131 lines
5.7 KiB
Python
131 lines
5.7 KiB
Python
import pandas as pd
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from gurobipy import Model, GRB, quicksum
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import json
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# 读取数据
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data = pd.read_csv("data.csv")
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price = data['price']
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load = data['load']
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pv = data['pv']
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wind = data['wind']
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# 总数据量和周期数
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total_period = len(data)
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days = total_period // 24
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# 存储所有结果
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all_results = []
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for day in range(days):
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# 获取每天的24条数据
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start_idx = day * 24
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end_idx = (day + 1) * 24
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daily_price = price[start_idx:end_idx]
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daily_load = load[start_idx:end_idx]
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daily_pv = pv[start_idx:end_idx]
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daily_wind = wind[start_idx:end_idx]
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# 初始化模型
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m = Model("OptimizationProblem_Day{}".format(day))
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# 决策变量
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Ag1 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag1")
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Ag2 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag2")
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Ag3 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="ag3")
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Apv = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="apv")
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Asoc = m.addVars(24, vtype=GRB.CONTINUOUS, lb=-1, ub=1, name="asoc")
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# 定义变量相关的约束
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soc = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0.2, ub=0.8, name="soc")
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pg1 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=150, name="pg1")
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pg2 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=375, name="pg2")
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pg3 = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=500, name="pg3")
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pv_power = m.addVars(24, vtype=GRB.CONTINUOUS, name="pv_power")
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# 设置交换约束
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grid_energy_export = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='export')
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grid_energy_import = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, ub=100, name='import')
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excess_energy = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, name='excess')
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shortage_energy = m.addVars(24, vtype=GRB.CONTINUOUS, lb=0, name='shortage')
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# 初始值约束
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m.addConstr(soc[0] == 0.2, name="soc_init")
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m.addConstr(pg1[0] == 0, name="pg1_init")
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m.addConstr(pg2[0] == 0, name="pg2_init")
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m.addConstr(pg3[0] == 0, name="pg3_init")
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# 添加soc和pg的递推约束
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m.addConstrs((soc[t] == soc[t - 1] + 0.18 * Asoc[t] for t in range(1, 24)), name="soc_update")
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m.addConstrs((pg1[t] == pg1[t - 1] + 100 * Ag1[t] for t in range(1, 24)), name="pg1_update")
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m.addConstrs((pg2[t] == pg2[t - 1] + 100 * Ag2[t] for t in range(1, 24)), name="pg2_update")
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m.addConstrs((pg3[t] == pg3[t - 1] + 200 * Ag3[t] for t in range(1, 24)), name="pg3_update")
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# 光伏发电功率
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m.addConstrs((pv_power[t] == daily_pv.iloc[t] * (1 + Apv[t]) for t in range(24)), name="pv_power_update")
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# 供需平衡约束
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m.addConstrs((pg1[t] + pg2[t] + pg3[t] + pv_power[t] + daily_wind.iloc[t] + grid_energy_import[t] ==
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daily_load.iloc[t] + 0.18 * Asoc[t] + grid_energy_export[t] + excess_energy[t] - shortage_energy[t]
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for t in range(24)), name="power_balance")
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# excess_energy 和 shortage_energy 约束
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m.addConstrs(
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(excess_energy[t] == pg1[t] + pg2[t] + pg3[t] + pv_power[t] + daily_wind.iloc[t] + grid_energy_import[t]
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- daily_load.iloc[t] - 0.18 * Asoc[t] - grid_energy_export[t] for t in range(1, 24)),
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name="excess_energy")
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m.addConstrs((shortage_energy[t] == daily_load.iloc[t] + 0.18 * Asoc[t] + grid_energy_export[t]
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- pg1[t] - pg2[t] - pg3[t] - pv_power[t] - daily_wind.iloc[t] - grid_energy_import[t]
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for t in range(1, 24)), name="shortage_energy")
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# 成本函数部分
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battery_cost = quicksum((Asoc[t] + 0.1 * soc[t]) for t in range(1, 24))
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gen1_cost = quicksum((0.0034 * pg1[t] ** 2 + 3 * pg1[t] + 30) for t in range(1, 24))
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gen2_cost = quicksum((0.001 * pg2[t] ** 2 + 10 * pg2[t] + 40) for t in range(1, 24))
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gen3_cost = quicksum((0.001 * pg3[t] ** 2 + 15 * pg3[t] + 70) for t in range(1, 24))
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renewable_cost = quicksum((0.01 * pv_power[t] + 0.01 * daily_wind.iloc[t]) for t in range(24))
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# 计算惩罚和收益
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excess_penalty = quicksum(excess_energy[t] * 50 for t in range(24))
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shortage_penalty = quicksum(shortage_energy[t] * 50 for t in range(24))
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sales_revenue = quicksum(0.5 * daily_price.iloc[t] * grid_energy_export[t] for t in range(24))
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purchase_cost = quicksum(daily_price.iloc[t] * grid_energy_import[t] for t in range(24))
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# 设置目标函数
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objective = (battery_cost + gen1_cost + gen2_cost + gen3_cost + renewable_cost +
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excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
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m.setObjective(objective, GRB.MINIMIZE)
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# 求解
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m.optimize()
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if m.status == GRB.OPTIMAL:
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day_results = []
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for t in range(24):
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gen1_cost = 0.0034 * pg1[t].x ** 2 + 3 * pg1[t].x + 30
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gen2_cost = 0.001 * pg2[t].x ** 2 + 10 * pg2[t].x + 40
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gen3_cost = 0.001 * pg3[t].x ** 2 + 15 * pg3[t].x + 70
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battery_cost = Asoc[t].x + 0.1 * soc[t].x
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renewable_cost = 0.01 * pv_power[t].x + 0.01 * daily_wind.iloc[t]
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excess_penalty = excess_energy[t].x * 50
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shortage_penalty = shortage_energy[t].x * 50
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sales_revenue = 0.5 * daily_price.iloc[t] * grid_energy_export[t].x
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purchase_cost = daily_price.iloc[t] * grid_energy_import[t].x
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current_obj = (gen1_cost + gen2_cost + gen3_cost + battery_cost + renewable_cost +
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excess_penalty + shortage_penalty - sales_revenue + purchase_cost)
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day_results.append([
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Asoc[t].x,
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Ag1[t].x,
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Ag2[t].x,
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Ag3[t].x,
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Apv[t].x,
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])
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# 保存当天的结果
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all_results.extend(day_results)
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# 最后将所有结果保存到一个JSON文件
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with open("./results_day.json", "w") as f:
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json.dump(all_results, f, indent=4)
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