智能微电网粒子群优化算法,微源：光伏、风机、发电机、储能等.zip

clear clc format long; %初始化条件**************************************** %微型燃气轮机最大功率 MTMaxPower=5; %微型燃气轮机最小功率 MTMinPower=1; %电网输入微网最大功率 GridMaxImportPower=250; %电网输入微网最小功率 GridMinImportPower=10; %储能最大放电功率 StorageMaxDischargingPower=150; %储能最大充电功率 StorageMaxChargingPower=-100; Max_Dt=300;%最大迭代次数300 D=72;%搜索空间维数（未知数个数） N=600;%粒子个数600 w_max=0.9; w_min=0.4; v_max=2; s=1; %初始化种群个体（位置和速度）*********************** for i=1:N for j=1:72 % v(i,j)=randn; v(i,j)=0.0; if j<25 x(i,j)=MTMinPower+rand()*(MTMaxPower-MTMinPower); elseif j>24&&j<49 x(i,j)=GridMinImportPower+rand()*(GridMaxImportPower-GridMinImportPower); elseif j>48&&j<73 x(i,j)=StorageMaxChargingPower+rand()*(StorageMaxDischargingPower-StorageMaxChargingPower); end end end %计算各个粒子的适应度，并初始化Pi和Pg**************** for i=1:N p(i)=fitness(x(i,:),s); y(i,:)=x(i,:);%每个粒子的个体寻优值 end Pbest=fitness(x(1,:),s); pg=x(1,:);%Pg为全局最优 for i=2:N if fitness(x(i,:),s)<fitness(pg,s) Pbest=fitness(x(i,:),s); pg=x(i,:);%全局最优更新 end end %进入主循环***************************************** for t=1:Max_Dt for i=1:N w=w_max-(w_max-w_min)*t/Max_Dt;%惯性权重更新 c1=(0.5-2.5)*t/Max_Dt+2.5; %认知 c2=(2.5-0.5)*t/Max_Dt+0.5; %社会认识 % w=0.7; % c1=2.05; %认知 % c2=2.05; %社会认识 v(i,:)=w*v(i,:)+c1*rand()*(y(i,:)-x(i,:))+c2*rand()*(pg-x(i,:)); for m=1:72 if(v(i,m)>v_max) v(i,m)=v_max; elseif(v(i,m)<-v_max) v(i,m)=-v_max; end end x(i,:)=x(i,:)+v(i,:); %对粒子边界处理***************************** for n=1:72 if n<25 if x(i,n)<MTMinPower x(i,n)=MTMinPower; v(i,n)=-v(i,n); elseif x(i,n)>MTMaxPower x(i,n)=MTMaxPower; v(i,n)=-v(i,n); else delt(i,n)=0; end elseif n>24&&n<49 if x(i,n)<GridMinImportPower x(i,n)=GridMinImportPower; v(i,n)=-v(i,n); elseif x(i,n)>GridMaxImportPower x(i,n)=GridMaxImportPower; v(i,n)=-v(i,n); else delt(i,n)=0; end else if x(i,n)<StorageMaxChargingPower x(i,n)=StorageMaxChargingPower; v(i,n)=-v(i,n); elseif x(i,n)>StorageMaxDischargingPower x(i,n)=StorageMaxDischargingPower; v(i,n)=-v(i,n); else delt(i,n)=0; end end end %对粒子进行评价，寻找最优值****************** if fitness(x(i,:),t)<p(i) p(i)=fitness(x(i,:),t); y(i,:)=x(i,:); end if p(i)<Pbest Pbest=p(i); pg=y(i,:); s=t; end end %Pbest(t)=fitness(pg,s); end disp('*************************************************************') disp('函数的全局最优位置为：') Solution=pg' for m=1:24 pg1(m)=pg(m); end for m=25:48 pg2(m-24)=pg(m); end for m=49:72 pg3(m-48)=pg(m); end figure subplot(311) plot( pg1,'-*') xlim([1 24]) grid title('MT运行计划') subplot(312) plot( pg2,'-*') xlim([1 24]) grid title('GRID运行计划') subplot(313) plot( pg3,'-*') xlim([1 24]) grid title('BA运行计划') disp('最后得到的优化极值为：') Result=economic(pg) Resulta=fitness(pg,s) s disp('*************************************************************')