目录
1 主要内容
2 部分代码
3 程序结果
4 下载链接
1 主要内容
该程序对应文章《Contract Design for Energy Demand Response》，电力系统需求响应（DR）用来调节用户对电能的需求，即在预测的需求高于电能供应时，希望通过需求响应减少用户用电，从而满足系统平衡。程序实现新的需求响应模型DR-VCG，该模型提供了灵活的用户参与DR过程合同，并且通过投标活动保证收益分配和价格计算的合理性。通过实例证实该方法的有效性，可靠性显著提升，总费用明显降低。该程序采用python编写。
2 部分代码
import grid
import agent
import contract
import matplotlib.pyplot as plt
import time
import statistics
import pandas as pd
from datetime import datetime
def main():for i in range(1):M = 1000number_of_agents = 200number_of_simulation_per_lanbda = 100generator_price_multiply = 1gamma = [1.0,1.166,1.333,1.5,1.666,1.833,2]df_columns = ['actuel expanse','gamma','M','actuel kWh reduced','Met the demand']row_data_df = pd.DataFrame(columns=df_columns)
Fixed_cont_avg_cost = []Fixed_cont_avg_reliability = []Fixed_single_cont_avg_cost = []Fixed_single_cont_avg_reliability = []T_F_List_Fixed_cont_Met_the_demand = []Fixed_cont_Total_expense = []gamma_used = []for lb in gamma:Fixed_cont_reduce_list = []for i in range(number_of_simulation_per_lanbda):start = time.time()print('iteration:',i)Grid = grid.grid(M,lb)Grid.introduce_self()Agents = []
for num in range(number_of_agents):ag = agent.agent(num)Agents.append(ag)
Contracts = []for i in range(10, M+1, 10):Contracts.append(contract.contract(i,0.3,0.5))
single_contract = []single_contract.append(contract.contract(50,0.3,0.5))
Grid.set_single_contract(single_contract)Grid.set_contract(Contracts)Grid.set_agents(Agents)Grid.send_contrects_to_agents()Grid.send_single_contrects_to_agents()
for ag in Agents:ag.Fixed_cont_bid_on_contract()
for ag in Agents:ag.Fixed_single_cont_bid_on_contract()
Grid.Fixed_cont_get_bids_from_agent()Grid.Fixed_cont_generator_bids(price_multiply=generator_price_multiply)Grid.Fixed_cont_get_q_from_agent()Fixed_cont_sum_of_bids = Grid.knapsack(bids_type='Fixed_cont')Grid.Fixed_cont_pay_to_agents(Fixed_cont_sum_of_bids)Grid.Fixed_cont_reliability()Grid.Fixed_single_cont_get_bids_from_agent()Grid.Fixed_single_cont_get_q_from_agent()
Fixed_cont_Total_expense.append(Grid.Fixed_cont_Total_expense_sum)Fixed_cont_reduce_list.append(Grid.Fixed_cont_reliability_sum_q)if Grid.Fixed_cont_reliability_sum_q >= Grid.M:met_the_demand = 1else:met_the_demand = 0T_F_List_Fixed_cont_Met_the_demand.append(met_the_demand)print('Fixed_cont- Met_the_demand: ', T_F_List_Fixed_cont_Met_the_demand)print('Fixed_cont- Total_expense: ', Fixed_cont_Total_expense)gamma_used.append(lb)
row_data_df = row_data_df.append(pd.DataFrame({'actuel expanse':[Grid.Fixed_cont_Total_expense_sum],'gamma':[lb],'M': [M],'actuel kWh reduced': [Grid.Fixed_cont_reliability_sum_q],'Met the demand': [met_the_demand]}))end = time.time()print('iteration took:', (end - start), 'sec')print('-'*200)Fixed_cont_avg_cost.append(statistics.mean(Fixed_cont_Total_expense))if len(T_F_List_Fixed_cont_Met_the_demand) > 0:Fixed_cont_avg_reliability.append(T_F_List_Fixed_cont_Met_the_demand.count(True) / len(T_F_List_Fixed_cont_Met_the_demand))else:Fixed_cont_avg_reliability.append(0.0)
filename = datetime.now().strftime('data/energy_demamd_row_data-%Y-%m-%d-%H-%M-%S.csv')row_data_df.to_csv(filename,index=False)graph_it(Fixed_cont_avg_reliability,Fixed_single_cont_avg_reliability, Fixed_cont_avg_cost,Fixed_single_cont_avg_cost)
def graph_it(Fixed_cont_avg_reliability =[],Fixed_single_cont_avg_reliability=[],Fixed_cont_avg_cost=[],Fixed_single_cont_avg_cost=[]):plt.rcParams["figure.figsize"] = (8, 8)fig, ax = plt.subplots()
ax.plot(Fixed_cont_avg_reliability, Fixed_cont_avg_cost, color='blue',marker='o',label="fixed multiple cont")ax.plot(Fixed_single_cont_avg_reliability, Fixed_single_cont_avg_cost, color='black',marker='o', label="fixed single cont")ax.set(xlabel="Total_Reliability", ylabel="expenses ($)", title="(a)n= 400")fig.savefig("test.png")
if __name__ == "__main__":main()plt.show()
3 程序结果
原文结果图：
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