matlab figure2无法输出

[y178488294]

clear;
clc;
tic;
c1=[7.35 7.78 7.64 8.36 8.23 8.55 8.23 7.29 6.93 6.75 6.69 6.58 6.66 6.97 6.82 7.21 7.54 8.18 8.56 8.37 8.81 8.56 7.93 7.77
];
k1=[2.23 2.26 1.95 2.27 2.15 1.94 2.01 2.12 2.28 2.29 2.16 2.29 2.28 2.09 2.22 1.96 2.07 2.27 2.22 2.28 1.96 1.91 2.24 2.27
];
c2=[8.84 8.66 8.26 8.02 7.65 7.70 7.40 7.12 6.94 6.86 7.05 6.92 7.02 7.38 7.55 7.69 7.98 8.32 8.78 9.21 9.18 9.04 9.32 9.11
];
k2=[2.01 1.98 1.89 2.04 2.21 2.10 1.88 2.15 1.91 2.01 2.18 1.96 2.04 2.07 1.93 2.21 2.19 2.20 1.91 2.09 2.05 2.11 1.93 2.12
];
c3=[7.72 7.83 7.43 7.14 7.31 7.46 7.80 7.40 7.08 7.23 6.82 6.92 7.49 7.75 7.61 7.89 7.40 7.38 7.70 7.99 8.05 8.18 8.36 8.01
];
k3=[2.09 1.96 1.90 1.97 1.92 2.08 2.08 2.08 1.91 2.03 1.93 1.94 2.06 1.98 2.01 1.98 2.03 1.93 1.92 1.98 1.91 2.08 1.95 2.00
];
n=20000;
for kkk=1:24;
c=[c1(kkk);c2(kkk);c3(kkk)];%给定值
k=[k1(kkk);k2(kkk);k3(kkk)];%给定值
nwf=length(c);%数组长度（即行数或列数中的较大值）
rho=[1 0.76 0.55
0.76 1 0.27
0.55 0.27 1];%3个机组的相关性

y=copularnd('gaussian',rho,n);%形成满足guassian 正态copula 的随机数

y1=normcdf(y,0,1);%求y的标准正态分布累积概率

wv=zeros(length(c),n);
for i=1:length(c)
wv(i,:)=wblinv(y1(:,i),c(i),k(i));%求y1的逆weibull累积分布，即竖的相同的那个值
end

v1(kkk,:)=wv(1,:);
v2(kkk,:)=wv(2,:);
v3(kkk,:)=wv(3,:);%这3步将正态分布和weibull相互关联，并且进行转换


end;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
tt=3;%选择研究的时段
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1);
plot(v1(tt,:),v2(tt,:),'+');
vin=4;
vr=12;
vout=20;
pr=2;
Nw=15;
wr=pr*Nw;
for i=1:24;
for j=1:20000;
if v1(i,j)<vin;
w1(i,j)=0;
elseif v1(i,j)>vout;
w1(i,j)=0;
elseif (v1(i,j)>vr)&(v1(i,j)<=vout);%大于vr并且小于vout的含义
w1(i,j)=wr;
else
w1(i,j)=(v1(i,j)-vin)*wr/(vr-vin);%w1不满足上面的条件之外的选择
end;
if v2(i,j)<vin;
w2(i,j)=0;
elseif v2(i,j)>vout;
w2(i,j)=0;
elseif (v2(i,j)>vr)&(v2(i,j)<=vout);
w2(i,j)=wr;
else
w2(i,j)=(v2(i,j)-vin)*wr/(vr-vin);
end;
if v3(i,j)<vin;
w3(i,j)=0;
elseif v3(i,j)>vout;
w3(i,j)=0;
elseif (v3(i,j)>vr)&(v3(i,j)<=vout);
w3(i,j)=wr;
else
w3(i,j)=(v3(i,j)-vin)*wr/(vr-vin);%w1.w2，w3这三个程序重复了3遍看一个就可以
end;
end;
end;
wsum=w1+w2+w3;%总的风电的功率；

[NB_Num ,Gen_Num, PD_NB_Num, Time_Num, Branch_Num, Cost_Subsection_Num, Reference_NB, Gen_UC, PD_System_UC, PD_NB_UC, Branch_UC]...
=load_case_UC;


load=PD_System_UC(tt,2);%负荷均值,同时为预测值
f=load+load*0.015*randn(1,n);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%r = μ + δ*randn(m,n)，μ为均值,δ为标准差(取负荷均值的1.5%），m,n为生成矩阵维数，为负荷实际值
w=wsum(tt,:);%总风电功率的tt数段的实际数值；
w_forest=mean(w);;%总风电功率的tt数段的预测数值；
delt_PD=f-load;
delt_W=w-w_forest;
a=delt_PD-delt_W;%tt时段 负荷-风电偏差的总和，综合随机变量




[b xa]=hist(a,10000);%分区间统计，这里分5000个区间，可根据你的数据适当调整
% num=numel(a);%num是数据的总个数
% c=cumsum(b/num);%计算累计分布
% plot(xa,c,'k-');%画图
% title ('旋转备用的综合分布概率');
%
% Mmean=0.9;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%0.99要进行改动
% B=abs(c-Mmean);
% [ca,indexx]=sort(B);
% c(indexx(1))
% Ac1=[xa(indexx(1))];%横坐标的值



F = ksdensity(a,xa,'function','cdf');%对数列进行统计
figure (2);
plot(xa,F);%输出图形
xlabel ('旋转备用');
ylabel ('概率分布值');
Ac1=interp1(F,xa,0.9);%0.9就是置信度，当F为0.9时横坐标的值为AC1，也就是所需要的上旋转备用，可以根据需要修改

[NB_Num ,Gen_Num, PD_NB_Num, Time_Num, Branch_Num, Cost_Subsection_Num, Reference_NB, Gen_UC, PD_System_UC, PD_NB_UC, Branch_UC]...
=load_case_UC;

ngen = Gen_Num;

% eq groups are separated by states rather than time periods
Aeq = [1 1 1];
beq = load-w_forest;

% Researve Cap. Constraints
Ares = [1 1 1];
brest = - Ac1 + sum(Gen_UC(:,5)) ;%3台火电机组的最大出力

% set of constraints
A = [ Ares];%矩阵，相当于例题中的A
B = [ brest];%最大值，旋转备用约束

vlb = Gen_UC(:,6);%第6列为最小出力
vub = Gen_UC(:,5);%第5列为最大出力

gencost_c = 2.*diag(Gen_UC(:,2)'); %将发电成本二次系数写成方阵，Gen_UC(:,2）是第2列的书
gencost_b = Gen_UC(:,3)'; %列矩阵，相当于例题中的f
gencost_a = sum(Gen_UC(:,4));%第4列的数值之和

[Pi,fval] = quadprog(gencost_c,gencost_b,A,B,Aeq,beq,vlb,vub,[],[]);%见例题，方括号里为不存在的数

disp('有功功率约束满足情况')
Aeq*Pi-beq

disp('旋转备用约束满足情况')
Ares*Pi - brest%Pi-（P(i,max)-Ri）≤0

disp('发电费用')
Pi'*gencost_c*Pi + gencost_b*Pi + gencost_a
toc;

代码中的figure2图形输出不了应该怎么改？？？

[赵4老师]

少hold on ？