【需求响应】一种新的需求响应机制DR-VCG研究

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()