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⛄ 内容介绍
根据微燃机冷热电联供的优化配置步骤及考虑因素,对微燃机冷热电联供系统的几种方案进行了研究.确立了以年运行费用最小为目标的函数及约束条件,在选定建筑物,满足其冷热电负荷的情况下,对几种运行方案分别进行了优化,得出了适合该建筑的最优方案,并对其运行策略进行了分析.讨论了天然气价格,微燃机价格对联供系统投资回收期的影响.
⛄ 部分代码
%%%%%%%%%%%%%%%%%粒子群算法求函数极值%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%初始化%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all; %清除所有变量
close all; %清图
clc; %清屏
N=100; %群体粒子个数
D=10; %粒子维数
T=200; %最大迭代次数
c1=1.5; %学习因子1
c2=1.5; %学习因子2
w=0.8; %惯性权重
Xmax=20; %位置最大值
Xmin=-20; %位置最小值
Vmax=10; %速度最大值
Vmin=-10; %速度最小值
%%%%%%%%%%%%%%%%初始化种群个体（限定位置和速度）%%%%%%%%%%%%%%%%
x=rand(N,D) * (Xmax-Xmin)+Xmin;
v=rand(N,D) * (Vmax-Vmin)+Vmin;
%%%%%%%%%%%%%%%%%%初始化个体最优位置和最优值%%%%%%%%%%%%%%%%%%%
p=x;
pbest=ones(N,1);
for i=1:N
pbest(i)=func1(x(i,:));
end
%%%%%%%%%%%%%%%%%%%初始化全局最优位置和最优值%%%%%%%%%%%%%%%%%%
g=ones(1,D);
gbest=inf;
for i=1:N
if(pbest(i)<gbest)
g=p(i,:);
gbest=pbest(i);
end
end
gb=ones(1,T);
%%%%%%%%%%%按照公式依次迭代直到满足精度或者迭代次数%%%%%%%%%%%%%
for i=1:T
for j=1:N
%%%%%%%%%%%%%%更新个体最优位置和最优值%%%%%%%%%%%%%%%%%
if (func1(x(j,:))<pbest(j))
p(j,:)=x(j,:);
pbest(j)=func1(x(j,:));
end
%%%%%%%%%%%%%%%%更新全局最优位置和最优值%%%%%%%%%%%%%%%
if(pbest(j)<gbest)
g=p(j,:);
gbest=pbest(j);
end
%%%%%%%%%%%%%%%%%跟新位置和速度值%%%%%%%%%%%%%%%%%%%%%
v(j,:)=w*v(j,:)+c1*rand*(p(j,:)-x(j,:))...
+c2*rand*(g-x(j,:));
x(j,:)=x(j,:)+v(j,:);
%%%%%%%%%%%%%%%%%%%%边界条件处理%%%%%%%%%%%%%%%%%%%%%%
for ii=1:D
if (v(j,ii)>Vmax) | (v(j,ii)< Vmin)
v(j,ii)=rand * (Vmax-Vmin)+Vmin;
end
if (x(j,ii)>Xmax) | (x(j,ii)< Xmin)
x(j,ii)=rand * (Xmax-Xmin)+Xmin;
end
end
end
%%%%%%%%%%%%%%%%%%%%记录历代全局最优值%%%%%%%%%%%%%%%%%%%%%
gb(i)=gbest;
end
g; %最优个体
gb(end); %最优值
figure
plot(gb)
xlabel('迭代次数');
ylabel('适应度值');
title('适应度进化曲线')
⛄ 运行结果
⛄ 参考文献
[1]魏兵, 王志伟, 蒋露,等. 微型燃气轮机冷热电联供系统的优化运行研究[J]. 华北电力大学学报：自然科学版, 2007, 34(2):7.
[1]熊军华, 陈艳华. 基于燃气轮机的冷热电联供系统优化配置研究[J]. 节能, 2017, 36(9):5.
