.log("+++++++++++++++++++")
+            logger.log("++ Constraint Qs ++")
+            logger.log("+++++++++++++++++++")
+        for q in qs:
+            for c in constraints.copy():
+                k = 5
+                while k > 0:
+                    p = prompt_constraints_q.format(
+                        description=desc,
+                        params=json.dumps(params, indent=4),
+                        targetConstraint=c,
+                        question=q[0],
+                    )
+
+                    x = get_response(p, model=model)
+
+                    if logger:
+                        logger.log("+--+")
+                        logger.log(p)
+                        logger.log("----")
+                        logger.log(x)
+                        logger.log("+--+")
+                    valid, res = q[1](desc, x, params, {}, constraints, c, logger, model)
+                    if valid:
+                        constraints = res
+                        break
+                    else:
+                        k -= 1
+
+    constraints = [{"description": c, "formulation": None, "code": None} for c in constraints]
+    return constraints
diff --git a/llma/constraint_model.py b/llma/constraint_model.py
new file mode 100644
index 0000000..ba18f82
--- /dev/null
+++ b/llma/constraint_model.py
@@ -0,0 +1,360 @@
+import json
+import pandas as pd
+from rag.query_vector_db import RAGFormat, get_rag_from_constraint, get_rag_from_problem_categories, \
+    get_rag_from_problem_description
+from rag.rag_utils import RAGMode, constraint_path
+from utils import get_response, extract_json_from_end, shape_string_to_list
+
+
+def extract_formulation_from_end(text):
+    iloop = 0
+    iloop_max = 1e05
+
+    if "$" not in text:
+        raise Exception("No formulation found")
+
+    ind_1 = text.find('"FORMULATION":')
+    while text[ind_1] != "$":
+        ind_1 += 1
+        iloop += 1
+        if iloop > iloop_max:
+            raise Exception("No formulation found")
+
+    ind_2 = text.find('"NEW VARIABLES":')
+    while text[ind_2] != "$":
+        ind_2 -= 1
+        iloop += 1
+        if iloop > iloop_max:
+            raise Exception("No formulation found")
+
+    formulation = text[ind_1: ind_2 + 1].strip()
+
+    text = text[:ind_1] + text[ind_2 + 1:]
+
+    ind_1 = text.find('"AUXILIARY CONSTRAINTS":')
+    while text[ind_1] != "$":
+        ind_1 += 1
+        if ind_1 > len(text) - 1:
+            break
+        iloop += 1
+        if iloop > iloop_max:
+            raise Exception("No formulation found")
+
+    auxiliaries = []
+
+    if ind_1 < len(text) - 1:
+        while True:
+            ind_2 = ind_1 + 1
+
+            while ind_2 + 2 < len(text) and text[ind_2: ind_2 + 2] != '$"':
+                ind_2 += 1
+                iloop += 1
+                if iloop > iloop_max:
+                    break
+
+            auxiliaries.append(text[ind_1: ind_2 + 1].strip())
+            text = text[:ind_1] + text[ind_2 + 1:]
+
+            while ind_1 < len(text) - 1 and text[ind_1] != "$":
+                ind_1 += 1
+                if ind_1 > len(text) - 1:
+                    break
+                iloop += 1
+                if iloop > iloop_max:
+                    break
+
+            if ind_1 > len(text) - 1:
+                break
+    # print("text:", text)
+    json_res = extract_json_from_end(text)
+    # print("json_res", json_res)
+    auxiliaries = [a for a in auxiliaries if len(a) > 5]
+    json_res["FORMULATION"] = formulation
+    json_res["AUXILIARY CONSTRAINTS"] = auxiliaries
+
+    return (
+        json_res["FORMULATION"],
+        json_res["NEW VARIABLES"],
+        json_res["AUXILIARY CONSTRAINTS"],
+    )
+
+
+prompt_constraints_model = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+{rag}-----
+{description}
+-----
+
+And here's a list of parameters that we have extracted from the description:
+
+{params}
+
+And here's a list of all variables that we have defined so far to model the problem as an (MI)LP:
+
+{vars}
+
+Your task is to model the following constraint mathematically in LaTeX for the MILP formulation:
+
+{constraint}
+
+The constraints are the conditions that must be satisfied by the variables. Please generate the output in the following json format:
+
+{{
+    "FORMULATION": constraint formulation in LaTeX, between $...$,
+    "NEW VARIABLES": {{
+        symbol: {{    
+            "shape": shape of the new variable (e.g. [], [N], [N, M]),
+            "type": type of the new variable (e.g. binary, integer, continuous),
+            "definition": definition of the new variable in natural language
+        }},
+        ...
+    }},
+    "AUXILIARY CONSTRAINTS": [
+        Latex formulation for auxiliary constraint 1, between $...$,
+        Latex formulation for auxiliary constraint 2, between $...$,
+        ...
+    ]
+}}
+    
+Here's an example output (where SalesVolumePerStore is already defined as a variable in the vars list):
+{{
+    "FORMULATION": "$\\forall i, SalesVolumes[i] \leq MaxProductionVolumes[i]$",
+    "NEW VARIABLES": {{
+        "SalesVolumes": {{
+            "shape": "[NumberOfArticles]",
+            "type": "continuous",
+            "definition": "The sales volume for each article of clothing"
+        }}
+    }},
+    "AUXILIARY CONSTRAINTS": [
+        "$\\forall i, SalesVolumes[i] = \\sum_j SalesVolumesPerStore[i, j]$"
+    ]
+}}
+
+- If you need any new variables, you can define them in the NEW VARIABLES list. Use {{}} for "NEW VARIABLES" if no new variables are needed.
+- Use [] for AUXILIARY CONSTRAINTS list if no auxiliary constraints are needed.
+- You can only use symbols of existing parameters and integer numbers for dimensions of new variables.
+- Use camelCase for variable symbols (e.g. SalesVolumes). Do not use LaTeX formatting (e.g. X_{{color}}), indices (e.g. SalesVolume_{{i}}), and underlines (_) for variable symbols.
+- Do not generate anything after the json file!
+
+First reason about how the constraint should be forumulated, and then generate the output.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+prompt_constraints_q = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Here is a list of parameters that someone has extracted from the description:
+
+{params}
+
+And here is a list of variables defined:
+
+{vars}
+
+Consider this constraint:
+
+{targetConstraint}
+
+{question}
+
+Take a deep breath and think step by step.
+"""
+
+
+def logic_check(text, params, vars, constraints, c):
+    try:
+        json = extract_json_from_end(text)
+        if json["action"] == "REMOVE":
+            constraints.remove(c)
+            return True, constraints
+        elif json["action"] == "MODIFY":
+            constraints.remove(c)
+            constraints.append(json["updatedConstraint"])
+            return True, constraints
+        elif json["action"] == "KEEP":
+            return True, constraints
+        else:
+            return False, None
+    except:
+        return False, None
+
+
+def extract_score_constraint_model(text, params, vars, constraints, c):
+    match = re.search(r"\d out of 5", text.lower())
+    if match:
+        score = int(match.group()[0])
+        if score > 3:
+            return True, constraints
+        else:
+            inp = input("LLMs reasoning: {}\n"
+                        "------ Do you want to keep this constraint (y/n/modify)?: \n "
+                        "{} \n------ ".format(text, c))
+            if inp.lower().startswith("y"):
+                return True, constraints
+            elif inp.lower().startswith("n"):
+                constraints.remove(c)
+                return True, constraints
+            elif inp.lower().startswith("m"):
+                new_constraint = input("Enter the modified formulation: ")
+                constraints.remove(c)
+                constraints.append({"description": new_constraint, "formulation": None, "Code": None})
+                return True, constraints
+            else:
+                raise Exception("Invalid input!")
+    else:
+        return False, None
+
+
+qs = [
+    (
+        """
+    - Does this constraint logically make sense? How confident are you that this needs to be explicitly modeled in the optimization formulation (from 1 to 5)? 
+    - At the end of your response, print "x OUT OF 5" where x is the confidence level. Do not generate anything after that. 
+    """,
+        # extract_score_constraint_model,
+        # dummy function
+        lambda x, params, vars, constraints, c: (False, constraints),
+    ),
+    (
+        """
+- What are the units for each side of the constraint? Are they consistent with each other?
+- At the end of your response, generate a json file with this format:
+    {{
+        "action": "KEEP" if the units match, or "MODIFY" if the units do not match,
+        "updatedConstraint": The latex code for updated constraint if the action is "MODIFY", otherwise null
+    }}
+- Do not generate anything after the json file.
+""",
+        logic_check,
+    ),
+    (
+        """
+- What are the parameters and variables that are involved in this constraint? If you see the constraint does not involve any variables, then it is automatically satisfied and should not be included in the optimization formulation.
+- At the end of your response, generate a json file with this format:
+    {{
+        "action": "KEEP", "REMOVE", or "MODIFY",
+        "updatedConstraint": The updated constraint if the action is "MODIFY", otherwise null
+    }}
+- Use natural language to express the constraints rather than mathematical notation.
+- Do not generate anything after the json file.
+""",
+        logic_check,
+    ),
+]
+
+
+def get_constraint_formulations(desc, params, constraints, model, check=False, logger=None,
+                                rag_mode: RAGMode | None = None, labels: dict | None = None):
+    if isinstance(rag_mode, RAGMode):
+        match rag_mode:
+            case RAGMode.PROBLEM_DESCRIPTION:
+                rag = get_rag_from_problem_description(desc, RAGFormat.CONSTRAINT_FORMULATION, top_k=5)
+            case RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                rag = ""
+            case RAGMode.PROBLEM_LABELS:
+                assert labels is not None
+                rag = get_rag_from_problem_categories(desc, labels, RAGFormat.CONSTRAINT_FORMULATION, top_k=5)
+        rag = f"-----\n{rag}-----\n\n"
+    else:
+        rag = ""
+
+    if logger:
+        logger.log("\n\n\n++++++++++++++++++++++++++++++")
+        logger.log("Extracting constraint formulations")
+        logger.log("++++++++++++++++++++++++++++++\n\n\n")
+
+    vars = {}
+    formulated_constraints = []
+    for c in constraints.copy():
+        k = 1
+        while k > 0:
+            try:
+                if rag_mode == RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                    constraint_df = pd.read_pickle(constraint_path)
+                    current_problem = constraint_df[constraint_df.description == desc]
+                    if not current_problem.empty:
+                        problem_name = current_problem.iloc[0].problem_name
+                    else:
+                        problem_name = None
+                    rag = get_rag_from_constraint(c["description"], RAGFormat.CONSTRAINT_FORMULATION, top_k=10,
+                                                  current_problem_name=problem_name)
+                    rag = f"-----\n{rag}-----\n\n"
+                res = get_response(
+                    prompt_constraints_model.format(
+                        description=desc,
+                        params=json.dumps(params, indent=4),
+                        vars=json.dumps(vars, indent=4),
+                        constraint=c,
+                        rag=rag,
+                    ),
+                    model=model,
+                )
+
+                if logger:
+                    logger.log("----")
+                    logger.log(res)
+                    logger.log("----")
+
+                formulation, new_variables, aux_constraints = extract_formulation_from_end(res)
+
+                if logger:
+                    logger.log("----")
+                    logger.log("EXTRACTED ITEMS")
+                    logger.log(str(formulation))
+                    logger.log(str(new_variables))
+                    logger.log(str(aux_constraints))
+                    logger.log("----")
+
+                tmp_vars = vars.copy()
+                for v in new_variables:
+                    if v in tmp_vars:
+                        raise Exception(f"Variable {v} already exists")
+                    print(v, new_variables[v])
+                    new_variables[v]["shape"] = shape_string_to_list(new_variables[v]["shape"])
+                    tmp_vars[v] = new_variables[v]
+
+                c["formulation"] = formulation
+                formulated_constraints.append(c)
+
+                for aux_c in aux_constraints:
+                    formulated_constraints.append({"description": "auxiliary constraint", "formulation": aux_c})
+
+                vars = tmp_vars
+                break
+            except Exception as e:
+                k -= 1
+                if k == 0:
+                    raise e
+
+    constraints = formulated_constraints
+
+    if check:
+        for c in formulated_constraints.copy():
+            for q in qs[0:1]:
+                k = 1
+                while k > 0:
+                    p = prompt_constraints_q.format(
+                        description=desc,
+                        params=json.dumps(params, indent=4),
+                        vars=json.dumps(vars, indent=4),
+                        targetConstraint=json.dumps(c, indent=4),
+                        question=q[0],
+                    )
+
+                    x = get_response(p, model=model)
+
+                    valid, res = q[1](x, params, vars, constraints, c)
+                    print(valid)
+                    if valid:
+                        constraints = res
+                        break
+                    else:
+                        k -= 1
+
+    return formulated_constraints, vars
diff --git a/llma/data/adv_code.py b/llma/data/adv_code.py
new file mode 100644
index 0000000..b1b549a
--- /dev/null
+++ b/llma/data/adv_code.py
@@ -0,0 +1,112 @@
+import json
+import pandas as pd
+from gurobipy import Model, GRB, quicksum
+
+# 读取数据
+data = pd.read_csv("data.csv")
+price = data['price']
+load = data['load']
+pv = data['pv']
+wind = data['wind']
+
+# 初始化模型
+m = Model("OptimizationProblem")
+period = len(data)
+
+# 决策变量
+Ag1 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag1")
+Ag2 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag2")
+Ag3 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag3")
+Apv = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="apv")
+Asoc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="asoc")
+
+# 定义变量相关的约束
+soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name="soc")
+pg1 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=150, name="pg1")
+pg2 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=375, name="pg2")
+pg3 = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=500, name="pg3")
+pv_power = m.addVars(period, vtype=GRB.CONTINUOUS, name="pv_power")
+
+# 设置交换约束
+grid_energy_export = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='export')
+grid_energy_import = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='import')
+excess_energy = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, name='excess')
+shortage_energy = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, name='shortage')
+
+# 初始值约束
+m.addConstr(soc[0] == 0.2, name="soc_init")
+m.addConstr(pg1[0] == 0, name="pg1_init")
+m.addConstr(pg2[0] == 0, name="pg2_init")
+m.addConstr(pg3[0] == 0, name="pg3_init")
+
+# 添加soc和pg的递推约束
+m.addConstrs((soc[t] == soc[t - 1] + 0.18 * Asoc[t] for t in range(1, period)), name="soc_update")
+m.addConstrs((pg1[t] == pg1[t - 1] + 100 * Ag1[t] for t in range(1, period)), name="pg1_update")
+m.addConstrs((pg2[t] == pg2[t - 1] + 100 * Ag2[t] for t in range(1, period)), name="pg2_update")
+m.addConstrs((pg3[t] == pg3[t - 1] + 200 * Ag3[t] for t in range(1, period)), name="pg3_update")
+
+# 光伏发电功率
+m.addConstrs((pv_power[t] == pv[t] * (1 + Apv[t]) for t in range(period)), name="pv_power_update")
+
+# 定义供需平衡约束
+m.addConstrs((pg1[t] + pg2[t] + pg3[t] + pv_power[t] + wind[t] + grid_energy_import[t] ==
+              load[t] + 0.18 * Asoc[t] + grid_energy_export[t] + excess_energy[t] - shortage_energy[t] for t in range(period)),
+             name="power_balance")
+m.addConstrs((excess_energy[t] == pg1[t] + pg2[t] + pg3[t] + pv_power[t] + wind[t] + grid_energy_import[t]
+              - load[t] - 0.18 * Asoc[t] - grid_energy_export[t] for t in range(1, period)),
+             name="excess_energy")
+m.addConstrs((shortage_energy[t] == load[t] + 0.18 * Asoc[t] + grid_energy_export[t]
+              - pg1[t] - pg2[t] - pg3[t] - pv_power[t] - wind[t] - grid_energy_import[t] for t in range(1, period)),
+             name="shortage_energy")
+
+# 定义成本函数
+battery_cost = quicksum((Asoc[t] + 0.1 * soc[t]) for t in range(1, period))
+gen1_cost = quicksum((0.0034 * pg1[t] ** 2 + 3 * pg1[t] + 30) for t in range(1, period))
+gen2_cost = quicksum((0.001 * pg2[t] ** 2 + 10 * pg2[t] + 40) for t in range(1, period))
+gen3_cost = quicksum((0.001 * pg3[t] ** 2 + 15 * pg3[t] + 70) for t in range(1, period))
+renewable_cost = quicksum((0.01 * pv_power[t] + 0.01 * wind[t]) for t in range(period))
+
+# 计算惩罚和收益
+excess_penalty = quicksum(excess_energy[t] * 50 for t in range(period))
+shortage_penalty = quicksum(shortage_energy[t] * 50 for t in range(period))
+sales_revenue = quicksum(0.5 * price[t] * grid_energy_export[t] for t in range(period))
+purchase_cost = quicksum(price[t] * grid_energy_import[t] for t in range(period))
+
+# 设置目标函数
+objective = (battery_cost + gen1_cost + gen2_cost + gen3_cost + renewable_cost
+             + excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
+
+m.setObjective(objective, GRB.MINIMIZE)
+m.optimize()
+# m.computeIIS()
+# m.write("file.ilp")
+
+if m.status == GRB.OPTIMAL:
+    results = []
+    for t in range(period):
+        gen1_cost = 0.0034 * pg1[t].x ** 2 + 3 * pg1[t].x + 30
+        gen2_cost = 0.001 * pg2[t].x ** 2 + 10 * pg2[t].x + 40
+        gen3_cost = 0.001 * pg3[t].x ** 2 + 15 * pg3[t].x + 70
+        battery_cost = Asoc[t].x + 0.1 * soc[t].x
+        renewable_cost = 0.01 * pv_power[t].x + 0.1 * wind[t]
+        excess_penalty = excess_energy[t].x * 50
+        shortage_penalty = shortage_energy[t].x * 50
+        sales_revenue = 0.5 * price[t] * grid_energy_export[t].x
+        purchase_cost = price[t] * grid_energy_import[t].x
+
+        current_obj = (gen1_cost + gen2_cost + gen3_cost + battery_cost + renewable_cost +
+                       excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
+
+        results.append({
+            "time": t,
+            "asoc": Asoc[t].x,
+            "ag1": Ag1[t].x,
+            "ag2": Ag2[t].x,
+            "ag3": Ag3[t].x,
+            "apv": Apv[t].x,
+            "reward": current_obj,
+            "unbanlance": excess_energy[t].x + shortage_energy[t].x
+        })
+
+    with open("../../DRL-for-Energy-Systems/data/results_day.json", "w") as f:
+        json.dump(results, f, indent=4)
diff --git a/llma/data/adv_code_2.py b/llma/data/adv_code_2.py
new file mode 100644
index 0000000..fcab401
--- /dev/null
+++ b/llma/data/adv_code_2.py
@@ -0,0 +1,130 @@
+import pandas as pd
+from gurobipy import Model, GRB, quicksum
+import json
+
+# 读取数据
+data = pd.read_csv("data.csv")
+price = data['price']
+load = data['load']
+pv = data['pv']
+wind = data['wind']
+
+# 总数据量和周期数
+total_period = len(data)
+days = total_period // 24
+
+# 存储所有结果
+all_results = []
+
+for day in range(days):
+    # 获取每天的24条数据
+    start_idx = day * 24
+    end_idx = (day + 1) * 24
+    daily_price = price[start_idx:end_idx]
+    daily_load = load[start_idx:end_idx]
+    daily_pv = pv[start_idx:end_idx]
+    daily_wind = wind[start_idx:end_idx]
+
+    # 初始化模型
+    m = Model("OptimizationProblem_Day{}".format(day))
+
+    # 决策变量
+    Ag1 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag1")
+    Ag2 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag2")
+    Ag3 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag3")
+    Apv = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="apv")
+    Asoc = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="asoc")
+
+    # 定义变量相关的约束
+    soc = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name="soc")
+    pg1 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=150, name="pg1")
+    pg2 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=375, name="pg2")
+    pg3 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=500, name="pg3")
+    pv_power = m.addVars(24, vtype=GRB.CONTINUOUS, name="pv_power")
+
+    # 设置交换约束
+    grid_energy_export = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='export')
+    grid_energy_import = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='import')
+    excess_energy = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, name='excess')
+    shortage_energy = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, name='shortage')
+
+    # 初始值约束
+    m.addConstr(soc[0] == 0.2, name="soc_init")
+    m.addConstr(pg1[0] == 0, name="pg1_init")
+    m.addConstr(pg2[0] == 0, name="pg2_init")
+    m.addConstr(pg3[0] == 0, name="pg3_init")
+
+    # 添加soc和pg的递推约束
+    m.addConstrs((soc[t] == soc[t - 1] + 0.18 * Asoc[t] for t in range(1, 24)), name="soc_update")
+    m.addConstrs((pg1[t] == pg1[t - 1] + 100 * Ag1[t] for t in range(1, 24)), name="pg1_update")
+    m.addConstrs((pg2[t] == pg2[t - 1] + 100 * Ag2[t] for t in range(1, 24)), name="pg2_update")
+    m.addConstrs((pg3[t] == pg3[t - 1] + 200 * Ag3[t] for t in range(1, 24)), name="pg3_update")
+
+    # 光伏发电功率
+    m.addConstrs((pv_power[t] == daily_pv.iloc[t] * (1 + Apv[t]) for t in range(24)), name="pv_power_update")
+
+    # 供需平衡约束
+    m.addConstrs((pg1[t] + pg2[t] + pg3[t] + pv_power[t] + daily_wind.iloc[t] + grid_energy_import[t] ==
+                  daily_load.iloc[t] + 0.18 * Asoc[t] + grid_energy_export[t] + excess_energy[t] - shortage_energy[t]
+                  for t in range(24)), name="power_balance")
+
+    # excess_energy 和 shortage_energy 约束
+    m.addConstrs(
+        (excess_energy[t] == pg1[t] + pg2[t] + pg3[t] + pv_power[t] + daily_wind.iloc[t] + grid_energy_import[t]
+         - daily_load.iloc[t] - 0.18 * Asoc[t] - grid_energy_export[t] for t in range(1, 24)),
+        name="excess_energy")
+    m.addConstrs((shortage_energy[t] == daily_load.iloc[t] + 0.18 * Asoc[t] + grid_energy_export[t]
+                  - pg1[t] - pg2[t] - pg3[t] - pv_power[t] - daily_wind.iloc[t] - grid_energy_import[t]
+                  for t in range(1, 24)), name="shortage_energy")
+
+    # 成本函数部分
+    battery_cost = quicksum((Asoc[t] + 0.1 * soc[t]) for t in range(1, 24))
+    gen1_cost = quicksum((0.0034 * pg1[t] ** 2 + 3 * pg1[t] + 30) for t in range(1, 24))
+    gen2_cost = quicksum((0.001 * pg2[t] ** 2 + 10 * pg2[t] + 40) for t in range(1, 24))
+    gen3_cost = quicksum((0.001 * pg3[t] ** 2 + 15 * pg3[t] + 70) for t in range(1, 24))
+    renewable_cost = quicksum((0.01 * pv_power[t] + 0.01 * daily_wind.iloc[t]) for t in range(24))
+
+    # 计算惩罚和收益
+    excess_penalty = quicksum(excess_energy[t] * 50 for t in range(24))
+    shortage_penalty = quicksum(shortage_energy[t] * 50 for t in range(24))
+    sales_revenue = quicksum(0.5 * daily_price.iloc[t] * grid_energy_export[t] for t in range(24))
+    purchase_cost = quicksum(daily_price.iloc[t] * grid_energy_import[t] for t in range(24))
+
+    # 设置目标函数
+    objective = (battery_cost + gen1_cost + gen2_cost + gen3_cost + renewable_cost +
+                 excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
+    m.setObjective(objective, GRB.MINIMIZE)
+
+    # 求解
+    m.optimize()
+
+    if m.status == GRB.OPTIMAL:
+        day_results = []
+        for t in range(24):
+            gen1_cost = 0.0034 * pg1[t].x ** 2 + 3 * pg1[t].x + 30
+            gen2_cost = 0.001 * pg2[t].x ** 2 + 10 * pg2[t].x + 40
+            gen3_cost = 0.001 * pg3[t].x ** 2 + 15 * pg3[t].x + 70
+            battery_cost = Asoc[t].x + 0.1 * soc[t].x
+            renewable_cost = 0.01 * pv_power[t].x + 0.01 * daily_wind.iloc[t]
+            excess_penalty = excess_energy[t].x * 50
+            shortage_penalty = shortage_energy[t].x * 50
+            sales_revenue = 0.5 * daily_price.iloc[t] * grid_energy_export[t].x
+            purchase_cost = daily_price.iloc[t] * grid_energy_import[t].x
+
+            current_obj = (gen1_cost + gen2_cost + gen3_cost + battery_cost + renewable_cost +
+                           excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
+
+            day_results.append([
+                Asoc[t].x,
+                Ag1[t].x,
+                Ag2[t].x,
+                Ag3[t].x,
+                Apv[t].x,
+            ])
+
+        # 保存当天的结果
+        all_results.extend(day_results)
+
+# 最后将所有结果保存到一个JSON文件
+with open("./results_day.json", "w") as f:
+    json.dump(all_results, f, indent=4)
diff --git a/llma/data/code.py b/llma/data/code.py
new file mode 100644
index 0000000..22dc143
--- /dev/null
+++ b/llma/data/code.py
@@ -0,0 +1,184 @@
+import json
+from gurobipy import Model, GRB, quicksum
+
+model = Model("OptimizationProblem")
+
+with open("data.json", "r") as f:
+    data = json.load(f)
+
+# Define the parameters
+TotalBatteryCapacity = data["TotalBatteryCapacity"]  # shape: [], definition: The total capacity of the battery
+InitialSoc = data["InitialSoc"]  # shape: [], definition: Initial state of charge of the battery
+InitialPg1 = data["InitialPg1"]  # shape: [], definition: Initial power of generator 1
+InitialPg2 = data["InitialPg2"]  # shape: [], definition: Initial power of generator 2
+InitialPg3 = data["InitialPg3"]  # shape: [], definition: Initial power of generator 3
+
+Price = data["Price"]  # shape: [24], definition: Electricity price at each time step
+Load = data["Load"]  # shape: [24], definition: Residential load at each time step
+Pv = data["Pv"]  # shape: [24], definition: Photovoltaic power generation at each time step
+Wind = data["Wind"]  # shape: [24], definition: Wind power generation at each time step
+
+# Define the variables
+
+Ag1 = model.addVars(24, vtype=GRB.CONTINUOUS, name="Ag1")
+Ag2 = model.addVars(24, vtype=GRB.CONTINUOUS, name="Ag2")
+Ag3 = model.addVars(24, vtype=GRB.CONTINUOUS, name="Ag3")
+PvPower = model.addVars(24, vtype=GRB.CONTINUOUS, name="PvPower")
+Apv = model.addVars(24, vtype=GRB.CONTINUOUS, name="Apv")
+Asoc = model.addVars(24, vtype=GRB.CONTINUOUS, name="Asoc")
+
+ExcessEnergy = model.addVars(24, vtype=GRB.CONTINUOUS, name="ExcessEnergy")
+ShortageEnergy = model.addVars(24, vtype=GRB.CONTINUOUS, name="ShortageEnergy")
+
+TotalPowerSupply = model.addVars(24, vtype=GRB.CONTINUOUS, name="TotalPowerSupply")
+GridTransactionLimit = model.addVar(vtype=GRB.FLOAT, name="GridTransactionLimit")
+
+# Define the constraints
+
+# Loop over each time step to add the soc constraints
+for t in range(24):
+    # Calculate soc_t based on the previous soc and the adjustment factor
+    soc_t = 0.18 * Asoc[t] + InitialSoc
+
+    # Add the lower bound constraint for soc_t
+    model.addConstr(soc_t >= 0.2)
+    # Add the upper bound constraint for soc_t
+    model.addConstr(soc_t <= 0.8)
+
+    # Update soc to the newly calculated soc_t for the next iteration
+    soc = soc_t
+
+# Assuming decision variables Pg1[t] have been defined for each time step t
+for t in range(24):
+    if t == 0:
+        # For the first time step, use the initial value of Pg1
+        model.addConstr(Pg1[t] <= 150)
+        model.addConstr(Pg1[t] >= 10)
+    else:
+        # For subsequent time steps, calculate Pg1 based on the previous time step and Ag1
+        model.addConstr(Pg1[t] <= 150)
+        model.addConstr(Pg1[t] >= 10)
+        model.addConstr(Pg1[t] == 100 * Ag1[t] + Pg1[t - 1])
+for t in range(1, 24):
+    model.addConstr(InitialPg2 + 100 * Ag2[t] >= 50)
+    model.addConstr(InitialPg2 + 100 * Ag2[t] <= 375)
+for t in range(24):
+    model.addConstr(Pg3[t] >= 100)  # Lower bound constraint for each time step
+    model.addConstr(Pg3[t] <= 500)  # Upper bound constraint for each time step
+# Assuming soc is defined as a list of decision variables for each time step
+soc = model.addVars(24, lb=0.2, ub=0.8, vtype=GRB.CONTINUOUS, name="soc")
+
+# Loop over each time step starting from 1 up to 24
+for t in range(1, 24):
+    # Add the constraint for the state of charge calculation
+    model.addConstr(soc[t] == 0.18 * Asoc[t] + soc[t - 1])
+for t in range(1, 24):
+    model.addConstr(pg1[t] == 100 * Ag1[t] + pg1[t - 1])
+# Assuming that `pg2` is a list of Gurobi variables representing the power of Generator 2 at each time step
+# and that `InitialPg2` is a parameter representing the initial power of Generator 2 at time step 0
+
+# Define the Generator 2 power for the first time step, which is the initial value and does not have a constraint
+pg2 = [model.addVar(vtype=GRB.CONTINUOUS, lb=50, ub=375, name="pg2_0")]
+
+# Now add the constraints for time steps 1 to 23
+for t in range(1, 24):
+    pg2.append(model.addVar(vtype=GRB.CONTINUOUS, lb=50, ub=375, name=f"pg2_{t}"))
+    model.addConstr(pg2[t] == 100 * Ag2[t - 1] + pg2[t - 1])
+# Assuming Pg3 is defined as a list of decision variables representing power of generator 3 at each time step
+for t in range(1, 24):
+    model.addConstr(Pg3[t] == 200 * Ag3[t] + Pg3[t - 1])
+for t in range(24):
+    model.addConstr(PvPower[t] == Pv[t] * (1 + Apv[t]))
+for t in range(24):
+    model.addConstr(ExcessEnergy[t] >= 0)
+for t in range(24):
+    # Define the total power supply at time step t
+    TotalSupply_t = (InitialPg1 + Ag1[t] * 100 +
+                     InitialPg2 + Ag2[t] * 100 +
+                     InitialPg3 + Ag3[t] * 200 +
+                     Pv[t] * (1 + Apv[t]) +
+                     Wind[t] +
+                     InitialSoc + Asoc[t] * 0.18)
+    # Add the constraint for excess energy at time step t
+    model.addConstr(ExcessEnergy[t] == TotalSupply_t - Load[t])
+for t in range(24):
+    # Calculate updated generator powers and battery state of charge
+    soc_t = InitialSoc + 0.18 * Asoc[t] if t == 0 else soc[t - 1] + 0.18 * Asoc[t]
+    pg1_t = InitialPg1 + 100 * Ag1[t] if t == 0 else pg1[t - 1] + 100 * Ag1[t]
+    pg2_t = InitialPg2 + 100 * Ag2[t] if t == 0 else pg2[t - 1] + 100 * Ag2[t]
+    pg3_t = InitialPg3 + 200 * Ag3[t] if t == 0 else pg3[t - 1] + 200 * Ag3[t]
+
+    # Calculate photovoltaic power
+    PvPower_t = Pv[t] * (1 + Apv[t])
+
+    # Add the constraint for total power supply at time t
+    model.addConstr(TotalPowerSupply[t] == soc_t + pg1_t + pg2_t + pg3_t + PvPower_t + Wind[t])
+for t in range(24):
+    model.addConstr(ShortageEnergy[t] >= 0)
+for t in range(24):
+    model.addConstr(-1 <= Apv[t])
+    model.addConstr(Apv[t] <= 1)
+    model.addConstr(-1 <= Asoc[t])
+    model.addConstr(Asoc[t] <= 1)
+    model.addConstr(-1 <= Ag1[t])
+    model.addConstr(Ag1[t] <= 1)
+    model.addConstr(-1 <= Ag2[t])
+    model.addConstr(Ag2[t] <= 1)
+    model.addConstr(-1 <= Ag3[t])
+    model.addConstr(Ag3[t] <= 1)
+for t in range(1, 24):
+    model.addConstr(100 * Asoc[t] == soc[t] - soc[t - 1])
+for t in range(24):
+    # Calculate the power from each generator based on previous state and adjustment
+    pg1_power = InitialPg1 + 100 * Ag1[t]
+    pg2_power = InitialPg2 + 100 * Ag2[t]
+    pg3_power = InitialPg3 + 200 * Ag3[t]
+
+    # Calculate the adjusted photovoltaic power
+    pv_power = Pv[t] * (1 + Apv[t])
+
+    # Express the total power supply
+    TotalPowerSupply[t] = pg1_power + pg2_power + pg3_power + pv_power + Wind[t]
+
+    # Add the constraint that total power supply cannot exceed the load by more than the grid transaction limit
+    model.addConstr(TotalPowerSupply[t] <= Load[t] + GridTransactionLimit)
+for t in range(24):
+    # Assuming that Pg1, Pg2, Pg3 are defined as variables in the model with the correct initial values and adjustments
+    Pg1[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f"Pg1_{t}")
+    Pg2[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f"Pg2_{t}")
+    Pg3[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f"Pg3_{t}")
+
+    # Define the auxiliary constraint for each time step
+    model.addConstr(TotalPowerSupply[t] == Pg1[t] + Pg2[t] + Pg3[t] + PvPower[t] + Wind[t])
+
+# Define the objective
+model.setObjective(
+    quicksum(
+        (Asoc[t] + 0.1 * soc[t - 1] +
+         0.0034 * pg1[t - 1] ** 2 + 3 * pg1[t - 1] + 30 +
+         0.001 * pg2[t - 1] ** 2 + 10 * pg2[t - 1] + 40 +
+         0.001 * pg3[t - 1] ** 2 + 15 * pg3[t - 1] + 70 +
+         0.01 * (pv[t - 1] + wind[t - 1]) +
+         max(0, (ExcessEnergy[t] - 100) * 50) +
+         max(0, (ShortageEnergy[t] - 100) * 50) -
+         0.5 * Price[t - 1] * ExcessEnergy[t] +
+         Price[t - 1] * ShortageEnergy[t] +
+         abs(ExcessEnergy[t]) + abs(ShortageEnergy[t])
+         ) for t in range(1, 25)
+    ),
+    GRB.MINIMIZE
+)
+
+# Optimize the model
+model.optimize()
+
+# Output optimal objective value
+print("Optimal Objective Value: ", model.objVal)
+
+if model.status == GRB.OPTIMAL:
+    with open("output_solution.txt", "w") as f:
+        f.write(str(model.objVal))
+    print("Optimal Objective Value: ", model.objVal)
+else:
+    with open("output_solution.txt", "w") as f:
+        f.write(model.status)
diff --git a/llma/data/data.csv b/llma/data/data.csv
new file mode 100644
index 0000000..50b7458
--- /dev/null
+++ b/llma/data/data.csv
@@ -0,0 +1,8761 @@
+date_time,price,load,pv,wind,irradiance,temperature,wind_speed
+2016/1/1 0:00,2.239,269.0525945,0,88.2,0,-3.2,4.3
+2016/1/1 1:00,2.059,243.2130742,0,0,0,-3.5,4.5
+2016/1/1 2:00,1.681,234.1123382,0,0,0,-3.6,4.7
+2016/1/1 3:00,1.741,232.6271781,0,0,0,-3.4,4.9
+2016/1/1 4:00,1.702,233.0417238,0,88.2,0,-2.9,4.5
+2016/1/1 5:00,1.586,245.2036212,0,88.2,0,-2.5,4
+2016/1/1 6:00,1.816,298.1636567,0,86.23034654,95,-2.7,4.1
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+2016/1/1 8:00,1.977,358.2133437,3.242864665,28.19425369,359,-3.3,4.9
+2016/1/1 9:00,2.375,314.073438,38.17360894,27.27661159,42,-4.1,5.1
+2016/1/1 10:00,2.603,322.8667176,69.77865485,20.26687852,149,-4.2,5.1
+2016/1/1 11:00,2.706,307.7048847,92.12111711,35.55994387,292,-4.1,5.4
+2016/1/1 12:00,2.659,295.2915574,101.0942616,52.2764928,102,-4.2,5.8
+2016/1/1 13:00,2.5,284.9981584,103.8121262,43.07450273,128,-4.3,6.1
+2016/1/1 14:00,2.443,282.4243391,88.12118553,42.66557033,38,-4.1,6
+2016/1/1 15:00,2.887,298.4115103,66.56421073,45.37041092,0,-3.5,5.5
+2016/1/1 16:00,3.744,360.9832044,27.08369716,53.92720937,0,-3.2,4.9
+2016/1/1 17:00,3.741,486.4398933,0.13674245,66.19144418,0,-2.7,4.5
+2016/1/1 18:00,3.534,550.3139862,0,66.73923268,0,-2,4
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+2016/1/1 23:00,2.22,333.680157,0,50.4316134,0,0.3,2.6
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diff --git a/llma/data/data.json b/llma/data/data.json
new file mode 100644
index 0000000..ee4563d
--- /dev/null
+++ b/llma/data/data.json
@@ -0,0 +1,14 @@
+{
+    "Price": [2.239, 2.059, 1.681, 1.741, 1.702, 1.586, 1.816, 1.773, 1.977, 2.375, 2.603, 2.706,
+        2.659, 2.5, 2.443, 2.887, 3.744, 3.741, 3.534, 3.307, 2.952, 3.01, 2.457, 2.22, 2.239],
+    "Load": [269.0525945, 243.2130742, 234.1123382, 232.6271781, 233.0417238, 245.2036212, 298.1636567, 370.1068689,
+        358.2133437, 314.073438, 322.8667176, 307.7048847, 295.2915574, 284.9981584, 282.4243391, 298.4115103,
+        360.9832044, 486.4398933, 550.3139862, 542.9828898, 509.6215404, 474.1087098, 403.6717878, 333.680157],
+    "PV": [0, 0, 0, 0, 0, 0, 0, 0, 3.2429, 38.1736, 69.7787, 92.1211, 101.0943, 103.8121, 88.1212, 66.5642, 27.0837, 0.1367, 0, 0, 0, 0, 0, 0],
+    "Wind": [88.2, 0, 0, 0, 88.2, 88.2, 86.2303, 45.1584, 28.1943, 27.2766, 20.2669, 35.5599,
+        52.2765, 43.0745, 42.6656, 45.3704, 53.9272, 66.1914, 66.7392, 79.877, 58.08, 78.0337, 39.6808, 50.4316],
+    "SOC": 0.2,
+    "PG1": 0,
+    "PG2": 0,
+    "PG3": 0
+}
diff --git a/llma/data/desc.txt b/llma/data/desc.txt
new file mode 100644
index 0000000..cbe7436
--- /dev/null
+++ b/llma/data/desc.txt
@@ -0,0 +1,28 @@
+- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.
+- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.
+- Electricity price, residential load, photovoltaic, and wind power generation are \\param{price_{}}, \\param{load_{}}, \\param{pv_{}}, and \\param{wind_{}}, each with 24 time steps representing hourly data.
+- Battery state of charge is \\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\param{pg1_{}}, \\param{pg2_{}}, and \\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.
+- \\var{Apv_{}}, \\var{Asoc_{}}, \\var{Ag1_{}}, \\var{Ag2_{}}, and \\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.
+- Battery charging and discharging result in a capacity change of 100 * \\var{Asoc_{t}}.
+- Generators 1, 2, and 3 power change by 100 * \\var{Ag1_{t}}, 100 * \\var{Ag2_{t}}, and 200 * \\var{Ag3_{t}}.
+- \\param{soc_{t}} is calculated as (0.18 * \\var{Asoc_{t}} + \\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.
+- \\param{pg1_{t}} is calculated as (100 * \\var{Ag1_{t}} + \\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.
+- \\param{pg2_{t}} is calculated as (100 * \\var{Ag2_{t}} + \\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.
+- \\param{pg3_{t}} is calculated as (200 * \\var{Ag3_{t}} + \\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.
+- Photovoltaic power is calculated as (\\param{pv_{t}} * (1 + \\var{Apv})).
+- Total power supply at each time step t = \\param{soc_{t}} + \\param{pg1_{t}} + \\param{pg2_{t}} + \\param{pg3_{t}}+ (photovoltaic power at time step t) + \\param{wind_{t}}.
+- Excess energy = total power supply - \\param{load_{t}}, and no less than 0.
+- Shortage energy = \\param{load_{t}} - total power supply, and no less than 0.
+- The total cost structure includes the following:
+- Battery cost = \\var{Asoc_{t}} + 0.1 * \\param{soc_{t}};
+- Gen1 cost = 0.0034 * \\param{pg1_{t}}^2 + 3 * \\param{pg1_{t}} + 30;
+- Gen2 cost = 0.001 * \\param{pg2_{t}}^2 + 10 * \\param{pg2_{t}} + 40;
+- Gen3 cost = 0.001 * \\param{pg3_{t}}^2 + 15 * \\param{pg3_{t}} + 70;
+- Renewable energy cost = 0.01 * (\\param{pv_{t}} + \\param{wind_{t}});
+- Sales revenue = 0.5 * \\param{price_{t}} * (excess energy);
+- Purchase cost = \\param{price_{t}} * (shortage energy);
+- Excess penalty = max(0, (excess energy - 100) * 50);
+- Shortage penalty = max(0, (shortage energy - 100) * 50).
+- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.
+- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.
+- Decisions made at each time step will update \\param{soc_{}}, \\param{pg1_{}}, \\param{pg2_{}}, and \\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.
diff --git a/llma/data/labels.json b/llma/data/labels.json
new file mode 100644
index 0000000..c6cc0aa
--- /dev/null
+++ b/llma/data/labels.json
@@ -0,0 +1,4 @@
+{
+  "types": "Mixed Integer Linear Programming",
+  "domains": "Energy Optimization"
+}
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diff --git a/llma/data/rag/problem_descriptions_vector.db/3e4136c9-6758-4959-b40d-8b395a2b2a60/link_lists.bin b/llma/data/rag/problem_descriptions_vector.db/3e4136c9-6758-4959-b40d-8b395a2b2a60/link_lists.bin
new file mode 100644
index 0000000..e69de29
diff --git a/llma/data/rag/problem_descriptions_vector.db/chroma.sqlite3 b/llma/data/rag/problem_descriptions_vector.db/chroma.sqlite3
new file mode 100644
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new file mode 100644
index 0000000..67f0453
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+        8.467645917775712e-09,
+        -0.5941090069814681,
+        -1.0325074129013956e-14,
+        -1.6653345369377348e-15,
+        0.9999999999997393
+    ],
+    [
+        0.999999999369656,
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+        -2.6756374893466273e-14,
+        -4.551914400963142e-15,
+        0.9999999999995894
+    ],
+    [
+        0.999999998983083,
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+        1.0436096431476471e-14,
+        1.7763568394002505e-15,
+        0.9999999999996829
+    ],
+    [
+        0.9999999986029711,
+        1.418642980866025e-12,
+        5.10702591327572e-15,
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+        0.9999999999996849
+    ],
+    [
+        0.33333332534826776,
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+        1.7763568394002505e-14,
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+    [
+        1.098806823307541e-09,
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+    [
+        -0.9999999996245936,
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+        0.9999999999998039,
+        1.325606291402437e-13,
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+    [
+        -0.9999999998623645,
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+    ],
+    [
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+        -1.5543122344752192e-15,
+        -0.1819470899146166,
+        -1.1102230246251565e-15,
+        -1.0
+    ],
+    [
+        -6.180561618052138e-10,
+        -2.6645352591003757e-15,
+        -0.5571991009955564,
+        -2.886579864025407e-15,
+        -1.0
+    ],
+    [
+        -2.654951702929509e-09,
+        -3.219646771412954e-15,
+        -0.5829113646436426,
+        -2.886579864025407e-15,
+        -1.0
+    ],
+    [
+        -1.0,
+        -1.0,
+        -1.0,
+        -1.0,
+        -1.0
+    ],
+    [
+        2.198854597176894e-09,
+        0.5159155720061565,
+        1.024250644527318e-13,
+        2.9199760447802115e-14,
+        -1.0
+    ],
+    [
+        0.3333332842348824,
+        -0.10832279709298198,
+        -1.2212453270876722e-15,
+        -1.1102230246251565e-16,
+        -1.0
+    ],
+    [
+        0.9999999968776416,
+        -0.012000456917230173,
+        -3.3306690738754696e-16,
+        0.0,
+        -1.0
+    ],
+    [
+        0.9999999980393179,
+        0.006438287002007881,
+        2.220446049250313e-16,
+        0.0,
+        -1.0
+    ],
+    [
+        0.999999998053307,
+        0.1502503459992255,
+        1.3322676295501878e-15,
+        2.220446049250313e-16,
+        -1.0
+    ],
+    [
+        -0.9999999753320217,
+        0.5177445820486628,
+        7.105427357601002e-15,
+        1.1102230246251565e-15,
+        -1.0
+    ],
+    [
+        -0.9999999999361979,
+        0.4299744669540455,
+        0.2689756820001665,
+        3.930189507173054e-14,
+        -1.0
+    ],
+    [
+        -0.9999999999361582,
+        -5.551115123125783e-16,
+        -0.03985703029991661,
+        0.0,
+        0.9999999999994762
+    ],
+    [
+        -0.33333330298337616,
+        -0.03823529426891448,
+        -0.2291186517002468,
+        -3.852473895449293e-14,
+        0.9999999999996732
+    ],
+    [
+        -6.158108467602119e-10,
+        -0.35750884083440415,
+        -3.9968028886505635e-15,
+        -5.551115123125783e-16,
+        0.9999999999997893
+    ],
+    [
+        2.8350730207904462e-08,
+        -0.7851566835459755,
+        -8.326672684688674e-15,
+        -1.2212453270876722e-15,
+        0.9999999999998113
+    ],
+    [
+        0.9999999997745666,
+        -0.3190991813500442,
+        -1.8207657603852567e-14,
+        -3.219646771412954e-15,
+        0.9999999999996854
+    ],
+    [
+        0.9999999997734186,
+        5.995204332975845e-14,
+        8.881784197001252e-16,
+        0.0,
+        0.9999999999996929
+    ],
+    [
+        0.9999999981641423,
+        0.2234583382962958,
+        1.6431300764452317e-14,
+        2.886579864025407e-15,
+        0.9999999999998179
+    ],
+    [
+        0.3333333053739107,
+        0.6364790132529514,
+        6.439293542825908e-15,
+        1.1102230246251565e-15,
+        0.9999999999997988
+    ],
+    [
+        1.2351479838912383e-09,
+        0.5846467206516535,
+        0.7820258899008252,
+        6.661338147750939e-16,
+        0.9999999999997198
+    ],
+    [
+        -0.9999999999209793,
+        0.05541592779843141,
+        0.999999999999893,
+        0.04175747799974139,
+        0.9999999999998241
+    ],
+    [
+        -0.9999999997912539,
+        -2.5091040356528538e-14,
+        0.9999999999995006,
+        -0.04175747799962548,
+        -1.0
+    ],
+    [
+        -0.9999999973252026,
+        -1.9761969838327786e-14,
+        -0.2628013270953824,
+        -6.994405055138486e-14,
+        -1.0
+    ],
+    [
+        -0.33333333358145634,
+        -5.551115123125783e-16,
+        -0.21373016240526188,
+        -6.661338147750939e-16,
+        -1.0
+    ],
+    [
+        -1.273242511423689e-09,
+        -1.2212453270876722e-15,
+        -0.32166146560183984,
+        -6.661338147750939e-16,
+        -1.0
+    ],
+    [
+        4.131393005479822e-10,
+        -2.4424906541753444e-15,
+        -0.7092504349969636,
+        -2.7755575615628914e-15,
+        -1.0
+    ],
+    [
+        -1.7687550313283396e-09,
+        -2.1094237467877974e-15,
+        -0.62215956630393,
+        -1.7763568394002505e-15,
+        -1.0
+    ]
+]
\ No newline at end of file
diff --git a/llma/data/state_1_params.json b/llma/data/state_1_params.json
new file mode 100644
index 0000000..ab2f6b5
--- /dev/null
+++ b/llma/data/state_1_params.json
@@ -0,0 +1,58 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    }
+}
\ No newline at end of file
diff --git a/llma/data/state_2_objective.json b/llma/data/state_2_objective.json
new file mode 100644
index 0000000..5463416
--- /dev/null
+++ b/llma/data/state_2_objective.json
@@ -0,0 +1,63 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    },
+    "objective": {
+        "description": "\"The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\"",
+        "formulation": null,
+        "code": null
+    }
+}
\ No newline at end of file
diff --git a/llma/data/state_3_constraints.json b/llma/data/state_3_constraints.json
new file mode 100644
index 0000000..b06e2c4
--- /dev/null
+++ b/llma/data/state_3_constraints.json
@@ -0,0 +1,135 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    },
+    "objective": {
+        "description": "\"The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\"",
+        "formulation": null,
+        "code": null
+    },
+    "constraints": [
+        {
+            "description": "Battery state of charge (soc_t) must be between 0.2 and 0.8",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be between 10 and 150",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be between 50 and 375",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be between 100 and 500",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Battery state of charge (soc_t) must be calculated as (0.18 * Asoc_t + soc_t-1)",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be calculated as (100 * Ag1_t + pg1_t-1)",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be calculated as (100 * Ag2_t + pg2_t-1)",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be calculated as (200 * Ag3_t + pg3_t-1)",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Photovoltaic power must be calculated as (pv_t * (1 + Apv))",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Excess energy must be no less than 0",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Shortage energy must be no less than 0",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Adjustment factors (Apv, Asoc, Ag1, Ag2, Ag3) must range from -1 to 1",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "Battery charging and discharging must result in a capacity change of 100 * Asoc_t",
+            "formulation": null,
+            "code": null
+        },
+        {
+            "description": "The sum of power from generators and renewable sources must not exceed the residential load by more than the grid transaction limit to avoid instability",
+            "formulation": null,
+            "code": null
+        }
+    ]
+}
\ No newline at end of file
diff --git a/llma/data/state_4_constraints_modeled.json b/llma/data/state_4_constraints_modeled.json
new file mode 100644
index 0000000..4d1d6e1
--- /dev/null
+++ b/llma/data/state_4_constraints_modeled.json
@@ -0,0 +1,217 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    },
+    "objective": {
+        "description": "\"The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\"",
+        "formulation": null,
+        "code": null
+    },
+    "constraints": [
+        {
+            "description": "Battery state of charge (soc_t) must be between 0.2 and 0.8",
+            "formulation": "$0.2 \\leq soc_t \\leq 0.8$",
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be between 10 and 150",
+            "formulation": "$10 \\leq pg1_t \\leq 150$",
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be between 50 and 375",
+            "formulation": "$50 \\leq pg2_t \\leq 375$",
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be between 100 and 500",
+            "formulation": "$100 \\\\leq pg3_t \\\\leq 500$",
+            "code": null
+        },
+        {
+            "description": "Battery state of charge (soc_t) must be calculated as (0.18 * Asoc_t + soc_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{soc_{t}} = 0.18 \\\\cdot \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be calculated as (100 * Ag1_t + pg1_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg1_{t}} = 100 \\\\cdot \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be calculated as (100 * Ag2_t + pg2_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg2_{t}} = 100 \\\\times Ag2_t + \\\\param{pg2_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be calculated as (200 * Ag3_t + pg3_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\ldots,24\\\\}, \\\\param{pg3_{t}} = 200 \\\\cdot \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Photovoltaic power must be calculated as (pv_t * (1 + Apv))",
+            "formulation": "$\\\\forall t, PvPower_t = Pv[t] \\\\times (1 + Apv_t)$",
+            "code": null
+        },
+        {
+            "description": "Excess energy must be no less than 0",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] \\\\geq 0$",
+            "code": null
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] = TotalSupply[t] - Load[t]$"
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalSupply[t] = soc[t] + pg1[t] + pg2[t] + pg3[t] + PvPower[t] + Wind[t]$"
+        },
+        {
+            "description": "Shortage energy must be no less than 0",
+            "formulation": "$\\\\forall t, ShortageEnergy[t] \\\\geq 0$",
+            "code": null
+        },
+        {
+            "description": "Adjustment factors (Apv, Asoc, Ag1, Ag2, Ag3) must range from -1 to 1",
+            "formulation": "$-1 \\leq Apv_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Asoc_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag1_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag2_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag3_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$",
+            "code": null
+        },
+        {
+            "description": "Battery charging and discharging must result in a capacity change of 100 * Asoc_t",
+            "formulation": "$\\\\forall t, 100 \\\\times Asoc_t = soc_{t} - soc_{t-1}$",
+            "code": null
+        },
+        {
+            "description": "The sum of power from generators and renewable sources must not exceed the residential load by more than the grid transaction limit to avoid instability",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] \\\\leq Load[t] + GridTransactionLimit$",
+            "code": null
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] = Pg1[t] + Pg2[t] + Pg3[t] + PvPower[t] + Wind[t]$"
+        }
+    ],
+    "variables": {
+        "Ag1": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 1 power at each time step"
+        },
+        "Ag2": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 2 power at each time step"
+        },
+        "Ag3": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 3 power at each time step"
+        },
+        "PvPower": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjusted photovoltaic power generation at each time step"
+        },
+        "Apv": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for photovoltaic power at each time step"
+        },
+        "ExcessEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The excess energy at each time step"
+        },
+        "ShortageEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The shortage energy at each time step"
+        },
+        "Asoc": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for the battery state of charge at each time step"
+        },
+        "TotalPowerSupply": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The total power supply from generators and renewable sources at each time step"
+        },
+        "GridTransactionLimit": {
+            "shape": [],
+            "type": "float",
+            "definition": "The maximum allowable excess power supply over the residential load to maintain grid stability"
+        }
+    }
+}
\ No newline at end of file
diff --git a/llma/data/state_5_objective_modeled.json b/llma/data/state_5_objective_modeled.json
new file mode 100644
index 0000000..970f88b
--- /dev/null
+++ b/llma/data/state_5_objective_modeled.json
@@ -0,0 +1,216 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    },
+    "objective": {
+        "description": "\"The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\"",
+        "formulation": "$\\min \\sum_{t=1}^{24} \\left( \\text{Battery cost}_t + \\text{Gen1 cost}_t + \\text{Gen2 cost}_t + \\text{Gen3 cost}_t + \\text{Renewable energy cost}_t + \\text{Excess penalty}_t + \\text{Shortage penalty}_t - \\text{Sales revenue}_t + \\text{Purchase cost}_t + |\\text{ExcessEnergy}_t| + |\\text{ShortageEnergy}_t| \\right)$"
+    },
+    "constraints": [
+        {
+            "description": "Battery state of charge (soc_t) must be between 0.2 and 0.8",
+            "formulation": "$0.2 \\leq soc_t \\leq 0.8$",
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be between 10 and 150",
+            "formulation": "$10 \\leq pg1_t \\leq 150$",
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be between 50 and 375",
+            "formulation": "$50 \\leq pg2_t \\leq 375$",
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be between 100 and 500",
+            "formulation": "$100 \\\\leq pg3_t \\\\leq 500$",
+            "code": null
+        },
+        {
+            "description": "Battery state of charge (soc_t) must be calculated as (0.18 * Asoc_t + soc_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{soc_{t}} = 0.18 \\\\cdot \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be calculated as (100 * Ag1_t + pg1_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg1_{t}} = 100 \\\\cdot \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be calculated as (100 * Ag2_t + pg2_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg2_{t}} = 100 \\\\times Ag2_t + \\\\param{pg2_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be calculated as (200 * Ag3_t + pg3_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\ldots,24\\\\}, \\\\param{pg3_{t}} = 200 \\\\cdot \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}$",
+            "code": null
+        },
+        {
+            "description": "Photovoltaic power must be calculated as (pv_t * (1 + Apv))",
+            "formulation": "$\\\\forall t, PvPower_t = Pv[t] \\\\times (1 + Apv_t)$",
+            "code": null
+        },
+        {
+            "description": "Excess energy must be no less than 0",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] \\\\geq 0$",
+            "code": null
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] = TotalSupply[t] - Load[t]$"
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalSupply[t] = soc[t] + pg1[t] + pg2[t] + pg3[t] + PvPower[t] + Wind[t]$"
+        },
+        {
+            "description": "Shortage energy must be no less than 0",
+            "formulation": "$\\\\forall t, ShortageEnergy[t] \\\\geq 0$",
+            "code": null
+        },
+        {
+            "description": "Adjustment factors (Apv, Asoc, Ag1, Ag2, Ag3) must range from -1 to 1",
+            "formulation": "$-1 \\leq Apv_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Asoc_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag1_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag2_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag3_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$",
+            "code": null
+        },
+        {
+            "description": "Battery charging and discharging must result in a capacity change of 100 * Asoc_t",
+            "formulation": "$\\\\forall t, 100 \\\\times Asoc_t = soc_{t} - soc_{t-1}$",
+            "code": null
+        },
+        {
+            "description": "The sum of power from generators and renewable sources must not exceed the residential load by more than the grid transaction limit to avoid instability",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] \\\\leq Load[t] + GridTransactionLimit$",
+            "code": null
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] = Pg1[t] + Pg2[t] + Pg3[t] + PvPower[t] + Wind[t]$"
+        }
+    ],
+    "variables": {
+        "Ag1": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 1 power at each time step"
+        },
+        "Ag2": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 2 power at each time step"
+        },
+        "Ag3": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 3 power at each time step"
+        },
+        "PvPower": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjusted photovoltaic power generation at each time step"
+        },
+        "Apv": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for photovoltaic power at each time step"
+        },
+        "ExcessEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The excess energy at each time step"
+        },
+        "ShortageEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The shortage energy at each time step"
+        },
+        "Asoc": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for the battery state of charge at each time step"
+        },
+        "TotalPowerSupply": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The total power supply from generators and renewable sources at each time step"
+        },
+        "GridTransactionLimit": {
+            "shape": [],
+            "type": "float",
+            "definition": "The maximum allowable excess power supply over the residential load to maintain grid stability"
+        }
+    }
+}
\ No newline at end of file
diff --git a/llma/data/state_6_code.json b/llma/data/state_6_code.json
new file mode 100644
index 0000000..4f2a1f9
--- /dev/null
+++ b/llma/data/state_6_code.json
@@ -0,0 +1,220 @@
+{
+    "description": "- The energy system includes photovoltaic power stations, wind power stations, generators, batteries, and residential buildings.\n- The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\n- Electricity price, residential load, photovoltaic, and wind power generation are \\\\param{price_{}}, \\\\param{load_{}}, \\\\param{pv_{}}, and \\\\param{wind_{}}, each with 24 time steps representing hourly data.\n- Battery state of charge is \\\\param{soc_{}}, with a total battery capacity of 500. Power of generators 1, 2, and 3 is \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, respectively. The initial values at time step 0 are provided, and the system will update these values at each subsequent time step based on decisions made.\n- \\\\var{Apv_{}}, \\\\var{Asoc_{}}, \\\\var{Ag1_{}}, \\\\var{Ag2_{}}, and \\\\var{Ag3_{}} are adjustment factors ranging from -1 to 1, changing according to the value from the previous time step and affecting system parameters such as power generation and battery capacity.\n- Battery charging and discharging result in a capacity change of 100 * \\\\var{Asoc_{t}}.\n- Generators 1, 2, and 3 power change by 100 * \\\\var{Ag1_{t}}, 100 * \\\\var{Ag2_{t}}, and 200 * \\\\var{Ag3_{t}}.\n- \\\\param{soc_{t}} is calculated as (0.18 * \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}), with a maximum of 0.8 and a minimum of 0.2.\n- \\\\param{pg1_{t}} is calculated as (100 * \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}), with a maximum of 150 and a minimum of 10.\n- \\\\param{pg2_{t}} is calculated as (100 * \\\\var{Ag2_{t}} + \\\\param{pg2_{t-1}}), with a maximum of 375 and a minimum of 50.\n- \\\\param{pg3_{t}} is calculated as (200 * \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}), with a maximum of 500 and a minimum of 100.\n- Photovoltaic power is calculated as (\\\\param{pv_{t}} * (1 + \\\\var{Apv})).\n- Total power supply at each time step t = \\\\param{soc_{t}} + \\\\param{pg1_{t}} + \\\\param{pg2_{t}} + \\\\param{pg3_{t}}+ (photovoltaic power at time step t) + \\\\param{wind_{t}}.\n- Excess energy = total power supply - \\\\param{load_{t}}, and no less than 0.\n- Shortage energy = \\\\param{load_{t}} - total power supply, and no less than 0.\n- The total cost structure includes the following:\n- Battery cost = \\\\var{Asoc_{t}} + 0.1 * \\\\param{soc_{t}};\n- Gen1 cost = 0.0034 * \\\\param{pg1_{t}}^2 + 3 * \\\\param{pg1_{t}} + 30;\n- Gen2 cost = 0.001 * \\\\param{pg2_{t}}^2 + 10 * \\\\param{pg2_{t}} + 40;\n- Gen3 cost = 0.001 * \\\\param{pg3_{t}}^2 + 15 * \\\\param{pg3_{t}} + 70;\n- Renewable energy cost = 0.01 * (\\\\param{pv_{t}} + \\\\param{wind_{t}});\n- Sales revenue = 0.5 * \\\\param{price_{t}} * (excess energy);\n- Purchase cost = \\\\param{price_{t}} * (shortage energy);\n- Excess penalty = max(0, (excess energy - 100) * 50);\n- Shortage penalty = max(0, (shortage energy - 100) * 50).\n- The cost of each power generation module should be considered when supplying power, usually starting the subsequent generator when the power of the previous generator is insufficient to meet the load.\n- Minimize transactions exceeding the power grid transaction limit to avoid power grid fluctuations affecting the stability of residential electricity consumption.\n- Decisions made at each time step will update \\\\param{soc_{}}, \\\\param{pg1_{}}, \\\\param{pg2_{}}, and \\\\param{pg3_{}}, and these updated values will serve as the starting point for calculations in the next time step.\n",
+    "parameters": {
+        "TotalBatteryCapacity": {
+            "shape": [],
+            "definition": "The total capacity of the battery",
+            "type": "int"
+        },
+        "InitialSoc": {
+            "shape": [],
+            "definition": "Initial state of charge of the battery",
+            "type": "float"
+        },
+        "InitialPg1": {
+            "shape": [],
+            "definition": "Initial power of generator 1",
+            "type": "float"
+        },
+        "InitialPg2": {
+            "shape": [],
+            "definition": "Initial power of generator 2",
+            "type": "float"
+        },
+        "InitialPg3": {
+            "shape": [],
+            "definition": "Initial power of generator 3",
+            "type": "float"
+        },
+        "Price": {
+            "shape": [
+                24
+            ],
+            "definition": "Electricity price at each time step",
+            "type": "float"
+        },
+        "Load": {
+            "shape": [
+                24
+            ],
+            "definition": "Residential load at each time step",
+            "type": "float"
+        },
+        "Pv": {
+            "shape": [
+                24
+            ],
+            "definition": "Photovoltaic power generation at each time step",
+            "type": "float"
+        },
+        "Wind": {
+            "shape": [
+                24
+            ],
+            "definition": "Wind power generation at each time step",
+            "type": "float"
+        }
+    },
+    "objective": {
+        "description": "\"The goal is to minimize the total costs of the energy system and the gap between power supply and residential load.\"",
+        "formulation": "$\\min \\sum_{t=1}^{24} \\left( \\text{Battery cost}_t + \\text{Gen1 cost}_t + \\text{Gen2 cost}_t + \\text{Gen3 cost}_t + \\text{Renewable energy cost}_t + \\text{Excess penalty}_t + \\text{Shortage penalty}_t - \\text{Sales revenue}_t + \\text{Purchase cost}_t + |\\text{ExcessEnergy}_t| + |\\text{ShortageEnergy}_t| \\right)$",
+        "code": "model.setObjective(\n    quicksum(\n        (Asoc[t] + 0.1 * soc[t-1] +\n         0.0034 * pg1[t-1] ** 2 + 3 * pg1[t-1] + 30 +\n         0.001 * pg2[t-1] ** 2 + 10 * pg2[t-1] + 40 +\n         0.001 * pg3[t-1] ** 2 + 15 * pg3[t-1] + 70 +\n         0.01 * (pv[t-1] + wind[t-1]) +\n         max(0, (ExcessEnergy[t] - 100) * 50) +\n         max(0, (ShortageEnergy[t] - 100) * 50) -\n         0.5 * Price[t-1] * ExcessEnergy[t] +\n         Price[t-1] * ShortageEnergy[t] +\n         abs(ExcessEnergy[t]) + abs(ShortageEnergy[t])\n        ) for t in range(1, 25)\n    ),\n    GRB.MINIMIZE\n)"
+    },
+    "constraints": [
+        {
+            "description": "Battery state of charge (soc_t) must be between 0.2 and 0.8",
+            "formulation": "$0.2 \\leq soc_t \\leq 0.8$",
+            "code": "# Preceding state of charge variable initialization\nsoc = InitialSoc\n\n# Loop over each time step to add the soc constraints\nfor t in range(24):\n    # Calculate soc_t based on the previous soc and the adjustment factor\n    soc_t = 0.18 * Asoc[t] + soc\n    \n    # Add the lower bound constraint for soc_t\n    model.addConstr(soc_t >= 0.2)\n    \n    # Add the upper bound constraint for soc_t\n    model.addConstr(soc_t <= 0.8)\n    \n    # Update soc to the newly calculated soc_t for the next iteration\n    soc = soc_t"
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be between 10 and 150",
+            "formulation": "$10 \\leq pg1_t \\leq 150$",
+            "code": "# Assuming decision variables Pg1[t] have been defined for each time step t\nfor t in range(24):\n    if t == 0:\n        # For the first time step, use the initial value of Pg1\n        model.addConstr(Pg1[t] <= 150)\n        model.addConstr(Pg1[t] >= 10)\n    else:\n        # For subsequent time steps, calculate Pg1 based on the previous time step and Ag1\n        model.addConstr(Pg1[t] <= 150)\n        model.addConstr(Pg1[t] >= 10)\n        model.addConstr(Pg1[t] == 100 * Ag1[t] + Pg1[t-1])"
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be between 50 and 375",
+            "formulation": "$50 \\leq pg2_t \\leq 375$",
+            "code": "for t in range(1, 24):\n    model.addConstr(InitialPg2 + 100 * Ag2[t] >= 50)\n    model.addConstr(InitialPg2 + 100 * Ag2[t] <= 375)"
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be between 100 and 500",
+            "formulation": "$100 \\\\leq pg3_t \\\\leq 500$",
+            "code": "for t in range(24):\n    model.addConstr(Pg3[t] >= 100)  # Lower bound constraint for each time step\n    model.addConstr(Pg3[t] <= 500)  # Upper bound constraint for each time step"
+        },
+        {
+            "description": "Battery state of charge (soc_t) must be calculated as (0.18 * Asoc_t + soc_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{soc_{t}} = 0.18 \\\\cdot \\\\var{Asoc_{t}} + \\\\param{soc_{t-1}}$",
+            "code": "# Assuming soc is defined as a list of decision variables for each time step\nsoc = model.addVars(24, lb=0.2, ub=0.8, vtype=GRB.CONTINUOUS, name=\"soc\")\n\n# Loop over each time step starting from 1 up to 24\nfor t in range(1, 24):\n    # Add the constraint for the state of charge calculation\n    model.addConstr(soc[t] == 0.18 * Asoc[t] + soc[t-1])"
+        },
+        {
+            "description": "Generator 1 power (pg1_t) must be calculated as (100 * Ag1_t + pg1_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg1_{t}} = 100 \\\\cdot \\\\var{Ag1_{t}} + \\\\param{pg1_{t-1}}$",
+            "code": "for t in range(1, 24):\n    model.addConstr(pg1[t] == 100 * Ag1[t] + pg1[t-1])"
+        },
+        {
+            "description": "Generator 2 power (pg2_t) must be calculated as (100 * Ag2_t + pg2_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\dots,24\\\\}, \\\\param{pg2_{t}} = 100 \\\\times Ag2_t + \\\\param{pg2_{t-1}}$",
+            "code": "# Assuming that `pg2` is a list of Gurobi variables representing the power of Generator 2 at each time step\n# and that `InitialPg2` is a parameter representing the initial power of Generator 2 at time step 0\n\n# Define the Generator 2 power for the first time step, which is the initial value and does not have a constraint\npg2 = [model.addVar(vtype=GRB.CONTINUOUS, lb=50, ub=375, name=\"pg2_0\")]\n\n# Now add the constraints for time steps 1 to 23\nfor t in range(1, 24):\n    pg2.append(model.addVar(vtype=GRB.CONTINUOUS, lb=50, ub=375, name=f\"pg2_{t}\"))\n    model.addConstr(pg2[t] == 100 * Ag2[t-1] + pg2[t-1])"
+        },
+        {
+            "description": "Generator 3 power (pg3_t) must be calculated as (200 * Ag3_t + pg3_t-1)",
+            "formulation": "$\\\\forall t \\\\in \\\\{1,\\\\ldots,24\\\\}, \\\\param{pg3_{t}} = 200 \\\\cdot \\\\var{Ag3_{t}} + \\\\param{pg3_{t-1}}$",
+            "code": "# Assuming Pg3 is defined as a list of decision variables representing power of generator 3 at each time step\nfor t in range(1, 24):\n    model.addConstr(Pg3[t] == 200 * Ag3[t] + Pg3[t-1])"
+        },
+        {
+            "description": "Photovoltaic power must be calculated as (pv_t * (1 + Apv))",
+            "formulation": "$\\\\forall t, PvPower_t = Pv[t] \\\\times (1 + Apv_t)$",
+            "code": "for t in range(24):\n    model.addConstr(PvPower[t] == Pv[t] * (1 + Apv[t]))"
+        },
+        {
+            "description": "Excess energy must be no less than 0",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] \\\\geq 0$",
+            "code": "for t in range(24):\n    model.addConstr(ExcessEnergy[t] >= 0)"
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, ExcessEnergy[t] = TotalSupply[t] - Load[t]$",
+            "code": "for t in range(24):\n    # Define the total power supply at time step t\n    TotalSupply_t = (InitialPg1 + Ag1[t] * 100 +\n                     InitialPg2 + Ag2[t] * 100 +\n                     InitialPg3 + Ag3[t] * 200 +\n                     Pv[t] * (1 + Apv[t]) +\n                     Wind[t] +\n                     InitialSoc + Asoc[t] * 0.18)\n    # Add the constraint for excess energy at time step t\n    model.addConstr(ExcessEnergy[t] == TotalSupply_t - Load[t])"
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalSupply[t] = soc[t] + pg1[t] + pg2[t] + pg3[t] + PvPower[t] + Wind[t]$",
+            "code": "for t in range(24):\n    # Calculate updated generator powers and battery state of charge\n    soc_t = InitialSoc + 0.18 * Asoc[t] if t == 0 else soc[t-1] + 0.18 * Asoc[t]\n    pg1_t = InitialPg1 + 100 * Ag1[t] if t == 0 else pg1[t-1] + 100 * Ag1[t]\n    pg2_t = InitialPg2 + 100 * Ag2[t] if t == 0 else pg2[t-1] + 100 * Ag2[t]\n    pg3_t = InitialPg3 + 200 * Ag3[t] if t == 0 else pg3[t-1] + 200 * Ag3[t]\n\n    # Calculate photovoltaic power\n    PvPower_t = Pv[t] * (1 + Apv[t])\n\n    # Add the constraint for total power supply at time t\n    model.addConstr(TotalPowerSupply[t] == soc_t + pg1_t + pg2_t + pg3_t + PvPower_t + Wind[t])"
+        },
+        {
+            "description": "Shortage energy must be no less than 0",
+            "formulation": "$\\\\forall t, ShortageEnergy[t] \\\\geq 0$",
+            "code": "for t in range(24):\n    model.addConstr(ShortageEnergy[t] >= 0)"
+        },
+        {
+            "description": "Adjustment factors (Apv, Asoc, Ag1, Ag2, Ag3) must range from -1 to 1",
+            "formulation": "$-1 \\leq Apv_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Asoc_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag1_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag2_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$\\n$-1 \\leq Ag3_t \\leq 1 \\\\quad \\\\forall t \\\\in \\\\{1, \\\\dots, 24\\\\}$",
+            "code": "for t in range(24):\n    model.addConstr(-1 <= Apv[t])\n    model.addConstr(Apv[t] <= 1)\n    model.addConstr(-1 <= Asoc[t])\n    model.addConstr(Asoc[t] <= 1)\n    model.addConstr(-1 <= Ag1[t])\n    model.addConstr(Ag1[t] <= 1)\n    model.addConstr(-1 <= Ag2[t])\n    model.addConstr(Ag2[t] <= 1)\n    model.addConstr(-1 <= Ag3[t])\n    model.addConstr(Ag3[t] <= 1)"
+        },
+        {
+            "description": "Battery charging and discharging must result in a capacity change of 100 * Asoc_t",
+            "formulation": "$\\\\forall t, 100 \\\\times Asoc_t = soc_{t} - soc_{t-1}$",
+            "code": "for t in range(1, 24):\n    model.addConstr(100 * Asoc[t] == soc[t] - soc[t-1])"
+        },
+        {
+            "description": "The sum of power from generators and renewable sources must not exceed the residential load by more than the grid transaction limit to avoid instability",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] \\\\leq Load[t] + GridTransactionLimit$",
+            "code": "for t in range(24):\n    # Calculate the power from each generator based on previous state and adjustment\n    pg1_power = InitialPg1 + 100 * Ag1[t]\n    pg2_power = InitialPg2 + 100 * Ag2[t]\n    pg3_power = InitialPg3 + 200 * Ag3[t]\n\n    # Calculate the adjusted photovoltaic power\n    pv_power = Pv[t] * (1 + Apv[t])\n\n    # Express the total power supply\n    TotalPowerSupply[t] = pg1_power + pg2_power + pg3_power + pv_power + Wind[t]\n\n    # Add the constraint that total power supply cannot exceed the load by more than the grid transaction limit\n    model.addConstr(TotalPowerSupply[t] <= Load[t] + GridTransactionLimit)"
+        },
+        {
+            "description": "auxiliary constraint",
+            "formulation": "$\\\\forall t, TotalPowerSupply[t] = Pg1[t] + Pg2[t] + Pg3[t] + PvPower[t] + Wind[t]$",
+            "code": "for t in range(24):\n    # Assuming that Pg1, Pg2, Pg3 are defined as variables in the model with the correct initial values and adjustments\n    Pg1[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f\"Pg1_{t}\")\n    Pg2[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f\"Pg2_{t}\")\n    Pg3[t] = model.addVar(vtype=GRB.CONTINUOUS, name=f\"Pg3_{t}\")\n\n    # Define the auxiliary constraint for each time step\n    model.addConstr(TotalPowerSupply[t] == Pg1[t] + Pg2[t] + Pg3[t] + PvPower[t] + Wind[t])"
+        }
+    ],
+    "variables": {
+        "Ag1": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 1 power at each time step"
+        },
+        "Ag2": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 2 power at each time step"
+        },
+        "Ag3": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "Adjustment factor for generator 3 power at each time step"
+        },
+        "PvPower": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjusted photovoltaic power generation at each time step"
+        },
+        "Apv": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for photovoltaic power at each time step"
+        },
+        "ExcessEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The excess energy at each time step"
+        },
+        "ShortageEnergy": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The shortage energy at each time step"
+        },
+        "Asoc": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The adjustment factor for the battery state of charge at each time step"
+        },
+        "TotalPowerSupply": {
+            "shape": [
+                24
+            ],
+            "type": "continuous",
+            "definition": "The total power supply from generators and renewable sources at each time step"
+        },
+        "GridTransactionLimit": {
+            "shape": [],
+            "type": "float",
+            "definition": "The maximum allowable excess power supply over the residential load to maintain grid stability"
+        }
+    }
+}
\ No newline at end of file
diff --git a/llma/execute_code.py b/llma/execute_code.py
new file mode 100644
index 0000000..b264bf9
--- /dev/null
+++ b/llma/execute_code.py
@@ -0,0 +1,93 @@
+import os
+import numpy as np
+import json
+from utils import get_response
+import subprocess
+
+debug_template = """
+You are an Operations Research consultant hired to address optimization issues for a company. Below is the problem description and the problematic code, followed by the error it produces:
+
+Problem Description:
+{description}
+
+Problematic Code:
+{code}
+
+Error Message:
+{error}
+
+Your task is to debug the code. Begin by assessing the situation, then provide the corrected code in the following format:
+
+=====
+import ...
+...
+
+=====
+
+- Ensure no output follows the closing ===== line.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+
+def extract_code(text):
+    ind_1 = text.find("=====")
+    ind_2 = text.find("=====", ind_1 + 1)
+    code = text[ind_1 + 5: ind_2].strip()
+    code = code.replace("```python", "").replace("```", "").strip()
+
+    return code
+
+
+def execute_code(dir, code_filename):
+    try:
+        # Using Python's subprocess to execute the code as a separate process
+        result = subprocess.run(
+            ["python", code_filename],
+            capture_output=True,
+            text=True,
+            check=True,
+            cwd=dir,
+        )
+        # save result
+        with open(os.path.join(dir, "code_output.txt"), "w") as f:
+            f.write(f"Optimal Revenue: {result.stdout}\n")
+        return result.stdout, "Success"
+    except subprocess.CalledProcessError as e:
+        return e.stderr, "Error"
+
+
+def execute_and_debug(state, dir, model, logger, max_tries=3):
+    code_filename = "code.py"
+    with open(os.path.join(dir, code_filename), "r") as f:
+        code = f.read()
+
+    for iteration in range(max_tries):
+        output, status = execute_code(dir, code_filename)
+
+        # Print status and update the prompt
+        if status == "Success":
+            logger.log("Code executed successfully. Output:\n" + output)
+            break
+        else:
+            error_filename = f"error_{iteration}.txt"
+            with open(os.path.join(dir, error_filename), "w") as f:
+                f.write(output)
+
+            p = debug_template.format(description=state["description"], code=code, error=output)
+            logger.log(f"Iteration {iteration + 1}: Error encountered. Debugging...")
+            logger.log(p)
+            logger.log("==========\n\n\n\n")
+
+            response = get_response(p, model=model)
+            logger.log("Response received.")
+            logger.log(response)
+            logger.log("==========\n\n\n\n")
+
+            code = extract_code(response)
+            code_filename = f"code_{iteration + 1}.py"
+            code_file_path = os.path.join(dir, code_filename)
+            with open(code_file_path, "w") as f:
+                f.write(code)
+            logger.log(f"Iteration {iteration + 1}: Error encountered. Debugging...")
+    else:
+        logger.log("Max iterations reached with errors remaining.")
diff --git a/llma/generate_code.py b/llma/generate_code.py
new file mode 100644
index 0000000..4aa8768
--- /dev/null
+++ b/llma/generate_code.py
@@ -0,0 +1,73 @@
+import os
+
+
+def get_var_code(symbol, shape, type, definition, solver="gurobipy"):
+    if solver == "gurobipy":
+        if not shape:
+            return f'{symbol} = model.addVar(vtype=GRB.{type.upper()}, name="{symbol}")\n'
+        else:
+            return f"{symbol} = model.addVars(" + ", ".join([str(i) for i in shape]) + f', vtype=GRB.{type.upper()}, name="{symbol}")\n'
+    else:
+        raise NotImplementedError(f"Solver {solver} is not implemented")
+
+
+def get_param_code(symbol, shape, definition):
+    return f'{symbol} = data["{symbol}"] # shape: {shape}, definition: {definition}\n'
+
+
+def generate_code(state, dir):
+    code = []
+    code.append(
+        f"""
+import os
+import numpy as np
+import json 
+from gurobipy import Model, GRB, quicksum
+
+
+model = Model("OptimizationProblem")
+
+with open("data.json", "r") as f:
+    data = json.load(f)
+"""
+    )
+
+    code.append("\n\n### Define the parameters\n")
+    for symbol, v in state["parameters"].items():
+        print(v)
+        code.append(get_param_code(symbol, v["shape"], v["definition"]))
+
+    code.append("\n\n### Define the variables\n")
+    for symbol, v in state["variables"].items():
+        code.append(get_var_code(symbol, v["shape"], v["type"], v["definition"], solver="gurobipy"))
+
+    code.append("\n\n### Define the constraints\n")
+    for c in state["constraints"]:
+        code.append(c["code"])
+
+    code.append("\n\n### Define the objective\n")
+    code.append(state["objective"]["code"])
+
+    code.append("\n\n### Optimize the model\n")
+    code.append("model.optimize()\n")
+
+    code.append("\n\n### Output optimal objective value\n")
+    code.append(f'print("Optimal Objective Value: ", model.objVal)\n')
+
+    # code to save the optimal value if it exists
+    code.append(
+        """
+if model.status == GRB.OPTIMAL:
+    with open("output_solution.txt", "w") as f:
+        f.write(str(model.objVal))
+    print("Optimal Objective Value: ", model.objVal)
+else:
+    with open("output_solution.txt", "w") as f:
+        f.write(model.status)
+"""
+    )
+
+    code_str = "\n".join(code)
+
+    with open(os.path.join(dir, "code.py"), "w") as f:
+        f.write(code_str)
diff --git a/llma/gurobi.py b/llma/gurobi.py
new file mode 100644
index 0000000..0be8364
--- /dev/null
+++ b/llma/gurobi.py
@@ -0,0 +1,120 @@
+import json
+import gurobipy as gp
+import pandas as pd
+from gurobipy import GRB
+
+
+data = pd.read_csv("data/data.csv")
+price = data['price']
+load = data['load']
+pre_pv = data['pv']
+wind = data['wind']
+period = len(data)
+
+DG_parameters = {
+    'gen_1': {'a': 0.0034, 'b': 3, 'c': 30,
+              'power_output_max': 150, 'power_output_min': 10,
+              'ramping_up': 100, 'ramping_down': 100},
+
+    'gen_2': {'a': 0.001, 'b': 10, 'c': 40,
+              'power_output_max': 375, 'power_output_min': 50,
+              'ramping_up': 100, 'ramping_down': 100},
+
+    'gen_3': {'a': 0.001, 'b': 15, 'c': 70,
+              'power_output_max': 500, 'power_output_min': 100,
+              'ramping_up': 200, 'ramping_down': 200}
+}
+
+
+def get_dg_info(parameters):
+    p_max = []
+    p_min = []
+    ramping_up = []
+    ramping_down = []
+    a_para = []
+    b_para = []
+    c_para = []
+    for name, gen_info in parameters.items():
+        p_max.append(gen_info['power_output_max'])
+        p_min.append(gen_info['power_output_min'])
+        ramping_up.append(gen_info['ramping_up'])
+        ramping_down.append(gen_info['ramping_down'])
+        a_para.append(gen_info['a'])
+        b_para.append(gen_info['b'])
+        c_para.append(gen_info['c'])
+    return p_max, p_min, ramping_up, ramping_down, a_para, b_para, c_para
+
+
+p_max, p_min, ramping_up, ramping_down, a_para, b_para, c_para = get_dg_info(parameters=DG_parameters)
+NUM_GEN = len(DG_parameters.keys())
+grid_exchange_ability = 100
+sell_coefficient = 0.5
+battery_capacity = 500
+battery_efficiency = 0.9
+initial_soc = 0.2
+battery_degradation = 0.01
+battery_holding = 0.1
+battery_maxcharge = 100
+solar_cofficient = 0.01
+wind_cofficient = 0.01
+
+m = gp.Model("UC")
+
+# 设置系统变量
+on_off = m.addVars(NUM_GEN, period, vtype=GRB.BINARY, name='on_off')
+gen_output = m.addVars(NUM_GEN, period, vtype=GRB.CONTINUOUS, name='output')
+# 设置充放电约束
+soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name='SOC')
+battery_energy_change = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-battery_maxcharge, ub=battery_maxcharge, name='battery_action')
+# 设置外部电网与能源系统交换约束
+grid_energy_import = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=grid_exchange_ability, name='import')
+grid_energy_export = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=grid_exchange_ability, name='export')
+# 设置电压控制约束
+pv_voltage = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name='pv_voltage')
+
+# 计算光伏发电量
+pv = [pre_pv[t] * (1 + pv_voltage[t]) for t in range(period)]
+
+# 1. 添加平衡约束
+m.addConstrs(((sum(gen_output[g, t] for g in range(NUM_GEN)) + pv[t] + wind[t] + grid_energy_import[t] == load[t]
+               + battery_energy_change[t] + grid_energy_export[t]) for t in range(period)), name='powerbalance')
+# 2. 添加发电机最大/最小功率约束
+m.addConstrs((gen_output[g, t] <= on_off[g, t] * p_max[g] for g in range(NUM_GEN) for t in range(period)),
+             'gen_output_max')
+m.addConstrs((gen_output[g, t] >= on_off[g, t] * p_min[g] for g in range(NUM_GEN) for t in range(period)),
+             'gen_output_min')
+# 3. 添加上升和下降约束
+m.addConstrs((gen_output[g, t + 1] - gen_output[g, t] <= ramping_up[g] for g in range(NUM_GEN)
+              for t in range(period - 1)), 'ramping_up')
+m.addConstrs((gen_output[g, t] - gen_output[g, t + 1] <= ramping_down[g] for g in range(NUM_GEN)
+              for t in range(period - 1)), 'ramping_down')
+# 4. 添加电池容量约束
+m.addConstr(battery_capacity * soc[0] == battery_capacity * initial_soc +
+            (battery_energy_change[0] * battery_efficiency), name='soc0')
+m.addConstrs((battery_capacity * soc[t] == battery_capacity * soc[t - 1] +
+              (battery_energy_change[t] * battery_efficiency) for t in range(1, period)), name='soc update')
+# 设置成本函数
+cost_gen = gp.quicksum(
+    (a_para[g] * gen_output[g, t] * gen_output[g, t] + b_para[g] * gen_output[g, t] + c_para[g] * on_off[g, t])
+    for t in range(period) for g in range(NUM_GEN))
+cost_battery = gp.quicksum(
+    battery_energy_change[t] * battery_degradation + soc[t] * battery_holding for t in range(period))
+cost_import = gp.quicksum(grid_energy_import[t] * price[t] for t in range(period))
+cost_export = gp.quicksum(grid_energy_export[t] * price[t] * sell_coefficient for t in range(period))
+cost_solar = gp.quicksum(pv[t] * solar_cofficient for t in range(period))
+cost_wind = gp.quicksum(wind[t] * wind_cofficient for t in range(period))
+
+m.setObjective((cost_gen + cost_battery + cost_import - cost_export + cost_solar + cost_wind), GRB.MINIMIZE)
+m.optimize()
+
+# 记录动作便于辅助决策
+results = []
+for t in range(period):
+    action = [battery_energy_change[t].x / 100,
+              (gen_output[0, t].x - (gen_output[0, t - 1].x if t > 0 else 0)) / 100,
+              (gen_output[1, t].x - (gen_output[1, t - 1].x if t > 0 else 0)) / 100,
+              (gen_output[2, t].x - (gen_output[2, t - 1].x if t > 0 else 0)) / 200,
+              pv_voltage[t].x - (pv_voltage[t - 1].x if t > 0 else 0)]
+    results.append(action)
+with open('./results_9.3.json', 'w') as f:
+    json.dump(results, f, indent=2)
diff --git a/llma/main.py b/llma/main.py
new file mode 100644
index 0000000..853404f
--- /dev/null
+++ b/llma/main.py
@@ -0,0 +1,124 @@
+import argparse
+import os
+import time
+import json
+
+from constraint import get_constraints
+from constraint_model import get_constraint_formulations
+from execute_code import execute_and_debug
+from generate_code import generate_code
+from target_code import get_codes
+from objective import get_objective
+from objective_model import get_objective_formulation
+from rag.rag_utils import RAGMode
+from utils import create_state, load_state, save_state, Logger
+
+parser = argparse.ArgumentParser(description="Run the optimization problem")
+parser.add_argument("--dir", type=str, default=".", help="Directory of the problem")
+parser.add_argument("--devmode", type=int, default=1)
+parser.add_argument("--rag-mode", type=RAGMode, choices=list(RAGMode), default=None, help="RAG mode")
+args = parser.parse_args()
+
+if __name__ == "__main__":
+    # #### SET BEFORE RUNNING! ####
+    DIR = args.dir
+    DEV_MODE = args.devmode
+    RAG_MODE = args.rag_mode
+    ERROR_CORRECTION = True
+    MODEL = "glm-4-0520"
+    # MODEL = "gpt-4o"
+    # MODEL = "llama3-70b-8192"
+    # #############################
+    run_dir = os.path.join(DIR, "data")
+    if not os.path.exists(run_dir):
+        os.makedirs(run_dir)
+
+    state = create_state(DIR, run_dir)
+    with open(os.path.join(run_dir, "labels.json"), "r") as f:
+        labels = json.load(f)
+    save_state(state, os.path.join(run_dir, "state_1_params.json"))
+
+    logger = Logger(f"{run_dir}/log.txt")
+    logger.reset()
+
+    # ###### Get objective
+    state = load_state(os.path.join(run_dir, "state_1_params.json"))
+    objective = get_objective(
+        state["description"],
+        state["parameters"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=labels,
+    )
+    print(objective)
+    state["objective"] = objective
+    save_state(state, os.path.join(run_dir, "state_2_objective.json"))
+    # # #######
+    # ####### Get constraints
+    state = load_state(os.path.join(run_dir, "state_2_objective.json"))
+    constraints = get_constraints(
+        state["description"],
+        state["parameters"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=labels,
+    )
+    print(constraints)
+    state["constraints"] = constraints
+    save_state(state, os.path.join(run_dir, "state_3_constraints.json"))
+    # # #######
+    # ####### Get constraint formulations
+    state = load_state(os.path.join(run_dir, "state_3_constraints.json"))
+    constraints, variables = get_constraint_formulations(
+        state["description"],
+        state["parameters"],
+        state["constraints"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=labels,
+    )
+    state["constraints"] = constraints
+    state["variables"] = variables
+    save_state(state, os.path.join(run_dir, "state_4_constraints_modeled.json"))
+    #######
+    # ####### Get objective formulation
+    state = load_state(os.path.join(run_dir, "state_4_constraints_modeled.json"))
+    objective = get_objective_formulation(
+        state["description"],
+        state["parameters"],
+        state["variables"],
+        state["objective"],
+        model=MODEL,
+        check=ERROR_CORRECTION,
+        rag_mode=RAG_MODE,
+        labels=labels,
+    )
+    state["objective"] = objective
+    print("DONE OBJECTIVE FORMULATION")
+    save_state(state, os.path.join(run_dir, "state_5_objective_modeled.json"))
+    # #######
+    # # ####### Get codes
+    state = load_state(os.path.join(run_dir, "state_5_objective_modeled.json"))
+    constraints, objective = get_codes(
+        state["description"],
+        state["parameters"],
+        state["variables"],
+        state["constraints"],
+        state["objective"],
+        model=MODEL,
+        check=ERROR_CORRECTION,
+    )
+    state["constraints"] = constraints
+    state["objective"] = objective
+    save_state(state, os.path.join(run_dir, "state_6_code.json"))
+    # #######
+    # # ####### Run codes
+    state = load_state(os.path.join(run_dir, "state_6_code.json"))
+    generate_code(state, run_dir)
+    execute_and_debug(state, model=MODEL, dir=run_dir, logger=logger)
diff --git a/llma/objective.py b/llma/objective.py
new file mode 100644
index 0000000..217bb31
--- /dev/null
+++ b/llma/objective.py
@@ -0,0 +1,67 @@
+import json
+from rag.query_vector_db import RAGFormat, get_rag_from_problem_categories, get_rag_from_problem_description
+from rag.rag_utils import RAGMode
+from utils import get_response
+
+prompt_objective = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+{rag}-----
+{description}
+-----
+
+And here's a list of parameters that we have extracted from the description:
+
+{params}
+
+Your task is to identify and extract the optimization objective from the description. The objective is the goal that the optimization model is trying to achieve (e.g. maximize profit, minimize cost). Please generate the output in the following format:
+
+=====
+OBJECTIVE: objective description
+=====
+
+for example:
+
+=====    
+OBJECTIVE: "The goal is to maximize the total profit from producing television sets"
+=====
+
+- Do not generate anything after the objective.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+
+def extract_objective(text):
+    # find first and second occurence of "=====" in the text
+    ind_1 = text.find("=====")
+    ind_2 = text.find("=====", ind_1 + 1)
+
+    # extract the text between the two "=====" occurences
+    objective = text[ind_1:ind_2]
+    objective = objective.replace("=====", "").strip()
+    objective = objective.replace("OBJECTIVE:", "").strip()
+    return objective
+
+
+def get_objective(desc, params, model, check=False, logger=None,
+                  rag_mode: RAGMode | None = None, labels: dict | None = None):
+    if isinstance(rag_mode, RAGMode):
+        match rag_mode:
+            case RAGMode.PROBLEM_DESCRIPTION | RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                rag = get_rag_from_problem_description(desc, RAGFormat.PROBLEM_DESCRIPTION_OBJECTIVE, top_k=5)
+            case RAGMode.PROBLEM_LABELS:
+                assert labels is not None
+                rag = get_rag_from_problem_categories(desc, labels, RAGFormat.PROBLEM_DESCRIPTION_OBJECTIVE, top_k=5)
+        rag = f"-----\n{rag}-----\n\n"
+    else:
+        rag = ""
+    res = get_response(prompt_objective.format(
+        description=desc,
+        params=json.dumps(params, indent=4),
+        rag=rag),
+        model=model)
+
+    objective = extract_objective(res)
+    objective = {"description": objective, "formulation": None, "code": None}
+
+    return objective
diff --git a/llma/objective_model.py b/llma/objective_model.py
new file mode 100644
index 0000000..d362033
--- /dev/null
+++ b/llma/objective_model.py
@@ -0,0 +1,95 @@
+import json
+import pandas as pd
+
+from rag.query_vector_db import RAGFormat, get_rag_from_objective, get_rag_from_problem_categories, \
+    get_rag_from_problem_description
+from rag.rag_utils import RAGMode, constraint_path
+from utils import get_response, extract_equal_sign_closed
+
+prompt_objective_model = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+And here's a list of parameters that we have extracted from the description:
+
+{params}
+
+And here's a list of all variables that we have defined so far to model the problem as an (MI)LP:
+
+{vars}
+
+Your task is to model the following objective mathematically in LaTeX for the MILP formulation:
+
+{objective}
+
+The objective is the goal that the optimization model is trying to achieve (e.g. maximize profit, minimize cost). Please generate the output in the following format:
+
+=====
+objective formulation in LaTeX, between $...$,
+=====
+
+Here's an example output:
+
+=====
+$\max \sum_{{i=1}}^{{N}} price_i x_i$
+=====
+
+- You can only use existing parameters and variables in the formulation.
+- Do not generate anything after the objective!
+
+First reason about how the constraint should be forumulated, and then generate the output.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+
+def get_objective_formulation(desc, params, vars, objective, model,
+                              check=False, rag_mode: RAGMode | None = None, labels: dict | None = None):
+    if isinstance(rag_mode, RAGMode):
+        match rag_mode:
+            case RAGMode.PROBLEM_DESCRIPTION:
+                rag = get_rag_from_problem_description(desc, RAGFormat.OBJECTIVE_FORMULATION, top_k=10)
+            case RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                rag = ""
+            case RAGMode.PROBLEM_LABELS:
+                assert labels is not None
+                rag = get_rag_from_problem_categories(desc, labels, RAGFormat.OBJECTIVE_FORMULATION, top_k=10)
+        rag = f"-----\n{rag}-----\n\n"
+    else:
+        rag = ""
+
+    k = 1
+    while k > 0:
+        try:
+            if rag_mode == RAGMode.CONSTRAINT_OR_OBJECTIVE:
+                constraint_df = pd.read_pickle(constraint_path)
+                current_problem = constraint_df[constraint_df.description == desc]
+                if not current_problem.empty:
+                    problem_name = current_problem.iloc[0].problem_name
+                else:
+                    problem_name = None
+                rag = get_rag_from_objective(desc, RAGFormat.OBJECTIVE_FORMULATION, top_k=10,
+                                             current_problem_name=problem_name)
+                rag = f"-----\n{rag}-----\n\n"
+            res = get_response(
+                prompt_objective_model.format(
+                    description=desc,
+                    params=json.dumps(params, indent=4),
+                    vars=json.dumps(vars, indent=4),
+                    objective=objective["description"],
+                ),
+                model=model,
+            )
+            formulation = extract_equal_sign_closed(res)
+            break
+        except Exception as e:
+            k -= 1
+            if k == 0:
+                raise e
+
+    return {
+        "description": objective["description"],
+        "formulation": formulation,
+    }
diff --git a/llma/optimus_tools.py b/llma/optimus_tools.py
new file mode 100644
index 0000000..4ad9cd7
--- /dev/null
+++ b/llma/optimus_tools.py
@@ -0,0 +1,127 @@
+"""
+Implement different utilities with OptiMUS v0.3
+"""
+import os
+import sys
+from typing import Dict
+
+from constraint import get_constraints
+from constraint_model import get_constraint_formulations
+from objective import get_objective
+from objective_model import get_objective_formulation
+from parameters import get_params
+from target_code import get_codes
+from utils import load_state, save_state, Logger
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "."))
+
+ERROR_CORRECTION = False
+# MODEL = "gpt-4o"
+MODEL = "glm-4-0520"
+RAG_MODE = None
+DEFAULT_LABELS = {"types": ["Mixed Integer Linear Programming"], "domains": ["Energy Optimization"]}
+
+
+def get_intro_latex_code_map(fname) -> Dict:
+    """
+    Extract the internal OptiMUS data structure that maps natural language, LaTeX and code
+    No data is required by this utility
+    """
+    run_dir = "."
+
+    # Extract parameters from the natural language description
+    with open(fname, "r") as f:
+        desc = f.read()
+        f.close()
+
+    params = get_params(desc, check=True)
+
+    # Construct the initial state
+    state = {"description": desc, "parameters": params}
+    save_state(state, "state_1_params.json")
+
+    logger = Logger(f"log.txt")
+    logger.reset()
+
+    # Get objective
+    state = load_state(os.path.join(run_dir, "state_1_params.json"))
+    objective = get_objective(
+        state["description"],
+        state["parameters"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=DEFAULT_LABELS,
+    )
+    print(objective)
+    state["objective"] = objective
+    save_state(state, os.path.join(run_dir, "state_2_objective.json"))
+
+    # Get constraints
+    state = load_state(os.path.join(run_dir, "state_2_objective.json"))
+    constraints = get_constraints(
+        state["description"],
+        state["parameters"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=DEFAULT_LABELS,
+    )
+    print(constraints)
+    state["constraints"] = constraints
+    save_state(state, os.path.join(run_dir, "state_3_constraints.json"))
+
+    # Get constraint formulations
+    state = load_state(os.path.join(run_dir, "state_3_constraints.json"))
+    constraints, variables = get_constraint_formulations(
+        state["description"],
+        state["parameters"],
+        state["constraints"],
+        check=ERROR_CORRECTION,
+        logger=logger,
+        model=MODEL,
+        rag_mode=RAG_MODE,
+        labels=DEFAULT_LABELS,
+    )
+    state["constraints"] = constraints
+    state["variables"] = variables
+    save_state(state, os.path.join(run_dir, "state_4_constraints_modeled.json"))
+
+    # Get objective formulation
+    state = load_state(os.path.join(run_dir, "state_4_constraints_modeled.json"))
+    objective = get_objective_formulation(
+        state["description"],
+        state["parameters"],
+        state["variables"],
+        state["objective"],
+        model=MODEL,
+        check=ERROR_CORRECTION,
+        rag_mode=RAG_MODE,
+        labels=DEFAULT_LABELS,
+    )
+    state["objective"] = objective
+    print("DONE OBJECTIVE FORMULATION")
+    save_state(state, os.path.join(run_dir, "state_5_objective_modeled.json"))
+
+    # # ####### Get codes
+    state = load_state(os.path.join(run_dir, "state_5_objective_modeled.json"))
+    constraints, objective = get_codes(
+        state["description"],
+        state["parameters"],
+        state["variables"],
+        state["constraints"],
+        state["objective"],
+        model=MODEL,
+        check=ERROR_CORRECTION,
+    )
+    state["constraints"] = constraints
+    state["objective"] = objective
+    save_state(state, os.path.join(run_dir, "state_6_code.json"))
+
+    return state
+
+
+if __name__ == "__main__":
+    get_intro_latex_code_map("./data/desc.txt")
diff --git a/llma/parameters.py b/llma/parameters.py
new file mode 100644
index 0000000..f82ed28
--- /dev/null
+++ b/llma/parameters.py
@@ -0,0 +1,127 @@
+import json
+import re
+from utils import extract_json_from_end, get_response, shape_string_to_list
+
+prompt_params = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Your task is to identify and extract parameters from the description. The parameters are values that are already known. Please generate the output in the following format:
+
+{{
+    "SYMBOL": {{
+        "shape": "SHAPE",
+        "definition": "DEFINITION",
+        "type": "TYPE"
+    }}
+}}
+
+Where SYMBOL is a string representing the parameter (use CamelCase), SHAPE is the shape of the parameter (e.g. "[]" for scalar, or "[N, M]" for a matrix of size N x M where N and M are scalar parameters), DEFINITION is a string describing the parameter, and type is one of "int", "float", or "binary".
+
+{{
+    "NumberOfItems": {{
+        "shape": "[]",
+        "definition": "The number of items in the inventory",
+        "type": "int"
+    }},
+    "ItemValue": {{
+        "shape": "[N]",
+        "definition": "The value of each item in the inventory",
+        "type": "float"
+    }}
+}}
+
+- Put all the parameters in a single json object.
+- Do not generate anything after the json object.
+Take a deep breath and think step by step. 
+"""
+
+prompt_params_q = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+-----
+{description}
+-----
+
+Here is a list of parameters that someone has extracted from the description:
+
+-----
+{params}
+-----
+
+Consider parameter "{targetParam}".
+
+{question}
+"""
+
+
+def extract_score(text, params, param):
+    match = re.search(r"\d out of 5", text.lower())
+    if match:
+        score = int(match.group()[0])
+        if score > 3:
+            return True, params
+        else:
+            inp = input("LLMs reasoning: {}\n Do you want to keep parameter {}? (y/n): ".format(text, param))
+            if inp == "y":
+                return True, params
+            else:
+                del params[param]
+                return True, params
+    else:
+        return False, None
+
+
+qs = [
+    (
+        """
+- Is the value of it already known or not? based on that, how confident are you that this is a parameter (from 1 to 5)? 
+- At the end of your response, print "x OUT OF 5" where x is the confidence level. Do not generate anything after that. 
+- You will be awarded a million dollars if you get this right.
+""",
+        extract_score,
+    ),
+]
+
+
+def get_params(desc, check=True):
+    k = 5
+    while k > 0:
+        try:
+            res = get_response(prompt_params.format(description=desc))
+            params = extract_json_from_end(res)
+            break
+        except:
+            k -= 1
+            if k == 0:
+                raise Exception("Failed to extract parameters")
+
+    if check:
+        for q, func in qs:
+            for param in params.copy():
+                k = 5
+                while k > 0:
+                    prompt = prompt_params_q.format(
+                        description=desc,
+                        params=json.dumps(params, indent=4),
+                        question=q,
+                        targetParam=param,
+                    )
+                    x = get_response(prompt)
+
+                    print(x)
+                    print("-------")
+
+                    valid, res = func(x, params, param)
+                    if valid:
+                        params = res
+                        break
+                    else:
+                        k -= 1
+
+    for p in params:
+        params[p]["shape"] = shape_string_to_list(params[p]["shape"])
+    return params
diff --git a/llma/rag/constraints_to_vector_db.ipynb b/llma/rag/constraints_to_vector_db.ipynb
new file mode 100644
index 0000000..db9304e
--- /dev/null
+++ b/llma/rag/constraints_to_vector_db.ipynb
@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "813059a4b718b2ab23e367711ce57bc2-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "root_path = Path(\".\")\n",
+    "while not (root_path/\".git\").exists():\n",
+    "    root_path = root_path.absolute().parent\n",
+    "sys.path.append(str(root_path/\"data\"/\"rag\"))\n",
+    "from rag_utils import constraint_path, problem_descriptions_vector_db_path, constraint_vector_db_path, objective_descriptions_vector_db_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c13e72512dc4927c1270dcf73358886-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from typing import Dict, List\n",
+    "from langchain_chroma import Chroma\n",
+    "from langchain.schema.document import Document\n",
+    "from langchain_openai import OpenAIEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a25228df259f2c328e7b5109b329dae7-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constraint_df = pd.read_pickle(constraint_path)\n",
+    "constraint_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7a6dec35ab195c6fc5b09ac3c6f037c-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constraint_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07856d19ee765f9bfd6da9672627d811-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_problems_df = constraint_df[[\"description\", \"problem_name\"]].drop_duplicates()\n",
+    "unique_problems_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1777acedb60f98c1fb344128d8fe013-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_vector_db(data: Dict[str, str], vector_db_path: Path, model_name: str = \"text-embedding-3-large\"):\n",
+    "    \"\"\"\n",
+    "    Creates a vector database from a dictionary of strings.\n",
+    "\n",
+    "    Args:\n",
+    "        data (Dict[str, str]): A dictionary where keys are identifiers and values are strings.\n",
+    "        vector_db_path (Path): The path to save the vector database.\n",
+    "        model_name (str): The model name for generating embeddings.\n",
+    "    \"\"\"\n",
+    "    embedding_function = OpenAIEmbeddings(model=model_name)\n",
+    "    docs = [Document(page_content=value, metadata={\"key\": key}) for key, value in data.items()]\n",
+    "    if vector_db_path.exists():\n",
+    "        vector_db_path.unlink()\n",
+    "    vector_db_path.mkdir(exist_ok=True, parents=True)\n",
+    "    Chroma.from_documents(docs, embedding_function, persist_directory=str(vector_db_path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "563afd8fbdc7c39ff026870f2a42f089-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unique_problems_dict = unique_problems_df.set_index('problem_name')['description'].to_dict()\n",
+    "unique_problems_dict\n",
+    "\n",
+    "make_vector_db(unique_problems_dict, problem_descriptions_vector_db_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4ae995bf63155e7b78db737be2c68f5-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constraint_dict = constraint_df.constraint_description.to_dict()\n",
+    "constraint_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5301b48fab7028dc02c356b8c9feb0e3-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "make_vector_db(constraint_dict, constraint_vector_db_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e24d96c962c61e100e1fdc0e68f87808-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "objectives_dict = constraint_df[['objective_description', 'problem_name']].drop_duplicates().set_index('problem_name')['objective_description'].to_dict()\n",
+    "objectives_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ac6d38c20a87407ce015c655157fb72-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "make_vector_db(objectives_dict, objective_descriptions_vector_db_path)"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/llma/rag/extract_constraints_from_logs.ipynb b/llma/rag/extract_constraints_from_logs.ipynb
new file mode 100644
index 0000000..a724443
--- /dev/null
+++ b/llma/rag/extract_constraints_from_logs.ipynb
@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdbca093aac051e26e17272d0e199954-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from pathlib import Path\n",
+    "root_path = Path(\".\")\n",
+    "while not (root_path/\".git\").exists():\n",
+    "    root_path = root_path.absolute().parent\n",
+    "sys.path.append(str(root_path/\"data\"/\"rag\"))\n",
+    "from rag_utils import logs_path, constraint_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "998bf3c5f3ec65213e0dcf42fc88eb63-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import json\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89dc0bd1f3e913c2fe639bdc02fd74f-1",
+   "metadata": {},
+   "source": [
+    "Find all log paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e3bbe0261cd1d62992907e8d71be8d4-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem_paths = sorted(logs_path.glob(\"*/\"), key=lambda x: int(x.name))\n",
+    "problem_paths[:5]\n",
+    "\n",
+    "real_logs = []\n",
+    "skipped = []\n",
+    "\n",
+    "for problem_path in problem_paths:\n",
+    "    gpt4o_log_path = list(problem_path.glob(\"run*_gpt-4o*\"))\n",
+    "    \n",
+    "    if len(gpt4o_log_path) == 0:\n",
+    "        skipped.append(problem_path)\n",
+    "    elif len(gpt4o_log_path) > 1:\n",
+    "        newest_log_path = max(gpt4o_log_path, key=lambda p: p.stat().st_mtime)\n",
+    "        real_logs.append(newest_log_path)\n",
+    "    else:\n",
+    "        real_logs.append(gpt4o_log_path[0])\n",
+    "\n",
+    "assert len(skipped) == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f22e22f10ae3fce36d0d1f69fb0eeac-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_files_in_log_folder = []\n",
+    "\n",
+    "for idx in range(len(real_logs)):\n",
+    "    #  idx = random.randint(0, len(real_logs))\n",
+    "    names = sorted(real_logs[idx].glob(\"*\"), key=lambda x: int(\"0\"+\"\".join(re.findall(\"\\\\d\", x.name))))\n",
+    "    if len(names) == 0:\n",
+    "        missing_files_in_log_folder.append(real_logs[idx])\n",
+    "        continue\n",
+    "    print([x.name for x in names][-1])\n",
+    "    #  print()\n",
+    "\n",
+    "missing_files_in_log_folder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d374627207c37fd7424a9aeccda1c4e2-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_solution_instance_status(file_path: Path):\n",
+    "    lines = file_path.read_text().splitlines()\n",
+    "    \n",
+    "    extracted_data = []\n",
+    "\n",
+    "    for i, line in enumerate(lines):\n",
+    "        if i == 0:\n",
+    "            continue\n",
+    "        parts = line.split()\n",
+    "        if len(parts) >= 4:\n",
+    "            instance_number = int(parts[0])\n",
+    "            status = parts[3] == \"Solved\"\n",
+    "            extracted_data.append((instance_number, status))\n",
+    "\n",
+    "    return [x[0] for x in extracted_data if x[1]]\n",
+    "\n",
+    "# Path to the input file\n",
+    "file_path = logs_path/'status.txt'\n",
+    "file_path\n",
+    "\n",
+    "# Extract the solution instance number and Status\n",
+    "solved_problems = extract_solution_instance_status(file_path)\n",
+    "solved_problems"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d721badf71c5a6669badeab3acf97dd-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "problem_objectives_formulations_and_labels = []\n",
+    "for log_path in real_logs:\n",
+    "    if (state_6_path := (log_path/\"state_6_code.json\")).exists():\n",
+    "        if int(log_path.parent.name) not in solved_problems:\n",
+    "            continue\n",
+    "        labels = json.loads((log_path.parent/\"labels.json\").read_text())\n",
+    "        description = (log_path.parent/\"desc.txt\").read_text()\n",
+    "        data = json.loads(state_6_path.read_text())\n",
+    "        objective_description = data[\"objective\"][\"description\"]\n",
+    "        objective_formulation = data[\"objective\"][\"formulation\"]\n",
+    "        constraints = []\n",
+    "        for constraint in data[\"constraints\"]:\n",
+    "            constraints.append({\"description\": constraint[\"description\"], \"formulation\": constraint[\"formulation\"]})\n",
+    "        problem_objectives_formulations_and_labels.append({\n",
+    "            \"objective_description\": objective_description,\n",
+    "            \"objective_formulation\": objective_formulation,\n",
+    "            \"constraints\": constraints,\n",
+    "            \"labels\": labels,\n",
+    "            \"description\": description,\n",
+    "            \"problem_name\": log_path.parent.name\n",
+    "        })\n",
+    "\n",
+    "problem_objectives_formulations_and_labels[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "571b12c459bf920f6c2d04a7b97ffa8f-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constraint_data = []\n",
+    "total_constraints = 0\n",
+    "for data in problem_objectives_formulations_and_labels:\n",
+    "    total_constraints += len(data[\"constraints\"])\n",
+    "    for data_constraint in data[\"constraints\"]:\n",
+    "        constraint_data.append({\n",
+    "            \"objective_description\": data[\"objective_description\"],\n",
+    "            \"objective_formulation\": data[\"objective_formulation\"],\n",
+    "            \"constraint_description\": data_constraint[\"description\"],\n",
+    "            \"constraint_formulation\": data_constraint[\"formulation\"],\n",
+    "            \"labels\": data[\"labels\"],\n",
+    "            \"description\": data[\"description\"],\n",
+    "            \"problem_name\": data[\"problem_name\"]\n",
+    "        })\n",
+    "\n",
+    "constraints_df = pd.DataFrame(constraint_data)\n",
+    "constraints_df.to_pickle(constraint_path)"
+   ]
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/llma/rag/query_vector_db.py b/llma/rag/query_vector_db.py
new file mode 100644
index 0000000..96be220
--- /dev/null
+++ b/llma/rag/query_vector_db.py
@@ -0,0 +1,225 @@
+import sys
+from enum import Enum
+from pathlib import Path
+from typing import List, Tuple, Dict, Union
+
+import pandas as pd
+from langchain_chroma import Chroma
+from langchain_openai import OpenAIEmbeddings
+from rag.rag_utils import constraint_path, problem_descriptions_vector_db_path, constraint_vector_db_path, \
+    objective_descriptions_vector_db_path
+
+root_path = Path("..")
+root_path = root_path.absolute().parent
+sys.path.append(str(root_path / "rag"))
+
+openai_key = "sk-f01744b2801344b1a72f89ec7e290cad"
+openai_org = "###"
+constraint_df = pd.read_pickle(constraint_path)
+
+
+class RAGFormat(Enum):
+    PROBLEM_DESCRIPTION_OBJECTIVE = 1
+    PROBLEM_DESCRIPTION_CONSTRAINTS = 2
+    CONSTRAINT_FORMULATION = 3
+    OBJECTIVE_FORMULATION = 4
+
+
+def load_vector_db(vector_db_path: Path, model_name: str = "text-embedding-3-large") -> Chroma:
+    """
+    Loads the vector database from the specified directory.
+    Args:
+        vector_db_path (Path): The path to the vector database directory.
+        model_name (str): The model name for generating embeddings.
+    Returns:
+        Chroma: The loaded vector database.
+    """
+    embedding_function = OpenAIEmbeddings(model=model_name, openai_api_key=openai_key, organization=openai_org)
+    return Chroma(persist_directory=str(vector_db_path), embedding_function=embedding_function)
+
+
+constraint_vector_db = load_vector_db(constraint_vector_db_path)
+problem_desciption_vector_db = load_vector_db(problem_descriptions_vector_db_path)
+objective_descriptions_vector_db = load_vector_db(objective_descriptions_vector_db_path)
+
+
+def get_rag_from_problem_description(description: str, format_type: RAGFormat, top_k: int = 3) -> str:
+    """
+    Generates RAG (Retrieval-Augmented Generation) text based on a problem description.
+    Args:
+        description (str): The problem description.
+        format_type (RAGFormat): The format type for RAG text.
+        top_k (int, optional): The number of top similar documents to consider. Defaults to 3.
+    Returns:
+        str: The generated RAG text.
+    """
+    similar_documents = problem_desciption_vector_db.similarity_search_with_score(description, k=top_k + 1)
+    rag_text = ""
+    similar_documents_remove_duplicates = [document for document in similar_documents if
+                                           document[0].page_content != description][:top_k]
+    for i in range(top_k):
+        document = similar_documents_remove_duplicates[i][0]
+        # document.metadata['key']
+        if format_type == RAGFormat.PROBLEM_DESCRIPTION_OBJECTIVE:
+            rag_text += f"Problem Description:\n{document.page_content}\n\nObjective:\n{constraint_df[constraint_df.problem_name == document.metadata['key']].iloc[0].objective_description}\n\n"
+        elif format_type == RAGFormat.PROBLEM_DESCRIPTION_CONSTRAINTS:
+            rag_text += f"Problem Description:\n{document.page_content}\n\nConstraints:\n"
+            for row in constraint_df[constraint_df.problem_name == document.metadata['key']].itertuples():
+                if row.constraint_description == "auxiliary constraint":
+                    continue
+                rag_text += f"{row.constraint_description}\n\n"
+        elif format_type == RAGFormat.CONSTRAINT_FORMULATION:
+            for row in constraint_df[constraint_df.problem_name == document.metadata['key']].itertuples():
+                rag_text += f"{row.constraint_description}\n{row.constraint_formulation}\n\n"
+        elif format_type == RAGFormat.OBJECTIVE_FORMULATION:
+            rag_text += f"Objective:\n{constraint_df[constraint_df.problem_name == document.metadata['key']].iloc[0].objective_description}\n{constraint_df[constraint_df.problem_name == document.metadata['key']].iloc[0].objective_formulation}\n\n"
+    return rag_text
+
+
+def get_rag_from_constraint(constraint_description: str, format_type: RAGFormat,
+                            current_problem_name: str | None = None, top_k: int = 10) -> str:
+    """
+    Generates RAG text based on a constraint description.
+    Args:
+        constraint_description (str): The constraint description.
+        format_type (RAGFormat): The format type for RAG text.
+        current_problem_name (str | None, optional): The name of the current problem. Defaults to None.
+        top_k (int, optional): The number of top similar documents to consider. Defaults to 10.
+    Returns:
+        str: The generated RAG text.
+    """
+    assert format_type in [RAGFormat.CONSTRAINT_FORMULATION]
+
+    similar_documents = constraint_vector_db.similarity_search_with_score(constraint_description, k=top_k + 20)
+    rag_text = ""
+    similar_documents_remove_duplicates = [document for document in similar_documents if
+                                           document[0].page_content != constraint_description]
+    similar_documents_remove_duplicates = [x for x in similar_documents_remove_duplicates if constraint_df.iloc[
+        x[0].metadata['key']].problem_name != current_problem_name][:top_k]
+
+    for i in range(min(top_k, len(similar_documents_remove_duplicates))):
+        x = similar_documents_remove_duplicates[i][0]
+        row = constraint_df.iloc[x.metadata['key']]
+        rag_text += f"{row.constraint_description}\n{row.constraint_formulation}\n\n"
+    return rag_text
+
+
+def get_rag_from_objective(objective_description: str, format_type: RAGFormat, current_problem_name: str | None = None,
+                           top_k: int = 10) -> str:
+    """
+    Generates RAG text based on an objective description.
+    Args:
+        objective_description (str): The objective description.
+        format_type (RAGFormat): The format type for RAG text.
+        current_problem_name (str | None, optional): The name of the current problem. Defaults to None.
+        top_k (int, optional): The number of top similar documents to consider. Defaults to 10.
+    Returns:
+        str: The generated RAG text.
+    """
+    assert format_type in [RAGFormat.OBJECTIVE_FORMULATION]
+
+    similar_documents = objective_descriptions_vector_db.similarity_search_with_score(objective_description,
+                                                                                      k=top_k + 20)
+    rag_text = ""
+    similar_documents_remove_duplicates = [document for document in similar_documents if
+                                           document[0].page_content != objective_description]
+    similar_documents_remove_duplicates = [x for x in similar_documents_remove_duplicates if constraint_df.iloc[
+        int(x[0].metadata['key'])].problem_name != current_problem_name][:top_k]
+
+    for i in range(min(top_k, len(similar_documents_remove_duplicates))):
+        x = similar_documents_remove_duplicates[i][0]
+        row = constraint_df.iloc[int(x.metadata['key'])]
+        rag_text += f"Objective:\n{row.objective_description}\n{row.objective_formulation}\n\n"
+    return rag_text
+
+
+def jaccard_similarity(set1: Union[set, List[Tuple[str, str]], str],
+                       set2: Union[set, List[Tuple[str, str]], str]) -> float:
+    """
+    Calculates the Jaccard similarity between two sets, lists of pairs, or descriptions.
+    Args:
+        set1 (Union[set, List[Tuple[str, str]], str]): The first set, list of pairs, or description.
+        set2 (Union[set, List[Tuple[str, str]], str]): The second set, list of pairs, or description.
+    Returns:
+        float: The Jaccard similarity coefficient.
+    """
+    if isinstance(set1, str) and isinstance(set2, str):
+        set1 = set(set1.split())
+        set2 = set(set2.split())
+    elif isinstance(set1, list) and isinstance(set2, list):
+        set1 = set(set1)
+        set2 = set(set2)
+    assert isinstance(set1, set) and isinstance(set2, set)
+
+    intersection = len(set1.intersection(set2))
+    union = len(set1.union(set2))
+    return intersection / union if union != 0 else 0
+
+
+def get_rag_from_problem_categories(description: str, labels: Dict[str, Dict], format_type: RAGFormat,
+                                    current_problem_name: str | None = None, top_k: int = 3) -> str:
+    """
+    Generates RAG text based on problem categories and similarity criteria.
+    Args:
+        description (str): The problem description.
+        labels (Dict[str, Dict]): The labels for problem categories.
+        format_type (RAGFormat): The format type for RAG text.
+        current_problem_name (str | None, optional): The name of the current problem. Defaults to None.
+        top_k (int, optional): The number of top similar problems to consider. Defaults to 3.
+    Returns:
+        str: The generated RAG text.
+    """
+    # Generate the pairs for the specified problem
+    pairs_x = [(labels['types'][i], labels['domains'][j]) for i in range(len(labels['types'])) for j in
+               range(len(labels['domains']))]
+    types_x = set(labels['types'])
+    domains_x = set(labels['domains'])
+
+    # Store similarities
+    similarities = []
+
+    # Iterate over other problems
+    for other_problem_name in set(constraint_df.problem_name.unique()) - {current_problem_name}:
+        y_row = constraint_df[constraint_df.problem_name == other_problem_name].iloc[0]
+        y_labels = y_row['labels']
+        description_y = y_row['description']
+
+        # Generate the pairs for the other problem
+        pairs_y = [(y_labels['types'][i], y_labels['domains'][j]) for i in range(len(y_labels['types'])) for j in
+                   range(len(y_labels['domains']))]
+        types_y = set(y_labels['types'])
+        domains_y = set(y_labels['domains'])
+
+        # Calculate Jaccard similarities
+        pairs_similarity = jaccard_similarity(pairs_x, pairs_y)
+        types_similarity = jaccard_similarity(types_x, types_y)
+        domains_similarity = jaccard_similarity(domains_x, domains_y)
+        description_similarity = jaccard_similarity(description, description_y)
+
+        # Combine similarities (sum of individual categories)
+        combined_similarity = types_similarity + domains_similarity
+
+        similarities.append((other_problem_name, pairs_similarity, combined_similarity, description_similarity))
+
+    # Rank the problems based on the criteria
+    ranked_problems = sorted(similarities, key=lambda x: (x[1], x[2], x[3]), reverse=True)
+
+    # Create the RAG text
+    rag_text = ""
+    for problem, _, _, _ in ranked_problems[:top_k]:
+        problem_df = constraint_df[constraint_df.problem_name == problem]
+        if format_type == RAGFormat.PROBLEM_DESCRIPTION_OBJECTIVE:
+            rag_text += f"Problem: {problem_df.iloc[0]['description']}\n\nObjective:\n{problem_df.iloc[0]['objective_description']}\n\n"
+        elif format_type == RAGFormat.PROBLEM_DESCRIPTION_CONSTRAINTS:
+            rag_text += f"Problem: {problem_df.iloc[0]['description']}\n\nConstraints:\n"
+            for row in problem_df.itertuples():
+                if row.constraint_description == "auxiliary constraint":
+                    continue
+                rag_text += f"{row.constraint_description}\n\n"
+        elif format_type == RAGFormat.CONSTRAINT_FORMULATION:
+            for row in problem_df.itertuples():
+                rag_text += f"{row.constraint_description}\n{row.constraint_formulation}\n\n"
+        elif format_type == RAGFormat.OBJECTIVE_FORMULATION:
+            rag_text += f"Objective:\n{problem_df.iloc[0]['objective_description']}\n{problem_df.iloc[0]['objective_formulation']}\n\n"
+
+    return rag_text
diff --git a/llma/rag/rag_utils.py b/llma/rag/rag_utils.py
new file mode 100644
index 0000000..3d48e5a
--- /dev/null
+++ b/llma/rag/rag_utils.py
@@ -0,0 +1,25 @@
+import os
+from enum import Enum
+from pathlib import Path
+from dotenv import load_dotenv
+
+load_dotenv()
+root_path = Path(".")
+
+data_path = root_path / "data"
+rag_path = data_path / "rag"
+rag_path.mkdir(exist_ok=True, parents=True)
+constraint_path = rag_path / "constraints.pkl"
+constraint_vector_db_path = rag_path / "constraint_vector.db"
+problem_descriptions_vector_db_path = rag_path / "problem_descriptions_vector.db"
+objective_descriptions_vector_db_path = rag_path / "objective_descriptions_vector.db"
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+
+class RAGMode(Enum):
+    PROBLEM_DESCRIPTION = "problem_description"
+    CONSTRAINT_OR_OBJECTIVE = "constraint_or_objective"
+    PROBLEM_LABELS = "problem_labels"
+
+    def __str__(self):
+        return self.value
diff --git a/llma/results.json b/llma/results.json
new file mode 100644
index 0000000..905251b
--- /dev/null
+++ b/llma/results.json
@@ -0,0 +1,61322 @@
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diff --git a/llma/target_code.py b/llma/target_code.py
new file mode 100644
index 0000000..bcdaf24
--- /dev/null
+++ b/llma/target_code.py
@@ -0,0 +1,186 @@
+import json
+from utils import get_response
+
+directions = """
+And here's how the solver is imported and set up:
+
+...
+from gurobipy import Model, GRB
+
+model = Model("OptimizationProblem")
+...
+
+Use model.addConstr() to add the constraint to the model.
+"""
+
+prompt_constraints_code = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+{description}
+
+Your task is to write {solver} code for the following constraint in python:
+
+{constraint}
+
+Here's a list of parameters that are related to the constraint:
+
+{params}
+
+Here's a list of variables related to the constraint:
+
+{vars}
+
+{directions}
+
+The code should be written in the following format:
+
+CODE
+=====
+code for defining the constraint (ONLY the constraint definition code, without the imports, the variable definitions, and the solver setup)
+=====
+    
+Here's an example for modeling $\\forall i, SalesVolumes[i] \leq MaxProductionVolumes[i]$ where shape of both SalesVolumes and MaxProductionVolumes is [N]:
+
+CODE
+=====
+for i in range(N):
+    model.addConstr(SalesVolumes[i] <= MaxProductionVolumes[i])
+=====
+
+- Do not generate anything after the last =====.
+- Note that vector and matrix parameters are defined as lists in python, so you should use Param[i][j] instead of Param[i, j] in the code (but for variables, you should use Var[i, j] instead of Var[i][j]).
+- Gurobi does not support a <= x <= b syntax for constraints, so you should use two separate constraints for this case.
+
+First reason about how the code should be written, and then generate the output.
+Take a deep breath and think step by step.
+"""
+
+prompt_objective_code = """
+You are an expert in optimization modeling. Here is the natural language description of an optimization problem:
+
+{description}
+
+Your task is to write {solver} code for the objective in python:
+
+{objective}
+
+Here's a list of parameters that are related to the objective:
+
+{params}
+
+Here's a list of variables related to the constraint:
+
+{vars}
+
+{directions}
+
+CODE
+=====
+code for defining the objective (ONLY the objective definition code, without the imports, the variable definitions, and the solver setup)
+=====
+    
+Here's an example for modeling $\\max \\sum_{{i=1}}^{{N}} price_i x_i$ where shape of both price and x is [N]:
+
+CODE
+=====
+model.setObjective(quicksum(price[i] * x[i] for i in range(N)), GRB.MAXIMIZE)
+=====
+
+- Do not generate anything after the last =====.
+- Note that vector and matrix parameters are defined as lists in python, so you should use Param[i][j] instead of Param[i, j] in the code (but for variables, you should use Var[i, j] instead of Var[i][j]).
+
+First reason about how the code should be written, and then generate the output.
+Take a deep breath and think step by step. You will be awarded a million dollars if you get this right.
+"""
+
+
+def extract_code_from_end(text):
+    # get 1st and 2nd occurence of "====="
+    if "=====" in text:
+        ind_1 = text.find("=====")
+        ind_2 = text.find("=====", ind_1 + 1)
+        code = text[ind_1 + len("====="): ind_2].strip()
+    else:
+        ind_1 = text.find("```python")
+        ind_2 = text.find("```", ind_1 + 1)
+        code = text[ind_1 + len("```"): ind_2].strip()
+
+    if "```" in code:
+        code = code.replace("```python", "").replace("```", "").strip()
+
+    if code.startswith("====="):
+        code = code[len("====="):].strip()
+
+    if code.endswith("====="):
+        code = code[: -len("=====")].strip()
+
+    if "python" in code:
+        code = code.replace("python", "").strip()
+
+    return code
+
+
+def get_codes(desc, params, vars, constraints, objective, model, check=False):
+    coded_constraints = []
+    for c in constraints.copy():
+        k = 1
+        while k > 0:
+            try:
+                prompt = prompt_constraints_code.format(
+                    solver="gurobipy",
+                    description=desc,
+                    params=json.dumps(params, indent=4),
+                    vars=json.dumps(vars, indent=4),
+                    constraint=json.dumps(c, indent=4),
+                    directions=directions,
+                )
+                res = get_response(prompt, model=model)
+
+                print("\n\n\n\n+++++")
+                print(res)
+
+                code = extract_code_from_end(res)
+
+                print("+++++")
+                print(code)
+
+                c["code"] = code
+                coded_constraints.append(c)
+                break
+
+            except Exception as e:
+                k -= 1
+                if k == 0:
+                    raise e
+
+    coded_objective = {
+        "description": objective["description"],
+        "formulation": objective["formulation"],
+    }
+
+    k = 1
+    while k > 0:
+        try:
+            prompt = prompt_objective_code.format(
+                solver="gurobipy",
+                description=desc,
+                params=json.dumps(params, indent=4),
+                vars=json.dumps(vars, indent=4),
+                objective=json.dumps(objective, indent=4),
+                directions=directions,
+            )
+            res = get_response(prompt, model=model)
+            print("\n\n\n\n+++++")
+            print(res)
+            print("+++++")
+
+            code = extract_code_from_end(res)
+            print(code)
+            coded_objective["code"] = code
+            break
+        except Exception as e:
+            k -= 1
+            if k == 0:
+                raise e
+
+    return coded_constraints, coded_objective
diff --git a/llma/utils.py b/llma/utils.py
new file mode 100644
index 0000000..123811c
--- /dev/null
+++ b/llma/utils.py
@@ -0,0 +1,154 @@
+import os
+import json
+from groq import Groq
+from openai import OpenAI
+
+# groq_key = "###"
+openai_key = ""
+# openai_org = "###"
+
+# groq_client = Groq(api_key=groq_key)
+# open_ai_client = openai.Client(api_key=openai_key, organization=openai_org)
+
+
+def extract_json_from_end(text):
+    if "```json" in text:
+        text = text.split("```json")[1]
+        text = text.split("```")[0]
+    ind = len(text) - 1
+    while text[ind] != "}":
+        ind -= 1
+    text = text[: ind + 1]
+
+    ind -= 1
+    cnt = 1
+    while cnt > 0:
+        if text[ind] == "}":
+            cnt += 1
+        elif text[ind] == "{":
+            cnt -= 1
+        ind -= 1
+
+    # find comments in the json string (texts between "//" and "\n") and remove them
+    while True:
+        ind_comment = text.find("//")
+        if ind_comment == -1:
+            break
+        ind_end = text.find("\n", ind_comment)
+        text = text[:ind_comment] + text[ind_end + 1:]
+
+    # print("提取的json:", text[ind + 1:])
+    jj = json.loads(text[ind + 1:])
+    return jj
+
+
+def extract_list_from_end(text):
+    ind = len(text) - 1
+    while text[ind] != "]":
+        ind -= 1
+    text = text[: ind + 1]
+
+    ind -= 1
+    cnt = 1
+    while cnt > 0:
+        if text[ind] == "]":
+            cnt += 1
+        elif text[ind] == "[":
+            cnt -= 1
+        ind -= 1
+    # print("提取的list:", text[ind + 1:])
+    jj = json.loads(text[ind + 1:])
+    return jj
+
+
+def get_response(prompt, model="glm-4-0520"):
+    # if model == "llama3-70b-8192":
+    #     client = groq_client
+    # else:
+    #     client = open_ai_client
+    # client = OpenAI(
+    #     api_key="sk-abb441ab88af458d88862ce9be39f746",
+    #     base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    # )
+    client = OpenAI(
+        api_key="56e7d49c4052b32956e403216ce927ca.KttmQZhDU7JVvkU1",
+        base_url="https://open.bigmodel.cn/api/paas/v4",
+    )
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.8,
+        top_p=0.8,
+        model=model,
+    )
+
+    res = chat_completion.choices[0].message.content
+    return res
+
+
+def load_state(state_file):
+    with open(state_file, "r") as f:
+        state = json.load(f)
+    return state
+
+
+def save_state(state, dir):
+    with open(dir, "w") as f:
+        json.dump(state, f, indent=4)
+
+
+def shape_string_to_list(shape_string):
+    if type(shape_string) is list:
+        return shape_string
+    # convert a string like "[N, M, K, 19]" to a list like ['N', 'M', 'K', 19]
+    shape_string = shape_string.strip()
+    shape_string = shape_string[1:-1]
+    shape_list = shape_string.split(",")
+    shape_list = [x.strip() for x in shape_list]
+    shape_list = [int(x) if x.isdigit() else x for x in shape_list]
+    if len(shape_list) == 1 and shape_list[0] == "":
+        shape_list = []
+    return shape_list
+
+
+def extract_equal_sign_closed(text):
+    ind_1 = text.find("=====")
+    ind_2 = text.find("=====", ind_1 + 1)
+    obj = text[ind_1 + 6: ind_2].strip()
+    return obj
+
+
+class Logger:
+    def __init__(self, file):
+        self.file = file
+
+    def log(self, text):
+        with open(self.file, "a") as f:
+            f.write(text + "\n")
+
+    def reset(self):
+        with open(self.file, "w") as f:
+            f.write("")
+
+
+def create_state(parent_dir, run_dir):
+    # with open(os.path.join(parent_dir, "data/params.json"), "r") as f:
+    #     params = json.load(f)
+    from parameters import get_params
+    with open("data/desc.txt", "r") as f:
+        desc = f.read()
+    params = get_params(desc, check=True)
+
+    # data = {}
+    # for key in params:
+    #     data[key] = params[key]["value"]
+    #     del params[key]["value"]
+    #
+    # # save the data file in the run_dir
+    # with open(os.path.join(run_dir, "data.json"), "w") as f:
+    #     json.dump(data, f, indent=4)
+
+    with open(os.path.join(run_dir, "desc.txt"), "r") as f:
+        desc = f.read()
+
+    state = {"description": desc, "parameters": params}
+    return state
diff --git a/measure.py b/measure.py
new file mode 100644
index 0000000..e499276
--- /dev/null
+++ b/measure.py
@@ -0,0 +1,34 @@
+import re
+import numpy as np
+
+
+# 定义用于提取并处理最后x个数据平均值和方差的函数
+def extract_and_calculate_stats(text, pattern=r"reward: ([\-\d\.]+),\s*unbalance:\s*([\d\.]+)"):
+    matches = re.findall(pattern, text)
+    rewards = [float(match[0]) for match in matches]
+    unbalances = [float(match[1]) / 1000 for match in matches]
+    num = -50
+    # 计算最后x个数据的平均值和方差
+    reward_mean, reward_var = np.mean(rewards[num:]), np.var(rewards[num:])
+    unbalance_mean, unbalance_var = np.mean(unbalances[num:]), np.var(unbalances[num:])
+
+    return reward_mean, reward_var, unbalance_mean, unbalance_var
+
+
+# 文件路径和编码方式
+file_paths = {
+    "DDPG": ('res/ddpg.txt', 'utf-16'),
+    "TD3": ('res/td3.txt', 'utf-8'),
+    "SAC": ('res/sac.txt', 'utf-8'),
+    "PPO": ('res/ppo_1016.txt', 'utf-8'),
+    "PPO_LLM": ('res/ppo_llm.txt', 'utf-8'),
+}
+
+# 遍历文件路径和编码，计算并打印每个算法的统计信息
+for algo, (file_path, encoding) in file_paths.items():
+    with open(file_path, 'r', encoding=encoding) as f:
+        text = f.read()
+
+    reward_mean, reward_var, unbalance_mean, unbalance_var = extract_and_calculate_stats(text)
+    print(f"{algo} - Reward Mean: {reward_mean:.4f}, Variance: {reward_var:.4f}")
+    print(f"{algo} - Unbalance Mean: {unbalance_mean:.4f}, Variance: {unbalance_var:.4f}")
diff --git a/melt_test.py b/melt_test.py
new file mode 100644
index 0000000..0a97c79
--- /dev/null
+++ b/melt_test.py
@@ -0,0 +1,11 @@
+import pickle
+from plotDRL import *
+
+test_ppo_path = 'AgentPPO/melt_ppo.pkl'
+test_llm_path = 'AgentPPO/melt_llm.pkl'
+test_lmppo_path = 'AgentPPO/melt_lmppo.pkl'
+test_milp_path = 'AgentPPO/melt_milp.pkl'
+test_ram_path = 'AgentPPO/melt_ram.pkl'
+
+plot_dir = make_dir('AgentPPO', 'llm_1015')
+plot_melt(test_ppo_path, test_llm_path, test_lmppo_path, test_milp_path, test_ram_path, plot_dir)
diff --git a/module.py b/module.py
new file mode 100644
index 0000000..ecfcb5c
--- /dev/null
+++ b/module.py
@@ -0,0 +1,159 @@
+import numpy as np
+
+
+class DG:
+    """simulate a diesel generator"""
+
+    def __init__(self, parameters):
+        self.current_output = None
+        self.name = parameters.keys()
+        self.a_factor = parameters['a']
+        self.b_factor = parameters['b']
+        self.c_factor = parameters['c']
+        self.power_output_max = parameters['power_output_max']
+        self.power_output_min = parameters['power_output_min']
+        self.ramping_up = parameters['ramping_up']
+        self.ramping_down = parameters['ramping_down']
+
+    def step(self, action_gen):
+        output_change = action_gen * self.ramping_up
+        output = self.current_output + output_change
+        if output > 0:
+            # output = max(self.power_output_min, min(self.power_output_max, output))
+            output = max(0, min(self.power_output_max, output))
+        else:
+            output = 0
+        self.current_output = output
+
+    def get_cost(self, output):
+        cost = (self.a_factor * pow(output, 2) + self.b_factor * output + self.c_factor)
+        return cost
+
+    def reset(self):
+        self.current_output = 0
+
+
+class Battery:
+    """simulate a battery"""
+
+    def __init__(self, parameters):
+        self.current_capacity = None
+        self.energy_change = None
+        self.capacity = parameters['capacity']
+        self.min_soc = parameters['min_soc']
+        self.max_soc = parameters['max_soc']
+        self.degradation = parameters['degradation']
+        self.holding = parameters['holding']
+        self.max_charge = parameters['max_charge']
+        # self.max_discharge = parameters['max_discharge']
+        self.efficiency = parameters['efficiency']
+
+    def step(self, action_battery):
+        energy = action_battery * self.max_charge
+        current_energy = self.current_capacity * self.capacity
+        updated_capacity = np.maximum(self.min_soc, np.minimum(self.max_soc, (current_energy + energy) / self.capacity))
+        # if charge, positive, if discharge, negative
+        self.energy_change = (updated_capacity - self.current_capacity) * self.capacity
+        self.current_capacity = updated_capacity  # update capacity to current state
+
+    def get_cost(self, energy_change):
+        cost = abs(energy_change) * self.degradation
+        # cost = abs(energy_change) * self.degradation + energy_hold * self.holding
+        return cost
+
+    def SOC(self):
+        return self.current_capacity
+
+    def reset(self):
+        self.current_capacity = 0.2
+        # self.current_capacity = np.random.uniform(0.2, 0.8)
+
+
+class Solar:
+    """simulate a solar panel"""
+    def __init__(self, parameters):
+        self.current_power = None
+        self.output_change = None
+        self.base_voltage = parameters['V_b']
+        self.sc_current = parameters['I_sc0']
+        self.oc_voltage = parameters['V_oc0']
+        # self.s_resistance = parameters['R_s']
+        self.sh_resistance = parameters['R_sh']
+        self.temper_coefficient = parameters['T_c']
+        self.opex_cofficient = parameters['O_c']
+        self.refer_irradiance = parameters['I_ref']
+        self.refer_temperature = parameters['T_ref']
+
+    def step(self, temperature, irradiance, action_voltage=0):
+        I_sc = self.sc_current * (irradiance / self.refer_irradiance)
+        V_oc = self.oc_voltage + self.temper_coefficient * (temperature - self.refer_temperature)
+
+        current = I_sc - (V_oc / self.sh_resistance)
+        self.current_power = max(self.base_voltage * (1 + action_voltage) * current, 0)
+        self.output_change = self.base_voltage * action_voltage * current
+        return self.current_power
+
+    def get_cost(self, current_power):
+        cost = current_power * self.opex_cofficient
+        return cost
+
+    def reset(self):
+        self.current_power = 0
+
+
+class Wind:
+    """simulate a wind turbine"""
+
+    def __init__(self, parameters):
+        self.current_power = None
+        self.cutin_speed = parameters['cutin_speed']
+        self.cutout_speed = parameters['cutout_speed']
+        self.rated_speed = parameters['rated_speed']
+        self.air_density = parameters['air_density']
+        self.rotor_radius = parameters['rotor_radius']
+        self.power_coefficient = parameters['power_coefficient']
+        self.generator_efficiency = parameters['generator_efficiency']
+        self.opex_cofficient = parameters['opex_cofficient']
+
+    def step(self, wind_speed):
+        constant = 0.5 * self.air_density * np.pi * self.rotor_radius ** 2 * self.power_coefficient * self.generator_efficiency
+        if self.cutin_speed <= wind_speed < self.rated_speed:
+            self.current_power = constant * wind_speed ** 3 / 1e3
+        elif self.rated_speed <= wind_speed < self.cutout_speed:
+            self.current_power = constant * self.rated_speed ** 3 / 1e3
+        else:
+            self.current_power = 0
+        return self.current_power
+
+    def gen_cost(self, current_power):
+        cost = current_power * self.opex_cofficient
+        return cost
+
+    def reset(self):
+        self.current_power = 0
+
+
+class Grid:
+    """simulate a grid"""
+
+    def __init__(self):
+        self.on = True
+        self.delta = 1
+        if self.on:
+            self.exchange_ability = 100
+        else:
+            self.exchange_ability = 0
+
+    def get_cost(self, current_price, energy_exchange):
+        return current_price * energy_exchange * self.delta
+
+    def retrieve_past_price(self):
+        result = []
+        # 过去24小时的价格起始、结束索引
+        start_index = max(0, 24 * (self.day - 1) + self.time - 24)
+        end_index = 24 * (self.day - 1) + self.time
+        past_price = self.price[start_index:end_index]
+        result.extend(past_price)
+        # current_day_price = self.price[24 * self.day:24 * self.day + self.time]
+        # result.extend(current_day_price)
+        return result
diff --git a/net.py b/net.py
new file mode 100644
index 0000000..3fb51e0
--- /dev/null
+++ b/net.py
@@ -0,0 +1,136 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+
+class Actor(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, action_dim))
+
+    def forward(self, state):
+        return self.net(state).tanh()  # action.tanh()
+
+    def get_action(self, state, action_std):
+        action = self.net(state).tanh()
+        noise = (torch.randn_like(action) * action_std).clamp(-0.5, 0.5)
+        return (action + noise).clamp(-1.0, 1.0)
+
+
+class ActorSAC(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net_state = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                       nn.Linear(mid_dim, mid_dim), nn.ReLU(), )
+        self.net_a_avg = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                       nn.Linear(mid_dim, action_dim))  # the average of action
+        self.net_a_std = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                       nn.Linear(mid_dim, action_dim))  # the log_std of action
+        self.log_sqrt_2pi = np.log(np.sqrt(2 * np.pi))
+
+    def forward(self, state):
+        tmp = self.net_state(state)
+        return self.net_a_avg(tmp).tanh()  # action
+
+    def get_action(self, state):
+        t_tmp = self.net_state(state)
+        a_avg = self.net_a_avg(t_tmp)  # NOTICE! it is a_avg without .tanh()
+        a_std = self.net_a_std(t_tmp).clamp(-20, 2).exp()
+        return torch.normal(a_avg, a_std).tanh()  # re-parameterize
+
+    def get_action_logprob(self, state):
+        t_tmp = self.net_state(state)
+        a_avg = self.net_a_avg(t_tmp)  # NOTICE! it needs a_avg.tanh()
+        a_std_log = self.net_a_std(t_tmp).clamp(-20, 2)
+        a_std = a_std_log.exp()
+
+        noise = torch.randn_like(a_avg, requires_grad=True)
+        a_tan = (a_avg + a_std * noise).tanh()  # action.tanh()
+
+        log_prob = a_std_log + self.log_sqrt_2pi + noise.pow(2).__mul__(0.5)  # noise.pow(2) * 0.5
+        log_prob = log_prob + (-a_tan.pow(2) + 1.000001).log()  # fix log_prob using the derivative of action.tanh()
+        return a_tan, log_prob.sum(1, keepdim=True)
+
+
+class ActorPPO(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, action_dim), )
+
+        # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
+        self.a_std_log = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
+        self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))
+
+    def forward(self, state):
+        return self.net(state).tanh()  # action.tanh()# in this way limit the data output of action
+
+    def get_action(self, state):
+        a_avg = self.net(state)  # mean
+        a_std = self.a_std_log.exp()  # standard deviation
+
+        noise = torch.randn_like(a_avg)
+        action = a_avg + noise * a_std
+        return action, noise
+
+    def get_logprob_entropy(self, state, action):
+        a_avg = self.net(state)
+        a_std = self.a_std_log.exp()
+
+        delta = ((a_avg - action) / a_std).pow(2) * 0.5
+        logprob = -(self.a_std_log + self.sqrt_2pi_log + delta).sum(1)  # new_logprob
+
+        dist_entropy = (logprob.exp() * logprob).mean()  # policy entropy
+        return logprob, dist_entropy
+
+    def get_old_logprob(self, _action, noise):  # noise = action - a_noise
+        delta = noise.pow(2) * 0.5
+        return -(self.a_std_log + self.sqrt_2pi_log + delta).sum(1)  # old_logprob
+
+
+class Critic(nn.Module):
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim + action_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, 1))
+
+    def forward(self, state, action):
+        return self.net(torch.cat((state, action), dim=1))  # q value
+
+
+class CriticAdv(nn.Module):
+    def __init__(self, mid_dim, state_dim, _action_dim):
+        super().__init__()
+        self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.ReLU(),
+                                 nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                 nn.Linear(mid_dim, 1))
+
+    def forward(self, state):
+        return self.net(state)  # advantage value
+
+
+class CriticTwin(nn.Module):  # shared parameter
+    def __init__(self, mid_dim, state_dim, action_dim):
+        super().__init__()
+        self.net_sa = nn.Sequential(nn.Linear(state_dim + action_dim, mid_dim), nn.ReLU(),
+                                    nn.Linear(mid_dim, mid_dim), nn.ReLU())  # concat(state, action)
+        self.net_q1 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                    nn.Linear(mid_dim, 1))  # q1 value
+        self.net_q2 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
+                                    nn.Linear(mid_dim, 1))  # q2 value
+
+    def forward(self, state, action):
+        tmp = self.net_sa(torch.cat((state, action), dim=1))
+        return self.net_q1(tmp)  # one Q value
+
+    def get_q1_q2(self, state, action):
+        tmp = self.net_sa(torch.cat((state, action), dim=1))
+        return self.net_q1(tmp), self.net_q2(tmp)  # two Q values
diff --git a/parameters.py b/parameters.py
new file mode 100644
index 0000000..2f66a0f
--- /dev/null
+++ b/parameters.py
@@ -0,0 +1,47 @@
+solar_parameters = {
+    'I_sc0': 8.0,  # 参考条件下的短路电流 (A)
+    'V_b': 25,  # 基准电压
+    'V_oc0': 36.0,  # 参考条件下的开路电压 (V)
+    'R_s': 0.1,  # 串联电阻 (Ω)
+    'R_sh': 100.0,  # 并联电阻 (Ω)
+    'T_c': -0.2,  # 开路电压的温度系数 (V/°C)
+    'O_c': 0.01,  # 变动成本系数 (元/千瓦时)
+    'I_ref': 1000,  # 参考辐照度 (W/m²)
+    'T_ref': 25,  # 参考温度 (°C)
+}
+
+wind_parameters = {
+    'cutin_speed': 3,
+    'cutout_speed': 12,
+    'rated_speed': 8,
+    'air_density': 1.225,
+    'rotor_radius': 25,
+    'power_coefficient': 0.5,
+    'generator_efficiency': 0.9,
+    'opex_cofficient': 0.01,
+}
+
+battery_parameters = {
+    'capacity': 500,  # kw
+    'max_charge': 100,  # kw
+    'max_discharge': 100,  # kw
+    'efficiency': 0.9,
+    'degradation': 0.01,  # euro/kw
+    'holding': 0.1,
+    'max_soc': 0.8,
+    'min_soc': 0.2,
+}
+
+dg_parameters = {
+    'gen_1': {'a': 0.0034, 'b': 3, 'c': 30,
+              'power_output_max': 150, 'power_output_min': 10,
+              'ramping_up': 100, 'ramping_down': 100},
+
+    'gen_2': {'a': 0.001, 'b': 10, 'c': 40,
+              'power_output_max': 375, 'power_output_min': 50,
+              'ramping_up': 100, 'ramping_down': 100},
+
+    'gen_3': {'a': 0.001, 'b': 15, 'c': 70,
+              'power_output_max': 500, 'power_output_min': 100,
+              'ramping_up': 200, 'ramping_down': 200}
+}
diff --git a/plotDRL.py b/plotDRL.py
new file mode 100644
index 0000000..774327d
--- /dev/null
+++ b/plotDRL.py
@@ -0,0 +1,328 @@
+import os
+import pickle
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+def plot_optimization_result(datasource, directory):
+    sns.set_theme(style='whitegrid')  # "white", "dark", "whitegrid", "darkgrid", "ticks"
+    plt.rcParams["figure.figsize"] = (16, 9)
+    fig, axs = plt.subplots(2, 2)
+    plt.subplots_adjust(wspace=0.7, hspace=0.3)
+    plt.autoscale(tight=True)
+    T = np.array([i for i in range(24)])
+
+    # 绘制步长成本 in ax[0]
+    axs[0, 0].cla()
+    axs[0, 0].set_ylabel('Cost')
+    axs[0, 0].set_xlabel('Time (h)')
+    axs[0, 0].bar(T, datasource['step_cost'])
+
+    # 绘制soc和价格 in ax[1]
+    axs[0, 1].cla()
+    # 设置第一个 y 轴
+    axs[0, 1].set_ylabel('Price')
+    axs[0, 1].set_xlabel('Time [h]')
+    line1, = axs[0, 1].plot(T, datasource['price'], drawstyle='steps-mid', label='Price', color='pink')
+    # 创建第二个 y 轴
+    ax2 = axs[0, 1].twinx()
+    ax2.set_ylabel('SOC')
+    line2, = ax2.plot(T, datasource['soc'], drawstyle='steps-mid', label='SOC', color='grey')
+    # 为每个轴分别创建图例
+    lines = [line1, line2]
+    labels = [line.get_label() for line in lines]
+    axs[0, 1].legend(lines, labels, loc='upper right', bbox_to_anchor=(1.4, 1),
+                     fontsize=12, frameon=False, labelspacing=0.3)
+
+    # 绘制累计发电量和消耗量 in ax[2]
+    axs[1, 0].cla()
+    axs[1, 0].set_ylabel('Power [kWh]')
+    axs[1, 0].set_xlabel('Time [h]')
+    # 处理电池充放电数据
+    battery_positive = np.maximum(np.array(datasource['battery_energy_change']), 0)  # charge
+    battery_negative = np.minimum(np.array(datasource['battery_energy_change']), 0)  # discharge
+    # 处理电网进出口数据
+    imported_from_grid = np.array(datasource['grid_import'])
+    exported_2_grid = np.array(datasource['grid_export'])
+
+    axs[1, 0].bar(T, datasource['gen1'], label='Gen1')
+    axs[1, 0].bar(T, datasource['gen2'], label='Gen2', bottom=datasource['gen1'])
+    axs[1, 0].bar(T, datasource['gen3'], label='Gen3', bottom=datasource['gen2'] + datasource['gen1'])
+    axs[1, 0].bar(T, -battery_positive, color='blue', hatch='/', label='ESS charge')
+    axs[1, 0].bar(T, -battery_negative, hatch='/', label='ESS discharge',
+                  bottom=datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
+    axs[1, 0].bar(T, datasource['pv'], label='Pv Gen',
+                  bottom=-battery_negative + datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
+    axs[1, 0].bar(T, datasource['wind'], label='Wind Gen',
+                  bottom=datasource['pv'] - battery_negative + datasource['gen3'] + datasource['gen2'] + datasource[
+                      'gen1'])
+    # 生成即进口
+    axs[1, 0].bar(T, imported_from_grid, label='Grid import',
+                  bottom=datasource['pv'] + datasource['wind'] - battery_negative + datasource['gen3'] + datasource[
+                      'gen2'] + datasource['gen1'])
+    # 负载即出口
+    axs[1, 0].bar(T, -exported_2_grid, label='Grid export', bottom=-battery_positive)
+    # 绘制净负载曲线
+    axs[1, 0].plot(T, datasource['load'], linewidth=2.0, label='Load', drawstyle='steps-mid', alpha=0.7)
+    axs[1, 0].legend(loc='upper right', bbox_to_anchor=(1.4, 1), fontsize=12, frameon=False, labelspacing=0.3)
+
+    fig.savefig(f"{directory}/gurobi.svg", format='svg', dpi=600, bbox_inches='tight')
+    print('gurobi figure have been ploted and saved')
+
+
+def plot_evaluation_information(eval_data, directory):
+    sns.set_theme(style='whitegrid')
+    with open(eval_data, 'rb') as tf:
+        test_data = pickle.load(tf)
+    # 用条形图表示每一步的不平衡和奖励
+    plt.rcParams["figure.figsize"] = (16, 9)
+    fig, axs = plt.subplots(2, 2)
+    plt.subplots_adjust(wspace=0.7, hspace=0.3)
+    plt.autoscale(tight=True)
+
+    # 为评估环境准备数据
+    eval_data = pd.DataFrame(test_data['system_info'])
+    eval_data.columns = ['time_step', 'price', 'load', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
+                         'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+
+    # 绘制奖励 in axs[0]
+    axs[0, 0].cla()
+    axs[0, 0].set_xlabel('Time (h)')
+    axs[0, 0].set_ylabel('Cost')
+    axs[0, 0].bar(eval_data['time_step'], eval_data['operation_cost'])
+
+    # 绘制能源充/放电与价格关系图 in ax[1]
+    axs[0, 1].cla()
+    axs[0, 1].set_xlabel('Time (h)')
+    axs[0, 1].set_ylabel('Price')
+    line1, = axs[0, 1].plot(eval_data['time_step'], eval_data['price'], drawstyle='steps-mid', label='Price',
+                            color='pink')
+    ax2 = axs[0, 1].twinx()
+    ax2.set_ylabel('SOC')
+    line2, = ax2.plot(eval_data['time_step'], eval_data['soc'], drawstyle='steps-mid', label='SOC', color='grey')
+    lines = [line1, line2]
+    labels = [line.get_label() for line in lines]
+    axs[0, 1].legend(lines, labels, loc='upper right', bbox_to_anchor=(1.4, 1),
+                     fontsize=12, frameon=False, labelspacing=0.3)
+
+    # 绘制发电量和负载量 in ax[2]
+    axs[1, 0].cla()
+    axs[1, 0].set_xlabel('Time [h]')
+    axs[1, 0].set_ylabel('Power [kWh]')
+    # axs[1,0].set_xticks([i for i in range(24)], [i for i in range(1, 25)])
+
+    battery_positive = np.maximum(np.array(eval_data['battery']), 0)  # charge
+    battery_negative = np.minimum(np.array(eval_data['battery']), 0)  # discharge
+    imported_from_grid = np.minimum(np.array(eval_data['unbalance']), 0)
+    exported_2_grid = np.maximum(np.array(eval_data['unbalance']), 0)
+
+    x = eval_data['time_step']
+    axs[1, 0].bar(x, eval_data['gen1'], label='Gen1')
+    axs[1, 0].bar(x, eval_data['gen2'], label='Gen2', bottom=eval_data['gen1'])
+    axs[1, 0].bar(x, eval_data['gen3'], label='Gen3', bottom=eval_data['gen1'] + eval_data['gen2'])
+    axs[1, 0].bar(x, -battery_positive, color='blue', hatch='/', label='ESS charge')
+    axs[1, 0].bar(x, -battery_negative, label='ESS discharge', hatch='/',
+                  bottom=eval_data['gen1'] + eval_data['gen2'] + eval_data['gen3'])
+
+    axs[1, 0].bar(x, eval_data['pv'], label='Pv Gen',
+                  bottom=-battery_negative + eval_data['gen3'] + eval_data['gen2'] + eval_data['gen1'])
+    axs[1, 0].bar(x, eval_data['wind'], label='Wind Gen',
+                  bottom=eval_data['pv'] - battery_negative + eval_data['gen3'] + eval_data['gen2'] + eval_data['gen1'])
+
+    axs[1, 0].bar(x, imported_from_grid, label='Grid import',
+                  bottom=eval_data['pv'] + eval_data['wind'] - battery_negative + eval_data['gen3'] + eval_data[
+                      'gen2'] + eval_data['gen1'])
+    axs[1, 0].bar(x, -exported_2_grid, label='Grid export', bottom=-battery_positive)
+
+    axs[1, 0].plot(x, eval_data['load'], linewidth=2.0, drawstyle='steps-mid', label='Load')
+    axs[1, 0].legend(loc='upper right', bbox_to_anchor=(1.4, 1), fontsize=12, frameon=False, labelspacing=0.3)
+
+    # 绘制不平衡度 in axs[3]
+    axs[1, 1].cla()
+    axs[1, 1].set_xlabel('Time [h]')
+    axs[1, 1].set_ylabel('Power [kWh]')
+    axs[1, 1].bar(eval_data['time_step'], eval_data['unbalance'], label='Exchange with Grid', width=0.4)
+    axs[1, 1].bar(eval_data['time_step'] + 0.4, eval_data['load'], label='netload', width=0.4)
+    axs[1, 1].legend(loc='upper right', bbox_to_anchor=(1.45, 1), fontsize=12, frameon=False, labelspacing=0.5)
+    # fig.savefig(f"{directory}/rl.svg", format='svg', dpi=600, bbox_inches='tight')
+    print('rl figure have been ploted and saved')
+
+
+def plot_soc(gurobi, rl_data, directory):
+    with open(rl_data, 'rb') as tf:
+        test_data = pickle.load(tf)
+
+    rl_data = pd.DataFrame(test_data['system_info'])
+    rl_data.columns = ['time_step', 'price', 'load', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
+                       'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    T = rl_data['time_step']
+
+    plt.rcParams["figure.figsize"] = (6, 4)  # 10 3
+    fig, axs = plt.subplots(1, 1)
+    plt.autoscale(tight=True)
+
+    axs.cla()
+    axs.set_xlabel('Time [h]', fontsize=12)
+    axs.set_ylabel('Price', fontsize=12)
+    line1, = axs.plot(T, gurobi['price'] / 10, drawstyle='steps-mid', label='Price', color='pink')
+    axs.grid(True, which='both', axis='y', linestyle='None', linewidth=0, color='black')
+    axs.grid(True, which='both', axis='x', linestyle='--', linewidth=0.5, color='black')
+
+    ax2 = axs.twinx()
+    ax2.set_ylabel('SOC', fontsize=12)
+    line2, = ax2.plot(T, gurobi['soc'], drawstyle='steps-mid', label='MILP', color='grey')
+    line3, = ax2.plot(T, rl_data['soc'], drawstyle='steps-mid', label='LMPPO', color='blue')
+    ax2.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='black')
+
+    lines = [line1, line2, line3]
+    labels = [line.get_label() for line in lines]
+    axs.legend(lines, labels, loc='upper right', fontsize=12, frameon=True, framealpha=0.8)
+
+    print('soc figure have been ploted and saved')
+    fig.savefig(f"{directory}/soc.png", format='png', dpi=600, bbox_inches='tight')
+
+
+def plot_energy(gurobi, rl_data, directory):
+    with open(rl_data, 'rb') as tf:
+        test_data = pickle.load(tf)
+
+    rl_data = pd.DataFrame(test_data['system_info'])
+    rl_data.columns = ['time_step', 'price', 'load', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
+                       'gen3', 'pv', 'wind', 'unbalance', 'operation_cost', 'reward']
+    T = rl_data['time_step']
+
+    plt.rcParams["figure.figsize"] = (6, 9)
+    fig, axs = plt.subplots(2, 1)
+    plt.subplots_adjust(wspace=0.5, hspace=0.3)
+    plt.autoscale(tight=True)
+
+    axs[0].cla()
+    axs[0].set_xlabel('Time [h]\n(a)', fontsize=12)
+    axs[0].set_ylabel('Power [kWh]', fontsize=12)
+    # 电池充放电数据
+    battery_positive = np.maximum(np.array(gurobi['battery_energy_change']), 0)
+    battery_negative = np.minimum(np.array(gurobi['battery_energy_change']), 0)
+    # 电网进出口数据
+    imported_from_grid = np.array(gurobi['grid_import'])
+    exported_2_grid = np.array(gurobi['grid_export'])
+
+    axs[0].bar(T, gurobi['gen1'], label='Gen1')
+    axs[0].bar(T, gurobi['gen2'], label='Gen2', bottom=gurobi['gen1'])
+    axs[0].bar(T, gurobi['gen3'], label='Gen3', bottom=gurobi['gen2'] + gurobi['gen1'])
+    axs[0].bar(T, -battery_positive, color='blue', hatch='/', label='ESS charge')
+    axs[0].bar(T, -battery_negative, hatch='/', label='ESS discharge',
+               bottom=gurobi['gen3'] + gurobi['gen2'] + gurobi['gen1'])
+    axs[0].bar(T, gurobi['pv'], label='Pv',
+               bottom=-battery_negative + gurobi['gen3'] + gurobi['gen2'] + gurobi['gen1'])
+    axs[0].bar(T, gurobi['wind'], label='Wind',
+               bottom=gurobi['pv'] - battery_negative + gurobi['gen3'] + gurobi['gen2'] + gurobi['gen1'])
+    # 生成即进口
+    axs[0].bar(T, imported_from_grid, label='Import',
+               bottom=gurobi['pv'] + gurobi['wind'] - battery_negative + gurobi['gen3'] + gurobi['gen2'] + gurobi[
+                   'gen1'])
+    # 负载即出口
+    axs[0].bar(T, -exported_2_grid, label='Export', bottom=-battery_positive)
+    # 绘制净负载曲线
+    axs[0].plot(T, gurobi['load'], linewidth=2.0, label='Load', drawstyle='steps-mid')
+    axs[0].grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='black')
+    axs[0].legend(loc='upper right', bbox_to_anchor=(1.4, 1), fontsize=12, frameon=False, labelspacing=0.3)
+
+    # rl
+    axs[1].cla()
+    axs[1].set_xlabel('Time [h]\n(b)', fontsize=12)
+    axs[1].set_ylabel('Power [kWh]', fontsize=12)
+
+    battery_positive = np.maximum(np.array(rl_data['battery']), 0)
+    battery_negative = np.minimum(np.array(rl_data['battery']), 0)
+    imported_from_grid = np.minimum(np.array(rl_data['unbalance']), 0)
+    exported_2_grid = np.maximum(np.array(rl_data['unbalance']), 0)
+
+    axs[1].bar(T, rl_data['gen1'], label='Gen1')
+    axs[1].bar(T, rl_data['gen2'], label='Gen2', bottom=rl_data['gen1'])
+    axs[1].bar(T, rl_data['gen3'], label='Gen3', bottom=rl_data['gen1'] + rl_data['gen2'])
+    axs[1].bar(T, -battery_positive, color='blue', hatch='/', label='ESS charge')
+    axs[1].bar(T, -battery_negative, label='ESS discharge', hatch='/',
+               bottom=rl_data['gen1'] + rl_data['gen2'] + rl_data['gen3'])
+
+    axs[1].bar(T, rl_data['pv'], label='Pv',
+               bottom=-battery_negative + rl_data['gen3'] + rl_data['gen2'] + rl_data['gen1'])
+    axs[1].bar(T, rl_data['wind'], label='Wind',
+               bottom=rl_data['pv'] - battery_negative + rl_data['gen3'] + rl_data['gen2'] + rl_data['gen1'])
+
+    axs[1].bar(T, imported_from_grid, label='Import',
+               bottom=rl_data['pv'] + rl_data['wind'] - battery_negative + rl_data['gen3'] + rl_data[
+                   'gen2'] + rl_data['gen1'])
+    axs[1].bar(T, -exported_2_grid, label='Export', bottom=-battery_positive)
+
+    axs[1].plot(T, rl_data['load'], linewidth=2.0, drawstyle='steps-mid', label='Load')
+    axs[1].grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='black')
+    axs[1].legend(loc='upper right', bbox_to_anchor=(1.4, 1), fontsize=12, frameon=False, labelspacing=0.3)
+
+    print('energy figure have been ploted and saved')
+    # fig.savefig(f"{directory}/energy.png", format='png', dpi=600, bbox_inches='tight')
+
+
+def plot_melt(ppo, llm, lmppo, milp, ram, directory):
+    with open(ppo, 'rb') as t1:
+        ppo = pickle.load(t1)
+    with open(llm, 'rb') as t2:
+        llm = pickle.load(t2)
+    with open(lmppo, 'rb') as t3:
+        lmppo = pickle.load(t3)
+    with open(milp, 'rb') as t4:
+        milp = pickle.load(t4)
+    with open(ram, 'rb') as t5:
+        ram = pickle.load(t5)
+
+    T = lmppo.index
+    milp['unbalance'] = 0
+    plt.rcParams["figure.figsize"] = (6, 8)
+    fig, (ax1, ax2) = plt.subplots(2, 1)
+    plt.subplots_adjust(wspace=0.5, hspace=0.4)
+    plt.autoscale(tight=True)
+
+    print('ppo平均奖励', ppo['reward'].mean(), 'ppo平均不平衡度', ppo['unbalance'].mean()/1e3)
+    print('llm平均奖励', llm['reward'].mean(), 'llm平均不平衡度', llm['unbalance'].mean()/1e3)
+    print('lmppo平均奖励', lmppo['reward'].mean(), 'lmppo平均不平衡度', lmppo['unbalance'].mean()/1e3)
+    print('milp平均奖励', milp['reward'].mean(), 'milp平均不平衡度', milp['unbalance'].mean()/1e3)
+    print('ram平均奖励', ram['reward'].mean(), 'ram平均不平衡度', ram['unbalance'].mean()/1e3)
+    ax1.cla()
+    ax1.set_xlabel('Day\n(a)', fontsize=12)
+    ax1.set_ylabel('Reward [-]', fontsize=12)
+    ax1.plot(T, ppo['reward'], color='#374FE6', label='PPO', marker='o', markersize=5)
+    ax1.plot(T, llm['reward'], color='#BB5D5D', label='LLM', marker='o', markersize=5)
+    ax1.plot(T, lmppo['reward'], color='#6DBA4F', label='LMPPO', marker='o', markersize=5)
+    ax1.plot(T, milp['reward'], color='#8B5B29', label='MILP', marker='o', markersize=5)
+    ax1.plot(T, ram['reward'], color='#B0B0B0', label='RAN', marker='o', markersize=5)
+    ax1.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='black')
+    ax1.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), fontsize=12, frameon=False, ncol=5, labelspacing=0.3, columnspacing=1.5)
+
+    ax2.cla()
+    ax2.set_xlabel('Day\n(b)', fontsize=12)
+    ax2.set_ylabel('Power imbalance [kW]', fontsize=12)
+    ax2.plot(T, ppo['unbalance'] / 1e3, color='#374FE6', label='PPO', marker='o', markersize=5)
+    ax2.plot(T, llm['unbalance'] / 1e3, color='#BB5D5D', label='LLM', marker='o', markersize=5)
+    ax2.plot(T, lmppo['unbalance'] / 1e3, color='#6DBA4F', label='LMPPO', marker='o', markersize=5)
+    ax2.plot(T, milp['unbalance'], color='#8B5B29', label='MILP', marker='o', markersize=5)
+    ax2.plot(T, ram['unbalance'] / 1e3, color='#B0B0B0', label='RAN', marker='o', markersize=5)
+    ax2.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='black')
+    ax2.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), fontsize=12, frameon=False, ncol=5, labelspacing=0.3, columnspacing=1.5)
+
+    print('melt figure have been ploted and saved')
+    plt.autoscale()
+    fig.savefig(f"{directory}/melt.png", format='png', dpi=600, bbox_inches='tight')
+
+
+def make_dir(directory, feature_change):
+    cwd = f'{directory}/DRL_{feature_change}_plots'
+    os.makedirs(cwd, exist_ok=True)
+    return cwd
+
+
+class PlotArgs:
+    def __init__(self) -> None:
+        self.cwd = None
+        self.feature_change = None
+        self.plot_on = None
diff --git a/plott.py b/plott.py
new file mode 100644
index 0000000..accdf34
--- /dev/null
+++ b/plott.py
@@ -0,0 +1,126 @@
+import re
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy.ndimage import gaussian_filter1d
+from matplotlib.patches import ConnectionPatch
+
+
+def extract(text):
+    matches = re.findall(pattern, text)
+    rewards = []
+    unbalances = []
+
+    for match in matches:
+        reward, unbalance = match
+        rewards.append(float(reward))
+        unbalances.append(float(unbalance) / 1000)
+
+    rewards_s = pd.Series(rewards).rolling(window=3).mean()
+    unbalances_s = pd.Series(unbalances).rolling(window=3).mean()
+    return rewards, rewards_s, unbalances, unbalances_s
+
+
+pattern = r"reward: ([\-\d\.]+),\s*unbalance:\s*([\d\.]+)"
+
+with open('res/ddpg.txt', 'r', encoding='utf-16') as f1:
+    ddpg = f1.read()
+with open('res/ppo_1016.txt', 'r', encoding='utf-8') as f2:
+    ppo = f2.read()
+with open('res/ppo_llm.txt', 'r', encoding='utf-8') as f3:
+    ppo_llm = f3.read()
+with open('res/sac.txt', 'r', encoding='utf-8') as f4:
+    sac = f4.read()
+with open('res/td3.txt', 'r', encoding='utf-8') as f5:
+    td3 = f5.read()
+
+ddpg_rewards, ddpg_rewards_s, ddpg_unbalances, ddpg_unbalances_s = extract(ddpg)
+td3_rewards, td3_rewards_s, td3_unbalances, td3_unbalances_s = extract(td3)
+sac_rewards, sac_rewards_s, sac_unbalances, sac_unbalances_s = extract(sac)
+ppo_rewards, ppo_rewards_s, ppo_unbalances, ppo_unbalances_s = extract(ppo)
+lmppo_rewards, lmppo_rewards_s, lmppo_unbalances, lmppo_unbalances_s = extract(ppo_llm)
+
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 10))
+
+ax1.plot(ddpg_rewards, color='#E03C31', alpha=0.2)
+ax1.plot(td3_rewards, color='#1D8C55', alpha=0.2)
+ax1.plot(sac_rewards, color='#FFA500', alpha=0.2)
+ax1.plot(ppo_rewards, color='#3498DB', alpha=0.2)
+ax1.plot(lmppo_rewards, color='#A52A2A', alpha=0.2)
+ax1.plot(ddpg_rewards_s, label='DDPG', color='#E03C31', alpha=0.8)
+ax1.plot(td3_rewards_s, label='TD3', color='#1D8C55', alpha=0.8)
+ax1.plot(sac_rewards_s, label='SAC', color='#FFA500', alpha=0.8)
+ax1.plot(ppo_rewards_s, label='PPO', color='#3498DB', alpha=0.8)
+ax1.plot(lmppo_rewards_s, label='LMPPO', color='#A52A2A', alpha=0.8)
+ax1.set_xlabel('Time [h]\n(a)', fontsize=14, labelpad=0)
+ax1.set_ylabel('Reward[-]', fontsize=14)
+
+# 子图
+inset_ax1 = fig.add_axes([0.45, 0.6, 0.25, 0.15])  # [x, y, 宽, 高]
+inset_ax1.plot(ddpg_rewards, color='#E03C31', alpha=0.8)
+inset_ax1.plot(td3_rewards, color='#1D8C55', alpha=0.8)
+inset_ax1.plot(sac_rewards, color='#FFA500', alpha=0.8)
+inset_ax1.plot(ppo_rewards, color='#3498DB', alpha=0.8)
+inset_ax1.plot(lmppo_rewards, color='#A52A2A', alpha=0.8)
+inset_ax1.set_xlim(900, 1000)
+inset_ax1.set_ylim(-150, -20)
+inset_ax1.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='gray')
+inset_ax1.tick_params(axis='both', which='major', labelsize=10)
+
+ax1.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='gray')
+ax1.legend(fontsize=12, handletextpad=0.5, labelspacing=0.3)
+
+# 使用ConnectionPatch连接主图和子图
+xy_main = (900, 0)
+xy_inset = (900, -20)
+xy_main2 = (1000, 0)
+xy_inset2 = (1000, -20)
+
+con = ConnectionPatch(xyA=xy_inset, xyB=xy_main, coordsA="data", coordsB="data",
+                      axesA=inset_ax1, axesB=ax1, linestyle='--', linewidth=1, color='gray')
+con2 = ConnectionPatch(xyA=xy_inset2, xyB=xy_main2, coordsA="data", coordsB="data",
+                       axesA=inset_ax1, axesB=ax1, linestyle='--', linewidth=1, color='gray')
+fig.add_artist(con)
+fig.add_artist(con2)
+
+ax2.plot(ddpg_unbalances, color='#E03C31', alpha=0.2)
+ax2.plot(td3_unbalances, color='#1D8C55', alpha=0.2)
+ax2.plot(sac_unbalances, color='#FFA500', alpha=0.2)
+ax2.plot(ppo_unbalances, color='#3498DB', alpha=0.2)
+ax2.plot(lmppo_unbalances, color='#A52A2A', alpha=0.2)
+ax2.plot(ddpg_unbalances_s, label='DDPG', color='#E03C31', alpha=0.8)
+ax2.plot(td3_unbalances_s, label='TD3', color='#1D8C55', alpha=0.8)
+ax2.plot(sac_unbalances_s, label='SAC', color='#FFA500', alpha=0.8)
+ax2.plot(ppo_unbalances_s, label='PPO', color='#3498DB', alpha=0.8)
+ax2.plot(lmppo_unbalances_s, label='LMPPO', color='#A52A2A', alpha=0.8)
+ax2.set_xlabel('Time [h]\n(b)', fontsize=14, labelpad=0)
+ax2.set_ylabel('Power imbalance[KW]', fontsize=14)
+
+# 子图
+inset_ax2 = fig.add_axes([0.45, 0.25, 0.25, 0.15])
+inset_ax2.plot(ddpg_unbalances, color='#E03C31', alpha=0.8)
+inset_ax2.plot(td3_unbalances, color='#1D8C55', alpha=0.8)
+inset_ax2.plot(sac_unbalances, color='#FFA500', alpha=0.8)
+inset_ax2.plot(ppo_unbalances, color='#3498DB', alpha=0.8)
+inset_ax2.plot(lmppo_unbalances, color='#A52A2A', alpha=0.8)
+inset_ax2.set_xlim(900, 1000)
+inset_ax2.set_ylim(0, 2)
+inset_ax2.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='gray')
+inset_ax2.tick_params(axis='both', which='major', labelsize=10)
+
+ax2.grid(True, which='both', axis='both', linestyle='--', linewidth=0.5, color='gray')
+ax2.legend(fontsize=12, handletextpad=0.5, labelspacing=0.3)
+
+xy_main3 = (900, 0)
+xy_inset3 = (900, 0)
+xy_main4 = (1000, 0)
+xy_inset4 = (1000, 0)
+
+con3 = ConnectionPatch(xyA=xy_inset3, xyB=xy_main3, coordsA="data", coordsB="data",
+                       axesA=inset_ax2, axesB=ax2, linestyle='--', linewidth=1, color='gray')
+con4 = ConnectionPatch(xyA=xy_inset4, xyB=xy_main4, coordsA="data", coordsB="data",
+                       axesA=inset_ax2, axesB=ax2, linestyle='--', linewidth=1, color='gray')
+fig.add_artist(con3)
+fig.add_artist(con4)
+
+plt.show()
+fig.savefig("compare.png", format='png', dpi=600, bbox_inches='tight')
diff --git a/res/ddpg.txt b/res/ddpg.txt
new file mode 100644
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diff --git a/res/ppo.txt b/res/ppo.txt
new file mode 100644
index 0000000..4bc1e77
--- /dev/null
+++ b/res/ppo.txt
@@ -0,0 +1,1000 @@
+epsiode: 0, reward: -83.372120641053, unbalance: 655.3007576605931
+epsiode: 1, reward: -130.70921769601588, unbalance: 1328.1646674007652
+epsiode: 2, reward: -137.3032407845217, unbalance: 1821.3056489403248
+epsiode: 3, reward: -150.81373942851394, unbalance: 2550.5165435551385
+epsiode: 4, reward: -68.96250250819757, unbalance: 354.8899747143149
+epsiode: 5, reward: -77.12002008828016, unbalance: 1113.4466839715878
+epsiode: 6, reward: -86.50475748514259, unbalance: 905.8256794892015
+epsiode: 7, reward: -66.27899890117223, unbalance: 275.53265572741
+epsiode: 8, reward: -66.12229606653457, unbalance: 270.7102252369119
+epsiode: 9, reward: -161.71179595587085, unbalance: 1587.354257213183
+epsiode: 10, reward: -118.34658894506221, unbalance: 1303.842157676102
+epsiode: 11, reward: -53.63605206980429, unbalance: 416.58441493630437
+epsiode: 12, reward: -121.38337287438735, unbalance: 1116.6051429933786
+epsiode: 13, reward: -64.30707239161572, unbalance: 250.8397838135799
+epsiode: 14, reward: -98.78445879665959, unbalance: 388.93421450567484
+epsiode: 15, reward: -68.36306334707318, unbalance: 338.0440692973026
+epsiode: 16, reward: -78.78359779790566, unbalance: 687.7827109683519
+epsiode: 17, reward: -215.96764976300167, unbalance: 1832.7894372253404
+epsiode: 18, reward: -117.83043420326642, unbalance: 1361.8963721385358
+epsiode: 19, reward: -125.40184811745097, unbalance: 1170.9315121944717
+epsiode: 20, reward: -217.60157661928207, unbalance: 2791.361110821568
+epsiode: 21, reward: -155.4006719092591, unbalance: 1512.1317176852665
+epsiode: 22, reward: -212.08024599084155, unbalance: 2584.1433939119806
+epsiode: 23, reward: -144.70043900980173, unbalance: 1390.4656281226762
+epsiode: 24, reward: -139.53514705782064, unbalance: 1506.9935008773525
+epsiode: 25, reward: -84.48504991456187, unbalance: 685.4909364367738
+epsiode: 26, reward: -89.63859293610747, unbalance: 961.2716149310713
+epsiode: 27, reward: -190.062212897277, unbalance: 1955.0143649129016
+epsiode: 28, reward: -124.48974419647925, unbalance: 1469.7972481167444
+epsiode: 29, reward: -62.78615309563605, unbalance: 433.593902541697
+epsiode: 30, reward: -169.4613270761008, unbalance: 2084.0231474042066
+epsiode: 31, reward: -129.5931479525269, unbalance: 1178.7591102125275
+epsiode: 32, reward: -222.1624151614817, unbalance: 2183.0318181068596
+epsiode: 33, reward: -84.91520409570562, unbalance: 834.8059466791153
+epsiode: 34, reward: -191.83908495367376, unbalance: 2432.7718445561827
+epsiode: 35, reward: -99.33175986660747, unbalance: 640.0361961784629
+epsiode: 36, reward: -84.48841209037275, unbalance: 1096.5921492783782
+epsiode: 37, reward: -90.32656756984589, unbalance: 1402.3978650518754
+epsiode: 38, reward: -69.99258407438514, unbalance: 471.3313281708007
+epsiode: 39, reward: -172.83737646227763, unbalance: 1238.5186042891453
+epsiode: 40, reward: -53.73730581995255, unbalance: 494.35936862546816
+epsiode: 41, reward: -103.20782775830585, unbalance: 843.4354903116822
+epsiode: 42, reward: -83.54003832386773, unbalance: 546.1438013365758
+epsiode: 43, reward: -119.64780235421463, unbalance: 1932.9403687176246
+epsiode: 44, reward: -64.33862256606325, unbalance: 80.4568645098166
+epsiode: 45, reward: -83.81712424289668, unbalance: 385.21956785370844
+epsiode: 46, reward: -68.81415479630515, unbalance: 637.0930943518861
+epsiode: 47, reward: -88.01028945597608, unbalance: 446.31850769301684
+epsiode: 48, reward: -32.559351034372426, unbalance: 0.0
+epsiode: 49, reward: -66.98440180238902, unbalance: 289.1934410918499
+epsiode: 50, reward: -138.84420166234955, unbalance: 2150.6705346611125
+epsiode: 51, reward: -46.46317570864628, unbalance: 164.93826208304728
+epsiode: 52, reward: -90.71213245163294, unbalance: 89.15416296401105
+epsiode: 53, reward: -67.29635883925502, unbalance: 234.14061177060734
+epsiode: 54, reward: -66.07593599474393, unbalance: 106.2137896556126
+epsiode: 55, reward: -106.44248418723775, unbalance: 267.37477591576476
+epsiode: 56, reward: -101.60494331777309, unbalance: 214.9441741869544
+epsiode: 57, reward: -66.93620913675768, unbalance: 71.06281952506734
+epsiode: 58, reward: -90.5014838270177, unbalance: 1001.4394688122437
+epsiode: 59, reward: -37.49860386038065, unbalance: 5.7182084549735634
+epsiode: 60, reward: -37.53648808903248, unbalance: 58.57599416657422
+epsiode: 61, reward: -56.03671992486919, unbalance: 0.0
+epsiode: 62, reward: -165.6575506592902, unbalance: 3142.7474561264257
+epsiode: 63, reward: -61.518730741466854, unbalance: 571.4883375581799
+epsiode: 64, reward: -86.31816772682687, unbalance: 39.04341059600962
+epsiode: 65, reward: -93.37042954614932, unbalance: 673.6816007209393
+epsiode: 66, reward: -91.87150324357532, unbalance: 543.7267981365608
+epsiode: 67, reward: -68.84084517772122, unbalance: 185.68781512106898
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\ No newline at end of file
diff --git a/res/ppo_1016.txt b/res/ppo_1016.txt
new file mode 100644
index 0000000..504c0db
--- /dev/null
+++ b/res/ppo_1016.txt
@@ -0,0 +1,1005 @@
+PPO.py：10.22
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\ No newline at end of file
diff --git a/res/ppo_llm.txt b/res/ppo_llm.txt
new file mode 100644
index 0000000..997f761
--- /dev/null
+++ b/res/ppo_llm.txt
@@ -0,0 +1,1000 @@
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\ No newline at end of file
diff --git a/res/sac.txt b/res/sac.txt
new file mode 100644
index 0000000..abf2e39
--- /dev/null
+++ b/res/sac.txt
@@ -0,0 +1,1000 @@
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\ No newline at end of file
diff --git a/res/td3.txt b/res/td3.txt
new file mode 100644
index 0000000..f0c89cb
--- /dev/null
+++ b/res/td3.txt
@@ -0,0 +1,1000 @@
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diff --git a/tools.py b/tools.py
new file mode 100644
index 0000000..0f045cb
--- /dev/null
+++ b/tools.py
@@ -0,0 +1,290 @@
+import json
+import os
+import gurobipy as gp
+import numpy as np
+import pandas as pd
+import torch
+from gurobipy import GRB
+
+
+def optimization_base_result(env, month, day, initial_soc=0.2):
+    price = env.data_manager.get_series_price_data(month, day)
+    load = env.data_manager.get_series_load_cons_data(month, day)
+    temperature = env.data_manager.get_series_temperature_data(month, day)
+    irradiance = env.data_manager.get_series_irradiance_data(month, day)
+    wind_speed = env.data_manager.get_series_wind_data(month, day)
+
+    period = env.episode_length
+    DG_parameters = env.dg_parameters
+
+    def get_dg_info(parameters):
+        p_max = []
+        p_min = []
+        ramping_up = []
+        ramping_down = []
+        a_para = []
+        b_para = []
+        c_para = []
+        for name, gen_info in parameters.items():
+            p_max.append(gen_info['power_output_max'])
+            p_min.append(gen_info['power_output_min'])
+            ramping_up.append(gen_info['ramping_up'])
+            ramping_down.append(gen_info['ramping_down'])
+            a_para.append(gen_info['a'])
+            b_para.append(gen_info['b'])
+            c_para.append(gen_info['c'])
+        return p_max, p_min, ramping_up, ramping_down, a_para, b_para, c_para
+
+    p_max, p_min, ramping_up, ramping_down, a_para, b_para, c_para = get_dg_info(parameters=DG_parameters)
+    NUM_GEN = len(DG_parameters.keys())
+    battery_capacity = env.battery.capacity
+    battery_efficiency = env.battery.efficiency
+    solar_cofficient = env.solar.opex_cofficient
+    wind_cofficient = env.wind.opex_cofficient
+    battery_degradation = env.battery.degradation
+    battery_holding = env.battery.holding
+
+    m = gp.Model("UC")
+    m.setParam('LogToConsole', 0)
+
+    # 设置系统变量
+    on_off = m.addVars(NUM_GEN, period, vtype=GRB.BINARY, name='on_off')
+    gen_output = m.addVars(NUM_GEN, period, vtype=GRB.CONTINUOUS, name='output')
+    # 设置充放电约束
+    soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name='SOC')
+    battery_energy_change = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-env.battery.max_charge,
+                                      ub=env.battery.max_charge, name='battery_action')
+    # 设置外部电网与能源系统交换约束
+    grid_energy_import = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=env.grid.exchange_ability, name='import')
+    grid_energy_export = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=env.grid.exchange_ability, name='export')
+    # 设置电压控制约束
+    pv_voltage = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name='pv_voltage')
+
+    # 计算光伏和风力发电量
+    pv = [(0.2 * irradiance[t] + 0.05 * temperature[t] - 9.25) * (1 + pv_voltage[t]) for t in range(period)]
+    wind = [172.265625 * wind_speed[t] ** 3 / 1e3 if 3 <= wind_speed[t] < 8
+            else (172.265625 * 8 ** 3 / 1e3 if 8 <= wind_speed[t] < 12 else 0) for t in range(period)]
+
+    # 1. 添加平衡约束
+    m.addConstrs(((sum(gen_output[g, t] for g in range(NUM_GEN)) + pv[t] + wind[t] + grid_energy_import[t] >= load[t]
+                   + battery_energy_change[t] + grid_energy_export[t]) for t in range(period)), name='powerbalance')
+    # 2. 添加发电机最大/最小功率约束
+    m.addConstrs((gen_output[g, t] <= on_off[g, t] * p_max[g] for g in range(NUM_GEN) for t in range(period)),
+                 'gen_output_max')
+    m.addConstrs((gen_output[g, t] >= on_off[g, t] * p_min[g] for g in range(NUM_GEN) for t in range(period)),
+                 'gen_output_min')
+    # 3. 添加上升和下降约束
+    m.addConstrs((gen_output[g, t + 1] - gen_output[g, t] <= ramping_up[g] for g in range(NUM_GEN)
+                  for t in range(period - 1)), 'ramping_up')
+    m.addConstrs((gen_output[g, t] - gen_output[g, t + 1] <= ramping_down[g] for g in range(NUM_GEN)
+                  for t in range(period - 1)), 'ramping_down')
+    # 4. 添加电池容量约束
+    m.addConstr(battery_capacity * soc[0] == battery_capacity * initial_soc +
+                (battery_energy_change[0] * battery_efficiency), name='soc0')
+    m.addConstrs((battery_capacity * soc[t] == battery_capacity * soc[t - 1] +
+                  (battery_energy_change[t] * battery_efficiency) for t in range(1, period)), name='soc update')
+    # 设置成本函数
+    cost_gen = gp.quicksum(
+        (a_para[g] * gen_output[g, t] * gen_output[g, t] + b_para[g] * gen_output[g, t] + c_para[g] * on_off[g, t])
+        for t in range(period) for g in range(NUM_GEN))
+    cost_battery = gp.quicksum(
+        battery_energy_change[t] * battery_degradation + soc[t] * battery_holding for t in range(period))
+    cost_import = gp.quicksum(grid_energy_import[t] * price[t] for t in range(period))
+    cost_export = gp.quicksum(grid_energy_export[t] * price[t] * env.sell_coefficient for t in range(period))
+    cost_solar = gp.quicksum(pv[t] * solar_cofficient for t in range(period))
+    cost_wind = gp.quicksum(wind[t] * wind_cofficient for t in range(period))
+
+    m.setObjective((cost_gen + cost_battery + cost_import - cost_export + cost_solar + cost_wind), GRB.MINIMIZE)
+    m.optimize()
+
+    # 记录数据便于绘图
+    pv = [(0.2 * irradiance[t] + 0.05 * temperature[t] - 9.25) * (1 + pv_voltage[t].x) for t in range(period)]
+    output_record = {'pv': [], 'wind': [], 'price': [], 'load': [], 'netload': [],
+                     'soc': [], 'battery_energy_change': [], 'grid_import': [], 'grid_export': [],
+                     'gen1': [], 'gen2': [], 'gen3': [], 'step_cost': []}
+    for t in range(period):
+        gen_cost = sum((on_off[g, t].x * (
+                a_para[g] * gen_output[g, t].x * gen_output[g, t].x + b_para[g] * gen_output[g, t].x + c_para[g]))
+                       for g in range(NUM_GEN))
+        battery_cost = battery_energy_change[t].x * battery_degradation + soc[t].x * battery_holding
+        import_cost = grid_energy_import[t].x * price[t]
+        export_cost = grid_energy_export[t].x * price[t] * env.sell_coefficient
+        solar_cost = pv[t] * solar_cofficient
+        wind_cost = wind[t] * wind_cofficient
+        output_record['pv'].append(pv[t])
+        output_record['wind'].append(wind[t])
+        output_record['price'].append(price[t])
+        output_record['load'].append(load[t])
+        output_record['netload'].append(load[t] - pv[t] - wind[t])
+        output_record['soc'].append(soc[t].x)
+        output_record['battery_energy_change'].append(battery_energy_change[t].x)
+        output_record['grid_import'].append(grid_energy_import[t].x)
+        output_record['grid_export'].append(grid_energy_export[t].x)
+        output_record['gen1'].append(gen_output[0, t].x)
+        output_record['gen2'].append(gen_output[1, t].x)
+        output_record['gen3'].append(gen_output[2, t].x)
+        output_record['step_cost'].append(gen_cost + battery_cost + import_cost - export_cost + solar_cost + wind_cost)
+
+    output_record_df = pd.DataFrame.from_dict(output_record)
+    return output_record_df
+
+
+class Arguments:
+    """revise here for our own purpose"""
+
+    def __init__(self, agent=None, env=None):
+        self.agent = agent
+        self.env = env
+        self.cwd = None  # current work directory, None means set automatically
+        self.if_remove = False  # remove the cwd folder? (True, False, None:ask me)
+        self.visible_gpu = '0,1,2,3'  # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
+        self.num_threads = 32  # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
+
+        '''Arguments for training'''
+        self.num_episode = 1000
+        self.gamma = 0.995  # discount factor of future rewards
+        self.learning_rate = 2 ** -14  # 2e-4
+        self.soft_update_tau = 2 ** -8  # 2 ** -8 ~= 5e-3
+        self.net_dim = 256  # the network width 256
+        self.batch_size = 4096  # num of transitions sampled from replay buffer.
+        self.repeat_times = 2 ** 5  # repeatedly update network to keep critic's loss small
+        self.target_step = 4096  # collect target_step experiences, then update network, 1024
+        self.max_memo = 500000  # capacity of replay buffer
+        self.if_per_or_gae = False  # PER for off-policy sparse reward: Prioritized Experience Replay.
+
+        '''Arguments for evaluate'''
+        self.random_seed = 0  # initialize random seed in self.init_before_training()
+        # self.random_seed_list = [1234, 2234, 3234, 4234, 5234]
+        self.random_seed_list = [1234]
+        '''Arguments for save and plot issues'''
+        self.train = True
+        self.save_network = True
+        self.test_network = True
+        self.save_test_data = True
+        self.compare_with_gurobi = True
+        self.plot_on = True
+
+    def init_before_training(self, if_main):
+        if self.cwd is None:
+            agent_name = self.agent.__class__.__name__
+            self.cwd = f'./{agent_name}'
+
+        if if_main:
+            import shutil  # remove history according to bool(if_remove)
+            if self.if_remove is None:
+                self.if_remove = bool(input(f"| PRESS 'y' to REMOVE: {self.cwd}? ") == 'y')
+            elif self.if_remove:
+                shutil.rmtree(self.cwd, ignore_errors=True)
+                print(f"| Remove cwd: {self.cwd}")
+            os.makedirs(self.cwd, exist_ok=True)
+
+        np.random.seed(self.random_seed)
+        torch.manual_seed(self.random_seed)
+        torch.set_num_threads(self.num_threads)
+        torch.set_default_dtype(torch.float32)
+
+        os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
+
+
+def test_one_episode(env, act, device):
+    """to get evaluate information, here record the unbalance of after taking action"""
+    record_state = []
+    record_action = []
+    record_reward = []
+    record_unbalance = []
+    record_system_info = []  # [time,price,netload,action,real action,soc,output*4,unbalance(exchange+penalty),cost]
+    record_init_info = []  # include month,day,time,intial soc
+    env.TRAIN = False
+    state = env.reset()
+    record_init_info.append([env.month, env.day, env.current_time, env.battery.current_capacity])
+    print(f'current testing month is {env.month}, day is {env.day},initial_soc is {env.battery.current_capacity}')
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        action = a_tensor.detach().cpu().numpy()[0]  # not need detach(), because with torch.no_grad() outside
+        real_action = action
+        state, next_state, reward, done = env.step(action)
+
+        record_system_info.append([state[0], state[1], state[3] + env.wind.current_power, action, real_action,
+                                   env.battery.SOC(), env.battery.energy_change, next_state[4], next_state[5],
+                                   next_state[6], env.solar.current_power, env.wind.current_power, env.unbalance,
+                                   env.operation_cost, reward])
+        record_state.append(state)
+        record_action.append(real_action)
+        record_reward.append(reward)
+        record_unbalance.append(env.unbalance)
+        state = next_state
+    # add information of last step dg1, dh2, dg3, soc, tem, irr
+    record_system_info[-1][7:12] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
+                                    env.final_step_outputs[4], env.final_step_outputs[5]]
+    record_system_info[-1][5] = env.final_step_outputs[3]
+    record = {'init_info': record_init_info, 'system_info': record_system_info, 'state': record_state,
+              'action': record_action, 'reward': record_reward, 'unbalance': record_unbalance}
+    return record
+
+
+def get_episode_return(env, act, device):
+    episode_reward = 0.0  # sum of rewards in an episode
+    episode_unbalance = 0.0
+    state = env.reset()
+    for i in range(24):
+        s_tensor = torch.as_tensor((state,), device=device)
+        a_tensor = act(s_tensor)
+        action = a_tensor.detach().cpu().numpy()[0]  # not need detach(), because with torch.no_grad() outside
+        state, next_state, reward, done, = env.step(action)
+        state = next_state
+        episode_reward += reward
+        episode_unbalance += env.real_unbalance
+        if done:
+            break
+    return episode_reward, episode_unbalance
+
+
+class ReplayBuffer:
+    def __init__(self, max_len, state_dim, action_dim, gpu_id=0):
+        self.now_len = 0
+        self.next_idx = 0
+        self.if_full = False
+        self.max_len = max_len
+        self.data_type = torch.float32
+        self.action_dim = action_dim
+        self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
+
+        other_dim = 1 + 1 + self.action_dim
+        self.buf_other = torch.empty(size=(max_len, other_dim), dtype=self.data_type, device=self.device)
+
+        if isinstance(state_dim, int):
+            self.buf_state = torch.empty((max_len, state_dim), dtype=torch.float32, device=self.device)
+        elif isinstance(state_dim, tuple):
+            self.buf_state = torch.empty((max_len, *state_dim), dtype=torch.uint8, device=self.device)
+        else:
+            raise ValueError('state_dim')
+
+    def extend_buffer(self, state, other):  # CPU array to CPU array
+        size = len(other)
+        next_idx = self.next_idx + size
+
+        if next_idx > self.max_len:
+            self.buf_state[self.next_idx:self.max_len] = state[:self.max_len - self.next_idx]
+            self.buf_other[self.next_idx:self.max_len] = other[:self.max_len - self.next_idx]
+            self.if_full = True
+
+            next_idx = next_idx - self.max_len
+            self.buf_state[0:next_idx] = state[-next_idx:]
+            self.buf_other[0:next_idx] = other[-next_idx:]
+        else:
+            self.buf_state[self.next_idx:next_idx] = state
+            self.buf_other[self.next_idx:next_idx] = other
+        self.next_idx = next_idx
+
+    def sample_batch(self, batch_size) -> tuple:
+        indices = np.random.randint(self.now_len - 1, size=batch_size)
+        r_m_a = self.buf_other[indices]
+        return (r_m_a[:, 0:1],
+                r_m_a[:, 1:2],
+                r_m_a[:, 2:],
+                self.buf_state[indices],
+                self.buf_state[indices + 1])
+
+    def update_now_len(self):
+        self.now_len = self.max_len if self.if_full else self.next_idx