update battery cost

tools.py

@@ -4,6 +4,7 @@ import numpy as np
 import numpy.random as rd
 import pandas as pd
 import torch
+import json
 from gurobipy import GRB
 
 
@@ -14,9 +15,6 @@ def optimization_base_result(env, month, day, initial_soc):
     irradiance = env.data_manager.get_series_irradiance_data(month, day)
     wind_speed = env.data_manager.get_series_wind_data(month, day)
 
-    pv = [env.solar.step(temp, irr) for temp, irr in zip(temperature, irradiance)]
-    wind = [env.wind.step(ws) for ws in wind_speed]
-
     period = env.episode_length
     DG_parameters = env.dg_parameters
 
@@ -28,7 +26,6 @@ def optimization_base_result(env, month, day, initial_soc):
         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'])
@@ -44,7 +41,10 @@ def optimization_base_result(env, month, day, initial_soc):
     battery_capacity = env.battery.capacity
     battery_efficiency = env.battery.efficiency
     solar_cofficient = env.solar.opex_cofficient
+    volatage_change_percent = env.solar.change_percent
     wind_cofficient = env.wind.opex_cofficient
+    battery_degradation = env.battery.degradation
+    battery_holding = env.battery.holding
 
     m = gp.Model("UC")
 
@@ -52,16 +52,24 @@ def optimization_base_result(env, month, day, initial_soc):
     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')
-    soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name='SOC')
+    # 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 + volatage_change_percent * 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] +
+    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')
+                   + 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')
@@ -81,14 +89,18 @@ def optimization_base_result(env, month, day, initial_soc):
     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_grid_import = gp.quicksum(grid_energy_import[t] * price[t] for t in range(period))
+    cost_battery = gp.quicksum(battery_energy_change[t] * battery_degradation + soc[t] * battery_holding for t in range(period))
-    cost_grid_export = gp.quicksum(grid_energy_export[t] * price[t] * env.sell_coefficient 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_grid_import - cost_grid_export + cost_solar + cost_wind), GRB.MINIMIZE)
+    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 + volatage_change_percent * 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': []}
@@ -96,8 +108,9 @@ def optimization_base_result(env, month, day, initial_soc):
         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))
-        grid_import_cost = grid_energy_import[t].x * price[t]
+        battery_cost = battery_energy_change[t].x * battery_degradation + soc[t].x * battery_holding
-        grid_export_cost = grid_energy_export[t].x * price[t] * env.sell_coefficient
+        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])
@@ -112,7 +125,18 @@ def optimization_base_result(env, month, day, initial_soc):
         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 + grid_import_cost - grid_export_cost + solar_cost + wind_cost)
+        output_record['step_cost'].append(gen_cost + battery_cost + import_cost - export_cost + solar_cost + wind_cost)
+
+    # 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.json', 'w') as f:
+        json.dump(results, f, indent=2)
 
     output_record_df = pd.DataFrame.from_dict(output_record)
     return output_record_df
@@ -184,7 +208,7 @@ def test_one_episode(env, act, device):
     record_output = []
     record_cost = []
     record_unbalance = []
-    record_system_info = []  # [time price,netload,action,real action, output*4,soc,unbalance(exchange+penalty)]
+    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()
@@ -207,7 +231,8 @@ def test_one_episode(env, act, device):
         record_unbalance.append(env.unbalance)
         state = next_state
     # add information of last step dg1, dh2, dg3, soc
-    record_system_info[-1][7:10] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2]]
+    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, 'cost': record_cost, 'unbalance': record_unbalance,