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 = 1000 number_of_agents = 200 number_of_simulation_per_lanbda = 100 generator_price_multiply = 1 gamma = [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 = 1 else: met_the_demand = 0 T_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:
4 程序链接
关注+私信免费获取程序源码和资料!