update battery cost
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chenxiaodong
parent
82ccfa7076
commit
ed51c79123
55
tools.py
55
tools.py
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@ -4,6 +4,7 @@ import numpy as np
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import numpy.random as rd
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import pandas as pd
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import torch
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import json
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from gurobipy import GRB
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@ -14,9 +15,6 @@ def optimization_base_result(env, month, day, initial_soc):
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irradiance = env.data_manager.get_series_irradiance_data(month, day)
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wind_speed = env.data_manager.get_series_wind_data(month, day)
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pv = [env.solar.step(temp, irr) for temp, irr in zip(temperature, irradiance)]
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wind = [env.wind.step(ws) for ws in wind_speed]
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period = env.episode_length
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DG_parameters = env.dg_parameters
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@ -28,7 +26,6 @@ def optimization_base_result(env, month, day, initial_soc):
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a_para = []
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b_para = []
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c_para = []
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for name, gen_info in parameters.items():
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p_max.append(gen_info['power_output_max'])
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p_min.append(gen_info['power_output_min'])
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@ -44,7 +41,10 @@ def optimization_base_result(env, month, day, initial_soc):
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battery_capacity = env.battery.capacity
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battery_efficiency = env.battery.efficiency
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solar_cofficient = env.solar.opex_cofficient
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volatage_change_percent = env.solar.change_percent
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wind_cofficient = env.wind.opex_cofficient
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battery_degradation = env.battery.degradation
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battery_holding = env.battery.holding
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m = gp.Model("UC")
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@ -52,16 +52,24 @@ def optimization_base_result(env, month, day, initial_soc):
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on_off = m.addVars(NUM_GEN, period, vtype=GRB.BINARY, name='on_off')
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gen_output = m.addVars(NUM_GEN, period, vtype=GRB.CONTINUOUS, name='output')
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# 设置充放电约束
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soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name='SOC')
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battery_energy_change = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-env.battery.max_charge,
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ub=env.battery.max_charge, name='battery_action')
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# 设置外部电网与能源系统交换约束
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grid_energy_import = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=env.grid.exchange_ability, name='import')
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grid_energy_export = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0, ub=env.grid.exchange_ability, name='export')
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soc = m.addVars(period, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name='SOC')
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# 设置电压控制约束
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pv_voltage = m.addVars(period, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name='pv_voltage')
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# 计算光伏和风力发电量
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pv = [(0.2 * irradiance[t] + 0.05 * temperature[t] - 9.25) *
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(1 + volatage_change_percent * pv_voltage[t]) for t in range(period)]
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wind = [172.265625 * wind_speed[t] ** 3 / 1e3 if 3 <= wind_speed[t] < 8
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else (172.265625 * 8 ** 3 / 1e3 if 8 <= wind_speed[t] < 12 else 0) for t in range(period)]
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# 1. 添加平衡约束
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m.addConstrs(((sum(gen_output[g, t] for g in range(NUM_GEN)) + pv[t] + wind[t] + grid_energy_import[t] >= load[t] +
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battery_energy_change[t] + grid_energy_export[t]) for t in range(period)), name='powerbalance')
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m.addConstrs(((sum(gen_output[g, t] for g in range(NUM_GEN)) + pv[t] + wind[t] + grid_energy_import[t] >= load[t]
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+ battery_energy_change[t] + grid_energy_export[t]) for t in range(period)), name='powerbalance')
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# 2. 添加发电机最大/最小功率约束
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m.addConstrs((gen_output[g, t] <= on_off[g, t] * p_max[g] for g in range(NUM_GEN) for t in range(period)),
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'gen_output_max')
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@ -81,14 +89,18 @@ def optimization_base_result(env, month, day, initial_soc):
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cost_gen = gp.quicksum(
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(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
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t in range(period) for g in range(NUM_GEN))
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cost_grid_import = gp.quicksum(grid_energy_import[t] * price[t] for t in range(period))
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cost_grid_export = gp.quicksum(grid_energy_export[t] * price[t] * env.sell_coefficient for t in range(period))
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cost_battery = gp.quicksum(battery_energy_change[t] * battery_degradation + soc[t] * battery_holding for t in range(period))
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cost_import = gp.quicksum(grid_energy_import[t] * price[t] for t in range(period))
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cost_export = gp.quicksum(grid_energy_export[t] * price[t] * env.sell_coefficient for t in range(period))
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cost_solar = gp.quicksum(pv[t] * solar_cofficient for t in range(period))
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cost_wind = gp.quicksum(wind[t] * wind_cofficient for t in range(period))
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m.setObjective((cost_gen + cost_grid_import - cost_grid_export + cost_solar + cost_wind), GRB.MINIMIZE)
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m.setObjective((cost_gen + cost_battery + cost_import - cost_export + cost_solar + cost_wind), GRB.MINIMIZE)
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m.optimize()
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# 记录数据便于绘图
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pv = [(0.2 * irradiance[t] + 0.05 * temperature[t] - 9.25) *
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(1 + volatage_change_percent * pv_voltage[t].x) for t in range(period)]
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output_record = {'pv': [], 'wind': [], 'price': [], 'load': [], 'netload': [],
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'soc': [], 'battery_energy_change': [], 'grid_import': [], 'grid_export': [],
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'gen1': [], 'gen2': [], 'gen3': [], 'step_cost': []}
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@ -96,8 +108,9 @@ def optimization_base_result(env, month, day, initial_soc):
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gen_cost = sum((on_off[g, t].x * (
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a_para[g] * gen_output[g, t].x * gen_output[g, t].x + b_para[g] * gen_output[g, t].x + c_para[g]))
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for g in range(NUM_GEN))
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grid_import_cost = grid_energy_import[t].x * price[t]
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grid_export_cost = grid_energy_export[t].x * price[t] * env.sell_coefficient
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battery_cost = battery_energy_change[t].x * battery_degradation + soc[t].x * battery_holding
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import_cost = grid_energy_import[t].x * price[t]
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export_cost = grid_energy_export[t].x * price[t] * env.sell_coefficient
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solar_cost = pv[t] * solar_cofficient
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wind_cost = wind[t] * wind_cofficient
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output_record['pv'].append(pv[t])
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@ -112,7 +125,18 @@ def optimization_base_result(env, month, day, initial_soc):
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output_record['gen1'].append(gen_output[0, t].x)
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output_record['gen2'].append(gen_output[1, t].x)
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output_record['gen3'].append(gen_output[2, t].x)
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output_record['step_cost'].append(gen_cost + grid_import_cost - grid_export_cost + solar_cost + wind_cost)
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output_record['step_cost'].append(gen_cost + battery_cost + import_cost - export_cost + solar_cost + wind_cost)
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# 记录动作便于辅助决策
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results = []
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for t in range(period):
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action = [battery_energy_change[t].x/100, (gen_output[0, t].x - (gen_output[0, t-1].x if t > 0 else 0))/100,
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(gen_output[1, t].x - (gen_output[1, t-1].x if t > 0 else 0))/100,
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(gen_output[2, t].x - (gen_output[2, t-1].x if t > 0 else 0))/200,
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pv_voltage[t].x - (pv_voltage[t-1].x if t > 0 else 0)]
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results.append(action)
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with open('./results.json', 'w') as f:
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json.dump(results, f, indent=2)
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output_record_df = pd.DataFrame.from_dict(output_record)
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return output_record_df
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@ -184,7 +208,7 @@ def test_one_episode(env, act, device):
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record_output = []
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record_cost = []
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record_unbalance = []
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record_system_info = [] # [time price,netload,action,real action, output*4,soc,unbalance(exchange+penalty)]
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record_system_info = [] # [time,price,netload,action,real action,soc,output*4,unbalance(exchange+penalty),cost]
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record_init_info = [] # include month,day,time,intial soc
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env.TRAIN = False
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state = env.reset()
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@ -207,7 +231,8 @@ def test_one_episode(env, act, device):
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record_unbalance.append(env.unbalance)
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state = next_state
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# add information of last step dg1, dh2, dg3, soc
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record_system_info[-1][7:10] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2]]
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record_system_info[-1][7:12] = [env.final_step_outputs[0], env.final_step_outputs[1], env.final_step_outputs[2],
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env.final_step_outputs[4], env.final_step_outputs[5]]
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record_system_info[-1][5] = env.final_step_outputs[3]
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record = {'init_info': record_init_info, 'system_info': record_system_info, 'state': record_state,
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'action': record_action, 'reward': record_reward, 'cost': record_cost, 'unbalance': record_unbalance,
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