改进的多目标差分进化算法在电力系统环境经济调度中的应用（Python代码实现）【电气期刊论文复现】

#!/usr/bin/env python
# coding:utf-8
import numpy as np
import math
# from DE.initial import create_child, initialize
from constraint import constraints
"""=============读取data.txt数据==================="""
def inital_model(file):  #file：data.txt这个文件
    data = open(file, 'r').readlines()   #读取整个文件所有行，保存在一个列表(list)变量中，每行作为一个元素
    num = int(data[0].split()[-1])    #切割字符串，结果返回由字符串元素组成的一个列表,得到num=6（机组个数）
    C = np.zeros([num, 5])   #存放煤耗特性系数
    E = np.zeros([num, 5])  #存放排放特性系数
    P = np.zeros([num, 2])  #存放机组最大出力与最小出力
    B = np.zeros([num, num])  #存放B矩阵
    parameters = len(data[3].split()) - 3  #第3行开始，到3+6=9行（Python从0开始计数）
    for i in range(2, 2 + num):
        raw_data = data[i].split()[1:]
        for j in range(parameters):
            C[i - 2][j] = float(raw_data[j])    #读取煤耗特性系数
        P[i - 2][0] = float(raw_data[-2])    #读取机组下限
        P[i - 2][1] = float(raw_data[-1])    #读取机组上限
    length = len(data[2 + num].split()) - 1
    for i in range(3 + num, 3 + 2 * num):    #读取排放特性系数
        raw_data = data[i].split()[1:]
        for j in range(length):
            E[i - 3 - num][j] = float(raw_data[j])  #排放特性系数
    for i in range(4 + 2 * num, 4 + 3 * num):   #读取B矩阵
        raw_data = data[i].split()
        B[i - 4 - 2 * num] = np.array(list(map(float, raw_data)))
    B_0 = np.array(list(map(float, data[5 + 3 * num].split())))
    B_00 = float(data[7 + 3 * num])
    return num, C, E, P, B, B_0, B_00
"""=====总燃料成本==========="""
def costfun(uid, load, C, P=None):
    if P is not None:  #如果满足机组上下限
        return load * (C[uid][2] * load + C[uid][1]) + C[uid][0] + math.fabs(C[uid][3] *
                                                                             math.sin(C[uid][4] * (P[uid][0] - load)))
    return load * (C[uid][2] * load + C[uid][1]) + C[uid][0]
"""======总污染排放量=========="""
def emission(uid, load, E, flag=True):
    if E[0][3] != 0 and flag:
        return (E[uid][0] + (E[uid][1] + E[uid][2] * load) * load) + E[uid][3] * math.exp(E[uid][4] * load)
    else:
        return load * (E[uid][2] * load + E[uid][1]) + E[uid][0]
class Model:
    def __init__(self, file):
        self.nGen, self.C, self.E, self.P, self.B, self.B_0, self.B_00 = inital_model(file)
    def constraint(self):
        return constraints
""'======运行===================='
if __name__ == '__main__':
    demand = 2.834   #负荷需求
    model = Model('../data.txt')
    print("===============排放系数===============")
    print(model.E)
    exit(0)
    pop = np.array([0.1917, 0.3804, 0.5603, 0.7154, 0.6009, 0.3804])  #六个机组出力
    fuel = 0
    emis = 0
    for i in range(len(pop)):   #遍历六个机组
        fuel += costfun(i, pop[i], model.C, model.P)
        emis += emission(i, pop[i], model.E, flag=True)
    print(fuel, emis)
    print(constraints(pop, model, demand))
    exit(0)

"""========开始运行============="""
if __name__ == '__main__':
    demand = 2.834
    model = Model('../data.txt')
    arguments = {'nIter': 1000, 'nPop': 200, 'nArc': 100, 'nGen': 6, 'F': 0.6, 'CR': 0, 'init': 1, 'mutation': 0}
    DE = MMODE(model=model, **arguments)
    DE.solve(demand)
    print(DE.finalY.shape)
    n = range(DE.nIter)
    fig, ax1 = plt.subplots()
    color = 'tab:red'
    ax1.set_xlabel('迭代次数')
    ax1.set_ylabel('最佳燃料成本($/h)', color=color)
    ax1.plot(n, DE.bestC, color=color)
    ax1.tick_params(axis='y', labelcolor=color)
    ax2 = ax1.twinx()  # second y axis
    color = 'tab:blue'
    ax2.set_ylabel('最佳污染排放量(t/h)', color=color)
    ax2.plot(n, DE.bestE, color=color)
    ax2.tick_params(axis='y', labelcolor=color)
    plt.title('最佳成本和排放')
    fig.tight_layout()
    plt.show()