26号资源-源程序：论文可在知网下载《两级电力市场环境下计及风险的省间交易商最优购电模型》本人博客有解读

%% 两级电力市场环境下计及风险的省间交易商最优购电模型 %电网技术论文复现 %双层规划,节点出清价,绿证交易,CVaR方法 clc clear close all %% 参数设定 P_G=sdpvar(5,24); %省内发电机组的出清电能,分别为火电、水电、光伏发电、风电、气电 P_IP_D=sdpvar(1,24); %省间交易商在省间市场的购电量 P_R=sdpvar(5,24); %省内发电机组的备用容量 P_IP_G_A=sdpvar(5,24); %送端省A的各个发电机组的出清电能 P_IP_G_B=sdpvar(5,24); %送端省B的各个发电机组的出清电能 P_IP_G_C=sdpvar(5,24); %送端省C的各个发电机组的出清电能 lambda=sdpvar(1,24); %下层等式约束的对偶变量 miu_1_max=sdpvar(3,24); %式(11)的上限对偶变量 miu_1_min=sdpvar(3,24); %式(11)的下限对偶变量 miu_2_A_max=sdpvar(5,24); %式(12)中的送端省A中发电机组的上限对偶变量 miu_2_A_min=sdpvar(5,24); %式(12)中的送端省A中发电机组的下限对偶变量 miu_2_B_max=sdpvar(5,24); %式(12)中的送端省B中发电机组的上限对偶变量 miu_2_B_min=sdpvar(5,24); %式(12)中的送端省B中发电机组的下限对偶变量 miu_2_C_max=sdpvar(5,24); %式(12)中的送端省C中发电机组的上限对偶变量 miu_2_C_min=sdpvar(5,24); %式(12)中的送端省C中发电机组的下限对偶变量 v1=binvar(3,24); %辅助布尔变量 v2=binvar(3,24); v3_A=binvar(5,24); v4_A=binvar(5,24); v3_B=binvar(5,24); v4_B=binvar(5,24); v3_C=binvar(5,24); v4_C=binvar(5,24); xi=sdpvar(1,1); %省内市场运行成本的VaR值 chi=sdpvar(1,1); %非负辅助变量 M=1e6; %% 系统中常数参数导入 %省内t时段的负荷需求,单位为MW P_D=[53783,53783,50551,48174,47319,47921,53641,59486,69087,76406,79543,78307,77167,76406,74695,75551,79543,80399,80399,80399,77167,66711,58821,58821]; P_wp=[2706,2838,2951,2800,2744,2725,2932,2951,3195,3251,3157,3458,3665,4003,3928,3402,2462,1503,1390,996,845,770,770,1860]; P_pv=[150,150,150,263,375,827,1127,1992,2593,4022,5601,6466,6334,5394,7706,6466,5413,5150,5075,1860,507,281,225,300]; %% 导入约束条件 C=[]; %上层优化模型的约束条件 C=[C, sum(P_G(1:5,:))+P_IP_D-P_D==0, %电力平衡约束 sum(P_R(1:5,:))-0.05*P_D>=0, %备用容量约束 -0.1*46350<=P_G(1,2:24)-P_G(1,1:23)<=0.1*46350, %火电机组的爬坡约束 -0.5*12500<=P_G(2,2:24)-P_G(2,1:23)<=0.5*12500, %水电机组的爬坡约束 -8000<=P_G(3,2:24)-P_G(3,1:23)<=8000, %光伏发电的爬坡约束 -4000<=P_G(4,2:24)-P_G(4,1:23)<=4000, %风电的爬坡约束 -0.3*7040<=P_G(5,2:24)-P_G(5,1:23)<=0.3*7040, %气电的爬坡约束 0.5*46350<=P_G(1,:)+P_R(1,:)<=46350, %火电机组的出力约束 0.1*12500<=P_G(2,:)+P_R(3,:)<=12500, %水电机组的出力约束 0<=P_G(3,:)+P_R(3,:)<=8000, %光伏发电的出力约束 0<=P_G(4,:)+P_R(4,:)<=4000, %风电的出力约束 0<=P_G(3,:)+P_R(3,:)<=P_pv, %光伏发电的出力约束 0<=P_G(4,:)+P_R(4,:)<=P_wp, %风电的出力约束 0.3*7040<=P_G(5,:)+P_R(5,:)<=7040, %气电机组的出力约束 ]; C=[C,P_G(1:5,:)>=0,P_R(1:5,:)>=0,P_IP_D>=0,]; %所有上层决策变量非负性约束 %下层问题的KKT条件 %原下层问题的等号约束条件 C=[C,(1-0.07)*sum(P_IP_G_A(1:5,:))+(1-0.065)*sum(P_IP_G_B(1:5,:))+(1-0.015)*sum(P_IP_G_C(1:5,:))==P_IP_D,]; %省间联络线电力平衡约束 %对偶问题的约束条件 %拉格朗日平稳性约束 C=[C, 420+53.5-lambda*(1-0.07)+miu_1_max(1,:)-miu_1_min(1,:)+miu_2_A_max(1,:)-miu_2_A_min(1,:)==0, 190+53.5-lambda*(1-0.07)+miu_1_max(1,:)-miu_1_min(1,:)+miu_2_A_max(2,:)-miu_2_A_min(2,:)==0, 280+53.5-lambda*(1-0.07)+miu_1_max(1,:)-miu_1_min(1,:)+miu_2_A_max(3,:)-miu_2_A_min(3,:)==0, 210+53.5-lambda*(1-0.07)+miu_1_max(1,:)-miu_1_min(1,:)+miu_2_A_max(4,:)-miu_2_A_min(4,:)==0, 730+53.5-lambda*(1-0.07)+miu_1_max(1,:)-miu_1_min(1,:)+miu_2_A_max(5,:)-miu_2_A_min(5,:)==0, 420+49.5-lambda*(1-0.065)+miu_1_max(2,:)-miu_1_min(2,:)+miu_2_B_max(1,:)-miu_2_B_min(1,:)==0, 190+49.5-lambda*(1-0.065)+miu_1_max(2,:)-miu_1_min(2,:)+miu_2_B_max(2,:)-miu_2_B_min(2,:)==0, 280+49.5-lambda*(1-0.065)+miu_1_max(2,:)-miu_1_min(2,:)+miu_2_B_max(3,:)-miu_2_B_min(3,:)==0, 210+49.5-lambda*(1-0.065)+miu_1_max(2,:)-miu_1_min(2,:)+miu_2_B_max(4,:)-miu_2_B_min(4,:)==0, 730+49.5-lambda*(1-0.065)+miu_1_max(2,:)-miu_1_min(2,:)+miu_2_B_max(5,:)-miu_2_B_min(5,:)==0, 420+10.0-lambda*(1-0.015)+miu_1_max(3,:)-miu_1_min(3,:)+miu_2_C_max(1,:)-miu_2_C_min(1,:)==0, 190+10.0-lambda*(1-0.015)+miu_1_max(3,:)-miu_1_min(3,:)+miu_2_C_max(2,:)-miu_2_C_min(2,:)==0, 280+10.0-lambda*(1-0.015)+miu_1_max(3,:)-miu_1_min(3,:)+miu_2_C_max(3,:)-miu_2_C_min(3,:)==0, 210+10.0-lambda*(1-0.015)+miu_1_max(3,:)-miu_1_min(3,:)+miu_2_C_max(4,:)-miu_2_C_min(4,:)==0, 730+10.0-lambda*(1-0.015)+miu_1_max(3,:)-miu_1_min(3,:)+miu_2_C_max(5,:)-miu_2_C_min(5,:)==0, ]; %互补松弛条件 C=[C, %送端省A的部分 0<=miu_1_min(1,:)<=M*(1-v1(1,:)), 0<=sum(P_IP_G_A(1:5,:))-0<=M*v1(1,:), 0<=8000-sum(P_IP_G_A(1:5,:))<=M*v2(1,:), 0<=miu_1_max(1,:)<=M*(1-v2(1,:)), 0<=P_IP_G_A(1,:)-0<=M*v3_A(1,:), 0<=miu_2_A_min(1,:)<=M*(1-v3_A(1,:)), 0<=5060-P_IP_G_A(1,:)<=M*v4_A(1,:), 0<=miu_2_A_max(1,:)<=M*(1-v4_A(1,:)), 0<=P_IP_G_A(2,:)-0<=M*v3_A(2,:), 0<=miu_2_A_min(2,:)<=M*(1-v3_A(2,:)), 0<=1900-P_IP_G_A(2,:)<=M*v4_A(2,:), 0<=miu_2_A_max(2,:)<=M*(1-v4_A(2,:)), 0<=P_IP_G_A(3,:)-0<=M*v3_A(3,:), 0<=miu_2_A_min(3,:)<=M*(1-v3_A(3,:)), 0<=1980-P_IP_G_A(3,:)<=M*v4_A(3,:), 0<=miu_2_A_max(3,:)<=M*(1-v4_A(3,:)), 0<=P_IP_G_A(4,:)-0<=M*v3_A(4,:), 0<=miu_2_A_min(4,:)<=M*(1-v3_A(4,:)), 0<=2800-P_IP_G_A(4,:)<=M*v4_A(4,:), 0<=miu_2_A_max(4,:)<=M*(1-v4_A(4,:)), 0<=P_IP_G_A(5,:)-0<=M*v3_A(5,:), 0<=miu_2_A_min(5,:)<=M*(1-v3_A(5,:)), 0<=1200-P_IP_G_A(5,:)<=M*v4_A(5,:), 0<=miu_2_A_max(5,:)<=M*(1-v4_A(5,:)), %送端省B的部分 0<=miu_1_min(2,:)<=M*(1-v1(2,:)), 0<=sum(P_IP_G_B(1:5,:))-0<=M*v1(2,:), 0<=8000-sum(P_IP_G_B(1:5,:))<=M*v2(2,:), 0<=miu_1_max(2,:)<=M*(1-v2(2,:)), 0<=P_IP_G_B(1,:)-0<=M*v3_B(1,:), 0<=miu_2_B_min(1,:)<=M*(1-v3_B(1,:)), 0<=1120-P_IP_G_B(1,:)<=M*v4_B(1,:), 0<=miu_2_B_max(1,:)<=M*(1-v4_B(1,:)), 0<=P_IP_G_B(2,:)-0<=M*v3_B(2,:), 0<=miu_2_B_min(2,:)<=M*(1-v3_B(2,:)), 0<=6640-P_IP_G_B(2,:)<=M*v4_B(2,:), 0<=miu_2_B_max(2,:)<=M*(1-v4_B(2,:)), 0<=P_IP_G_B(3,:)-0<=M*v3_B(3,:), 0<=miu_2_B_min(3,:)<=M*(1-v3_B(3,:)), 0<=200-P_IP_G_B(3,:)<=M*v4_B(3,:), 0<=miu_2_B_max(3,:)<=M*(1-v4_B(3,:)), 0<=P_IP_G_B(4,:)-0<=M*v3_B(4,:), 0<=miu_2_B_min(4,:)<=M*(1-v3_B(4,:)), 0<=480-P_IP_G_B(4,:)<=M*v4_B(4,:), 0<=miu_2_B_max(4,:)<=M*(1-v4_B(4,:)), 0<=P_IP_G_B(5,:)-0<=M*v3_B(5,:), 0<=miu_2_B_min(5,:)<=M*(1-v3_B(5,:)), 0<=168-P_IP_G_B(5,:)<=M*v4_B(5,:), 0<=miu_2_B_max(5,:)<=M*(1-v4_B(5,:)), %送端省C的部分 0<=miu_1_min(3,:)<=M*(1-v1(3,:)), 0<=sum(P_IP_G_C(1:5,:))-0<=M*v1(3,:), 0<=18000-sum(P_IP_G_C(1:5,:))<=M*v2(3,:), 0<=miu_1_max(3,:)<=M*(1-v2(3,:)), 0<=P_IP_G_C(1,:)-0<=M*v3_C(1,:), 0<=miu_2_C_min(1,:)<=M*(1-v3_C(1,:)), 0<=7536-P_IP_G_C(1,:)<=M*v4_C(1,:), 0<=miu_2_C_max(1,:)<=M*(1-v4_C(1,:)), 0<=P_IP_G_C(2,:)-0<=M*v3_C(2,:), 0<=miu_2_C_min(2,:)<=M*(1-v3_C(2,:)), 0<=1360-P_IP_G_C(2,:)<=M*v4_C(2,:), 0<=miu_2_C_max(2,:)<=M*(1-v4_C(2,:)), 0<=P_IP_G_C(3,:)-0<=M*v3_C(3,:), 0<=miu_2_C_min(3,:)<=M*(1-v3_C(3,:)), 0<=1200-P_IP_G_C(3,:)<=M*v4_C(3,:), 0<=miu_2_C_max(3,:)<=M*(1-v4_C(3,:)), 0<=P_IP_G_C(4,:)-0<=M*v3_C(4,:), 0<=miu_2_C_min(4,:)<=M*(1-v3_C(4,:)), 0<=1500-P_IP_G_C(4,:)<=M*v4_C(4,:), 0<=miu_2_C_max(4,:)<=M*(1-v4_C(4,:)), 0<=P_IP_G_C(5,:)-0<=M*v3_C(5,:), 0<=miu_2_C_min(5,:)<=M*(1-v3_C(5,:)), 0<=772-P_IP_G_C(5,:)<=M*v4_C(5,:), 0<=miu_2_C_max(5,:)<=M*(1-v4_C(5,:)), ]; %% 导入目标函数 %通过强对偶定理消去双线性项 Oc=420*sum(P_G(1,:))+190*sum(P_G(2,:))+280*sum(P_G(3,:))+210*sum(P_G(4,:))+730*sum(P_G(5,:))+... 0.05*420*sum(P_R(1,:))+0.05*190*sum(P_R(2,:))+0.05*280*sum(P_R(3,:))+0.05*210*sum(P_R(4,:))+0.05*730*sum(P_R(5,:))+... 0+8000*sum(miu_1_max(1,:))+8000*sum(miu_1_max(2,:))+18000*sum(miu_1_max(3,:))+... sum(miu_2_A_max(1,:)*5060)+sum(miu_2_A_max(2,:)*1900)+sum(miu_2_A_max(3,:)*1980)+sum(miu_2_A_max(4,:)*2800)+sum(miu_2_A_max(5,:)*1200)+... sum(miu_2_B_max(1,:)*1120)+sum(miu_2_B_max(2,:)*6640)+sum(miu_2_B_max(3,:)*200)+sum(miu_2_B_max(