2.2 IEEE6和IEEE118
x=[1,2,3,4]; b=BarPlotBreak(x,y_time_record',15,20,'Line',0.99); x_label={'CC (Gaussian)','DRO (Binomial)','Clairvoyant','SO (Scenario)'}; set(gca,'XTickLabel', x_label) legend('6 bus','118 bus') ylabel('time (s)','FontSize',13.2,'FontName','Times New Roman','FontWeight','Bold') set(gca,'FontSize',12,'FontName','Times New Roman') b(1).FaceColor=[0.9290, 0.6940, 0.1250]; b(2).FaceColor=[0.4660, 0.6740, 0.1880];
2.3 IEEE6
2.4 IEEE118
部分代码：
function[fval_avg,x_avg]=gen_SSO_average_performance(c_level,epsilon,T,N,M,bb,d_f,p,q,H,fmax,gmin,gmax,w_loc,w_num,error_data,ramp_rate,DR,UR) % scenarios to be generated to meet the probability guarantee n_dv=4*N*T; Num=ceil(n_dv/(epsilon*c_level))-1; % generating wind scenario index=ceil(T*Num*(1-epsilon)); d_real=d_f; virtual_bb=bb; [gm] = gen_samples(error_data); for k=1:w_num loc=w_loc(k); % random_num=zeros(T*Num,1); % for i=1:Num % rng('default'); % [random_T,~]=random(gm,T); % random_num((i-1)*T+1:i*T)=random_T; % end rng('default'); random_num=random(gm,T*Num); random_num=sort(random_num); virtual_bb(loc)=max([bb(loc)-random_num(index),0]); end [standard_delta,real_bb]= gen_standard_delta(bb,virtual_bb,w_loc); umin=-real_bb*ramp_rate; umax=real_bb*ramp_rate; [x_avg,fval_avg]=MinC(T,N,M,real_bb,d_real,p,q,H,fmax,gmin,gmax,umin,umax,DR,UR); end function[wind_error]= gen_wind_data(real_capacity_scale,filename) %filename='.\data\WindGenTotalLoadYTD_2020.xls'; input_data= xlsread(filename, 1, 'B25:C52428'); input_scale=mean(input_data(:,1)); wind_data=input_data./input_scale*real_capacity_scale; wind_error=wind_data(:,2)-wind_data(:,1); % error_norm=normalize(error_data,'scale'); % wind_error=error_norm*ratio; end function[wind_error]= gen_wind_data(real_capacity_scale,filename) %filename='.\data\WindGenTotalLoadYTD_2020.xls'; input_data= xlsread(filename, 1, 'B25:C52428'); input_scale=mean(input_data(:,1)); wind_data=input_data./input_scale*real_capacity_scale; wind_error=wind_data(:,2)-wind_data(:,1); % error_norm=normalize(error_data,'scale'); % wind_error=error_norm*ratio; end
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1]耿晓路,童小娇.分布鲁棒机会约束优化问题的研究[J].运筹学学报,2020,24(01):115-130.DOI:10.15960/j.cnki.issn.1007-6093.2020.01.009.
[2]随权,林湘宁,童宁,李雪松,汪致洵,胡志豪,李正天,孙士茼.基于改进两阶段鲁棒优化的主动配电网经济调度[J].中国电机工程学报,2020,40(07):2166-2179+2396.DOI:10.13334/j.0258-8013.pcsee.182259.
[3]程凤璐. 在线经济调度的鲁棒优化方法研究[D].山东大学,2015.
[4]王晨曦. 含大规模风电的电力系统鲁棒优化调度研究[D].华南理工大学,2019.DOI:10.27151/d.cnki.ghnlu.2019.002201.
[5]许书伟,吴文传,朱涛,王珍意.机会约束随机动态经济调度的凸松弛迭代求解法[J].电力系统自动化,2020,44(17):43-51.
