1 简介

分布式能源系统是能源利用的未来趋势,其中协同经济优化运行是实现能量供需平衡、 降低能源站成本的关键.首先从冷热电协同优化运行入手,建立了包括蓄电池、海水发电以及风光储在内的分布式能源协同运行优化模型,然后考虑设备约束和系统约束,目标函数综合考虑运行成本和环境成本,采用粒子群优化算法求解.结果表明,针对国内某示范园区分布式能源系统进行优化验证,所提方法能够有效降低总成本,提高分布式能源系统经济效益,促进可再生能源充分消纳.

2 部分代码

clear
clc
close all
%% 参数初始化
pso_option = struct('c1',2.05,'c2',2.05,'maxgen',5000,'sizepop',30, ...
    'k',0.6,'wV',1.1,'wP',1.1,'v',5, ...
    'popmax',30,'popmin',-30);
D=10;
Vmax = pso_option.k*pso_option.popmax;
Vmin = -Vmax ;
eps = 10^(-5);
%% 产生初始粒子和速度
for i=1:pso_option.sizepop
    % 随机产生种群和速度
    pop(i,:) = (pso_option.popmax-pso_option.popmin)*rand(1,D)+pso_option.popmin;
    V(i,:)=Vmax*rands(1,D);
    % 计算初始适应度
    fitness(i)=myfunc_fit1(pop(i,:));
end
% 找极值和极值点
[global_fitness bestindex]=min(fitness); % 全局极值
local_fitness=fitness;   % 个体极值初始化
global_x=pop(bestindex,:);   % 全局极值点
local_x=pop;    % 个体极值点初始化
% 每一代种群的平均适应度
avgfitness_gen = zeros(1,pso_option.maxgen);
%% 迭代寻优
for i=1:pso_option.maxgen
    for j=1:pso_option.sizepop
        %速度更新
        V(j,:) = pso_option.wV*V(j,:) + pso_option.c1*rand*(local_x(j,:) - pop(j,:)) + pso_option.c2*rand*(global_x - pop(j,:));
        if find(V(j,:) > Vmax)
           V_maxflag=find(V(j,:) > Vmax);
            V(j,V_maxflag) = Vmax;
        end
        if find(V(j,1) < Vmin)
            V_minflag=find(V(j,1) < Vmin);
            V(j,V_minflag) = Vmin;
        end
        %种群更新
        pop(j,:)=pop(j,:) + pso_option.wP*V(j,:);
        if find(pop(j,:) > pso_option.popmax)
            pop_maxflag=find(pop(j,:) > pso_option.popmax);
            pop(j,pop_maxflag) = pso_option.popmax;
        end
        if find(pop(j,:) < pso_option.popmin)
            pop_minflag=find(pop(j,:) < pso_option.popmin);
            pop(j,pop_minflag) = pso_option.popmin;
        end
        % 自适应粒子变异
        if rand>0.5
            k=ceil(2*rand);
            pop(j,k) = (pso_option.popmax-pso_option.popmin)*rand + pso_option.popmin;
        end
        %适应度值
         fitness(j)=myfunc_fit1(pop(j,:));
        %个体最优更新
        if fitness(j) < local_fitness(j)
            local_x(j,:) = pop(j,:);
            local_fitness(j) = fitness(j);
        end
        if fitness(j) == local_fitness(j) && length(pop(j,:) < local_x(j,:))
            local_flag=find(pop(j,:) < local_x(j,:));
            local_x(j,local_flag) = pop(j,local_flag);
            local_fitness(j) = fitness(j);
        end
        %群体最优更新
        if fitness(j) < global_fitness
            global_x = pop(j,:);
            global_fitness = fitness(j);
        end
%         if abs( fitness(j)-global_fitness )<=eps && length(pop(j,:) < global_x)
%             global_flag=find(pop(j,:) < global_x);
%             global_x(global_flag) = pop(j,global_flag);
%             global_fitness = fitness(j);
%         end
    end
    fit_gen(i)=global_fitness;
    avgfitness_gen(i) = sum(fitness)/pso_option.sizepop;
end
xlswrite('fit_gen.xlsx',fit_gen);
xlswrite('avgfitness_gen.xlsx',avgfitness_gen);
%% 结果分析
figure;
hold on;
plot(fit_gen,'r*-','LineWidth',1.5);
plot(avgfitness_gen,'o-','LineWidth',1.5);
legend('最佳适应度','平均适应度');
xlabel('进化代数','FontSize',12);
ylabel('适应度','FontSize',12);
grid on;
bestX = global_x;
bestCVmse = fit_gen(pso_option.maxgen);
line1 = '适应度曲线MSE[PSOmethod]';
line2 = ['(参数c1=',num2str(pso_option.c1), ...
    ',c2=',num2str(pso_option.c2),',终止代数=', ...
    num2str(pso_option.maxgen),',种群数量pop=', ...
    num2str(pso_option.sizepop),')'];
title({line1;line2},'FontSize',12);

3 仿真结果

4 参考文献

[1]王禹, 彭道刚, 姚峻,等. 基于改进粒子群算法的分布式能源系统协同优化运行研究[J]. 浙江电力, 2019, 038(002):33-39.

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部分理论引用网络文献，若有侵权联系博主删除。