基于蒙特卡洛模拟法的电动汽车负荷曲线模拟matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); %利用蒙特卡洛模拟法模拟出电动汽车负荷曲线 %同时求解出无序充电功率曲线，作为有序充电曲线的对比基础 clc;clear; Ntest=100;%仿真程序 车辆数 SOC_end=0.9; Pbiao=15;%充电功率为15kW nn=0.9;%充电效率为0.9 Pcharge=Pbiao*nn;%实际充电的功率 Cbattery=30; %电池容量 distance=unifrnd(30,80,1,Ntest); %Ntest辆车 每辆车的单程距离 judge=0.15*distance/Cbattery; %单程耗电SOC SOC=rand(1,Ntest).*(1-judge)+judge; %初始SOC timestart=8; %8点离家 timework=normrnd(8.5,0.5,1,Ntest); %到班时间，服从正态分布 timerest=normrnd(17.5,0.5,1,Ntest); %下班时间 timehome=normrnd(19,0.5,1,Ntest); %到家时间，由于上下班高峰路况复杂，所以不认为下班回家耗时与上班耗时相同 SOC=SOC-judge; battery=SOC*Cbattery; %到班后的电量 time1=zeros(1,Ntest); time2=zeros(1,Ntest); %SOC记录数组 SOC_sa=ones(1,Ntest); SOC_sb=ones(1,Ntest); for i=1:Ntest if SOC(i)<judge+0.2 SOC_sa(i)=SOC(i); time1(i)=timework(i); %到班后需要充电，充电开始时间为到班时间 time2(i)=time1(i)+(1-SOC(i))*Cbattery/Pcharge; %充电结束时间，充电功率Pcharge SOC(i)=SOC_end; %下班前充满电 battery(i)=Cbattery*SOC(i); end end SOC=SOC-judge; battery=SOC*Cbattery; %到家后的电量 time3=zeros(1,Ntest); time4=zeros(1,Ntest); for i=1:Ntest if SOC(i)<max(judge,0.4) SOC_sb(i)=SOC(i); time3(i)=timehome(i); %到家后需要充电，充电开始时间为到班时间 time4(i)=time3(i)+(1-SOC(i))*Cbattery/Pcharge; %充电结束时间，充电功率4KW SOC(i)=SOC_end; %第二天8点前可以充满电 battery(i)=Cbattery*SOC(i); end end time=0:0.1:48; Ycharge=zeros(1,481); roundn(time1,-1); roundn(time2,-1); roundn(time3,-1); roundn(time4,-1); for i=1:Ntest if (time2(i)-time1(i)~=0) kstart=round(10*time1(i)+1); kend=round(10*time2(i)+1); Ycharge(1,kstart:kend)=Ycharge(1,kstart:kend)+1; end if (time4(i)-time3(i)~=0) kstart=round(10*time3(i)+1); kend=round(10*time4(i)+1); Ycharge(1,kstart:kend)=Ycharge(1,kstart:kend)+1; end end temp=Ycharge(1:241)+Ycharge(241:481); x=0:0.1:24; xx=0:0.05:24; tempp = interp1(x,temp,xx,'linear'); Pwuxu=tempp(1:5:481)*Pbiao; %========================================================================= %原电网基础负荷 bsload=1.5*xlsread('baseload',1,'B2:CT2'); Swuxu=bsload+Pwuxu; xt=0:0.25:24; plot(xt,bsload,xt,Swuxu); %plot(xt,Pwuxu); legend('电网原负荷','叠加无序充电负荷后'); xlabel('时间/h'); ylabel('负荷/kW'); %========================================================================= %解有序充电模型 %分时电价赋值 price=zeros(1,96); for j=1:33-1 price(j)=0.365;%谷电价0.365元/kWh end for j=33:4*8+1-1 price(j)=0.869;%峰电价 元/kWh end for j=4*8+1:4*17+1-1 price(j)=0.687;%平电价 元/kWh end for j=4*17+1:4*21+1-1 price(j)=0.869;%峰电价 元/kWh end for j=4*21+1:4*24+1-1 price(j)=0.687;%平电价 元/kWh end deltaT=15/60;%15min折算成小时 cost=0;%购电电价 S=zeros(Ntest,96); %充电开始时间折算成96点形式 J1=zeros(1,Ntest); J2=zeros(1,Ntest); J3=zeros(1,Ntest); J4=zeros(1,Ntest); for temp=1:Ntest J1(temp) =round(4*time1(temp)+1); J2(temp) =round(4*time2(temp)+1); J3(temp) =round(4*time3(temp)+1); J4(temp) =round(4*time4(temp)+1); end %是否充电记录数组 1表示充电 yesfirst=zeros(1,Ntest); yessec=zeros(1,Ntest); %S（ij）赋初值 也就是无序充电初值 for i=1:Ntest %到达单位后的充电情况 if(J2(i)-J1(i)~=0) yesfirst(1,i)=1; jstart=J1(i); jend=J2(i); for temp=jstart:jend S(i,temp)=1; end end %下班后充电情况 if(J4(i)-J3(i)~=0) yessec(1,i)=1; jstart=J3(i); jend=J4(i); for temp=jstart:jend S(i,temp)=1; end end end P_mft=5087;%最大允许负荷5087kW cost_wuxu=0; for i=1:Ntest for j=1:96 cost_wuxu=cost_wuxu+Pbiao*S(i,j)*deltaT*price(j); end end T1=round(timework*4+1); T2=round(timerest*4+1); T3=round(timehome*4+1); S_yx=zeros(Ntest,96); %SS=S; lambda=0.1*ones(1,96);%拉格朗日乘子初值 v=1; obj=10000000000000000;%初值足够大 jingdu=0.1; a=1; b=0.1; die=100; while((v<4)&&(die>jingdu)) L=zeros(1,Ntest); x=zeros(1,96); SS=zeros(Ntest,96); %执行智能充电单元 run('ZN.m'); myk=1/(a+b*v); temp=5087*ones(1,96); mybsload=bsload(1,1:96); myh=mybsload+Pcharge*sum(S_yx)-temp; Tlambda=lambda; lambda=lambda+myk*myh/norm(myh); die=norm(lambda-Tlambda,2)/norm(Tlambda); v=v+1; end PSS=zeros(1,97); PSS(1,1:96)=sum(SS)*Pbiao; PSS(1,97)=PSS(1,1); Syouxu=bsload+PSS; xt=0:0.25:24; plot(xt,bsload,xt,Swuxu,'r:',xt,Syouxu,'g'); legend('电网原负荷','叠加无序充电负荷后','叠加有序充电负荷后'); xlabel('时间/h'); ylabel('负荷/kW');