改进多目标粒子群算法计算微电网多目标调度 Multisim仿真

ID:122912 · 发表于 2016-10-21 16:29

以微电网为模型，以成本和网络损耗为优化目标，采用多目标粒子群算法对电网进行优化。仿真结果表明，收敛性较好。代码直接可运行。

改进多目标粒子群算法计算微电网多目标调度.zip (11.7 KB, 下载次数: 22)

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部分源码：

function cost=shiyingzhi_zonghexiaoyi_all_2(PGT)
%适应值函数
%--------------------------------------------------------------------------
% PGT=[ 2.7238 0.3086 0.3078 0.3076 0.6724 0.8129 0.8239 0.8293 0.8240 0.8131 0.6770 0.8127 0.8150 0.8182 0.8286 0.6529 0.8358 2.6527 4.6283 4.6611 1.6757 1.6930 0.8180 0.6920
% 4.0000 3.4152 3.3845 3.4100 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000 4.0000
% 4.0000 0 0 0 0 0.8463 2.3894 2.8585 2.3982 0.8902 0 0.8038 1.3032 1.7781 2.8045 0 3.3269 4.0000 4.0000 4.0000 4.0000 4.0000 1.3820 0
% 0.2762 0.2762 0.3077 0.2824 0.3276 0.3408 0.2867 0.3122 0.2778 0.2967 0.3230 0.3836 0.3818 0.4037 0.3669 0.3471 0.3373 0.3473 0.3717 0.3389 0.3243 0.3070 0.3000 0.3080];
T=24;
N=4;
% 输入原始数据,包括各时段负荷大小，发电机有功输出上下限，发电机耗量成本系数，各时段风电场预测的平均输出功率
pmax(1)=7;pmin(1)=0;
pmax(2)=4;pmin(2)=0;
pmax(3)=4;pmin(3)=0;
% a=[1000,970,700,680,450,370,480,660,665,670];
% b=[16.19,17.26,16.60,16.50,19.70,22.26,27.74,25.92,27.27,27.79];
% c=[0.00048,0.00031,0.002,0.00211,0.00398,0.00712,0.00079,0.00413,0.00222,0.00173];
% 柴油发电机的耗量参数
a=0.4333;b=0.2333;c=0.0071;
% 微型燃气轮机,P_mt表示燃气轮机发出的功率，Xl_mt表示燃气轮机的效率
Price_mt=0.1;
% Xl_mt=0.0753*(P_mt/65)^3-0.3095*(P_mt/65)^2+0.1068;
% 燃料电池
Xl_fc=0.4;
Price_fc=0.2;
% Cost_fc=Price_fc*P_fc/Xl_fc;
% 污染物排放成本
% aa表示NOX，bb表示FC,cc表示DG
aa=[21.8,0.03,0.44];
bb=[0.454,0.006,0.008];
cc=[1.432,1.078,1.596];
% PL=[11,4,4,4,5,6,7.5,8,7.5,6,5,6,6.5,7,8,5,8.5,11,13,13,10,10,6.5,5];
PL=[3,4,4,4,5,6,6.5,7,7.5,8.5,9,10,10.5,10,9,8.5,9,10,11,11.5,10,9,5.5,5];
W=[37.35,43.52,41.81,41.54,47.29,42.10,44.74,33.11,23.98,22.70,30.76,29.23,24.25,45.37,26.82,27.71,47.12,54.10,44.52,49.51,50.67,44.35,35.56,17.67];
%--------------------------------------------------------------------------
C=zeros(1,T);
Cost=0;
chengben=0;
wuranwu=0;
zonghexiaoyi=0;
s1=zeros(1,T);
s2=zeros(1,T);
s=zeros(1,T);
for jj=1:T
s1(jj)=a+b*PGT(1,jj)+c*PGT(1,jj)*PGT(1,jj)+Price_fc*PGT(2,jj)/Xl_fc+Price_mt*PGT(3,jj)/(0.0753*(PGT(3,jj)/65)^3-0.3095*(PGT(3,jj)/65)^2+0.4174*PGT(3,jj)/65+0.1068);
s2(jj)=4.2*(aa(1)*PGT(1,jj)+aa(2)*PGT(2,jj)+aa(3)*PGT(3,jj))/1000+0.99*(bb(1)*PGT(1,jj)+bb(2)*PGT(2,jj)+bb(3)*PGT(3,jj))/1000+0.014*(cc(1)*PGT(1,jj)+cc(2)*PGT(2,jj)+cc(3)*PGT(3,jj))/1000;
% s=a+b*PGT(1,jj)+c*PGT(1,jj)*PGT(1,jj)+Price_fc*PGT(2,jj)/Xl_fc+Price_mt*PGT(3,jj)/(0.0753*(PGT(3,jj)/65)^3-0.3095*(PGT(3,jj)/65)^2+0.4174*PGT(3,jj)/65+0.1068)+4.2*(aa(1)*PGT(1,jj)+aa(2)*PGT(2,jj)+aa(3)*PGT(3,jj))/1000+0.99*(bb(1)*PGT(1,jj)+bb(2)*PGT(2,jj)+bb(3)*PGT(3,jj))/1000+0.014*(cc(1)*PGT(1,jj)+cc(2)*PGT(2,jj)+cc(3)*PGT(3,jj))/1000;
% Cost=Cost+s;
s(jj)=s1(jj)+s2(jj);
Cost=Cost+s(jj);
chengben=chengben+s1(jj);
wuranwu=wuranwu+s2(jj);
end
xx=1:T;
figure(4);
plot(xx,s1,'-bdiamond',xx,s2,'-rsquare',xx,s,'-k*'); % 绘制24小时的各微电源的功率曲线图
xlabel('t/h');ylabel('目标成本/
);
cost=Cost;

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ID:338675 · 发表于 2018-5-26 13:39

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改进多目标粒子群算法计算微电网多目标调度 Multisim仿真

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