[能源管理系统（EMS）] 基于粒子群算法的分布式能源发电机（如光伏和电池）规模优化调度研究（Matlab 代码实现
最编程
2024-04-05 11:10:12
...
%% Main PSO
for n_ite=1:set.Niteration
for n_par=1:set.Nparticle
[LPSP,COE]=EMS(particle(n_par).position(1),...
particle(n_par).position(2),...
particle(n_par).position(3));
%% Calculate Mark
Mark=set.weight_LPSP*abs(LPSP-set.desired_LPSP)+...
set.weight_COE*COE/set.Normal_COE;
%% Best Particle
if isempty(particle(n_par).best_Mark) || particle(n_par).best_Mark>Mark
particle(n_par).best_position=particle(n_par).position;
particle(n_par).best_LPSP=LPSP;
particle(n_par).best_COE=COE;
particle(n_par).best_Mark=Mark;
end
%% Best Global
if (n_ite==1 && n_par==1) || best_global.Mark>Mark
best_global.position=particle(n_par).position;
best_global.LPSP=LPSP;
best_global.COE=COE;
best_global.Mark=Mark;
end
log_global(n_ite)=best_global;
%% Velocity and New Position
particle(n_par).velocity=set.w*particle(n_par).velocity...
+set.c1*(particle(n_par).best_position-particle(n_par).position)...
+set.c2*(best_global.position-particle(n_par).position);
particle(n_par).position=particle(n_par).position...
+particle(n_par).velocity;
%% Round Position
particle(n_par).position(1)=round(particle(n_par).position(1));
particle(n_par).position(2)=round(particle(n_par).position(2));
particle(n_par).position(3)=round(particle(n_par).position(3));
%% Limit Position
if particle(n_par).position(1)<set.Npv_min
particle(n_par).position(1)=set.Npv_min;
end
if particle(n_par).position(2)<set.Nbat_min
particle(n_par).position(2)=set.Nbat_min;
end
if particle(n_par).position(3)<set.Ndg_min
particle(n_par).position(3)=set.Ndg_min;
end
if particle(n_par).position(1)>set.Npv_max
particle(n_par).position(1)=set.Npv_max;
end
if particle(n_par).position(2)>set.Nbat_max
particle(n_par).position(2)=set.Nbat_max;
end
if particle(n_par).position(3)>set.Ndg_max
particle(n_par).position(3)=set.Ndg_max;
end
end
end
clear LPSP COE Mark n_ite n_par
%% Show Result
for n_ite=1:set.Niteration
LPSP(n_ite)=log_global(n_ite).LPSP;
COE(n_ite)=log_global(n_ite).COE;
end
subplot(2,1,1);
plot(LPSP);
grid on;
xlabel('n-th Iteration')
ylabel('Loss of Load Probability, LPSP');
subplot(2,1,2);
plot(COE);
grid on;
xlabel('n-th Iteration')
ylabel('Cost of Energy, COE ($)');
tpro=toc;
fprintf('The optimum system size is:\n Npv=%d\n Nbat=%d\n Ndg=%d\nwith the LPSP = %.3f%% and COE = $%.2f\nCompute in %.2f s\n',...
best_global.position,best_global.LPSP*100,best_global.COE,tpro);
beep;