目录
1 主要内容
2 部分代码
3 程序结果
4 下载链接
1 主要内容
该程序复现文章《Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework》，程序主要做的是一个基于数据驱动的电力系统机组组合调度模型，采用IEEE24节点系统作为研究对象，该模型的创新点在于：提出了一个闭环预测与优化(C-PO)框架，即利用NCUC模型的结构以及相关特征数据来训练一个以成本为导向的RES预测模型，该模型通过诱导的NCUC成本而不是统计预测误差来评估预测质量，并且在优化过程中采用拉格朗日松弛来加速训练过程，该模型理论深度较大，代码学习难度也较大，本次免费分享供相关方向同学参考。
原文模型及部分结果：
2 部分代码
Number_day = Validate_day_end - Validate_day_1st + 1;
First_day_intuition = Validate_day_1st;
Final_day_intuition = Validate_day_end;
Scaler_load = 0.22;
Scaler_SPG = 0.39;
Scaler_WPG = 0.39;
R_for_load = 0.10;
R_for_RES = 0.05;
Method_flag = 'CPO';
Number_hour = 24;
Number_RES = 5;
%% -----------------------------SPO tunning----------------------------- %%
lamda = 100000;
Number_training_day = 2;
Number_day_H_validity = 7; % The frequency of updating Predictor H.
Number_historic_day = 7;
Solver_flag = 'g';
Solver_gap = 3;
Solver_time = 10;
%% -----------------------Prepare box for recorder---------------------- %%
% Rec for UC
Rec_Decision_UC_I = cell(Number_day, 1);
Rec_Decision_UC_P = cell(Number_day, 1);
Rec_Decision_UC_R_h = cell(Number_day, 1);
Rec_Decision_UC_R_c = cell(Number_day, 1);
Rec_cost_UC_expected = cell(Number_day, 1);
Rec_cost_UC_SUSD = cell(Number_day, 1);
Rec_RES_prediction = cell(Number_day, 1);
Rec_infea_UC_flag = cell(Number_day, 1);
Rec_UC_time = cell(Number_day, 1);
% Rec for ED
Rec_cost_ACT = cell(Number_day, 1);
Rec_cost_UC = cell(Number_day, 1);
Rec_cost_SUSD_all = cell(Number_day, 1);
Rec_cost_SUSD_UC = cell(Number_day, 1);
Rec_cost_SUSD_ED = cell(Number_day, 1);
Rec_cost_P = cell(Number_day, 1);
Rec_cost_LS = cell(Number_day, 1);
Rec_cost_loss_ACT = cell(Number_day, 1);
Rec_cost_loss_UC = cell(Number_day, 1);
Rec_infea_ED_flag = cell(Number_day, 1);
%% --------------------------Prepare box for CPO------------------------ %%
% Cost
CPO_cost_ACT = zeros(Number_day, 1);
CPO_cost_UC = zeros(Number_day, 1);
CPO_cost_SUSD_all = zeros(Number_day, 1);
CPO_cost_SUSD_UC = zeros(Number_day, 1);
CPO_cost_SUSD_ED = zeros(Number_day, 1);
CPO_cost_P = zeros(Number_day, 1);
CPO_cost_LS = zeros(Number_day, 1);
CPO_cost_loss_ACT = zeros(Number_day, 1);
CPO_cost_loss_UC = zeros(Number_day, 1);
% Flag
CPO_infeasible_UC = zeros(Number_day, 1);
CPO_infeasible_ED = zeros(Number_day, 1);
%% -------------------------Set updating frequency---------------------- %%
Number_period = ceil(Number_day/Number_day_H_validity);
if Number_period == floor(Number_day/Number_day_H_validity)
Number_day_in_period_full = Number_day_H_validity;
Number_day_in_period_last = Number_day_H_validity;
Period_size_list = ones(Number_period,1);
Period_1st_list = zeros(Number_period,1);
Period_end_list = zeros(Number_period,1);
Period_size_list(1:Number_period-1) = Number_day_in_period_full;
Period_size_list(Number_period) = Number_day_in_period_last;
end
if Number_period > floor(Number_day/Number_day_H_validity)
Number_day_in_period_full = Number_day_H_validity;
Number_day_in_period_last = Number_day - (Number_period - 1)*Number_day_H_validity;
Period_size_list = ones(Number_period,1);
Period_1st_list = zeros(Number_period,1);
Period_end_list = zeros(Number_period,1);
Period_size_list(1:Number_period-1) = Number_day_in_period_full;
Period_size_list(Number_period) = Number_day_in_period_last;
end
for i_period = 1:Number_period
Period_1st_list(i_period) = (Validate_day_end+1) - sum(Period_size_list(i_period:end));
Period_end_list(i_period) = (Validate_day_1st-1) + sum(Period_size_list(1:i_period));
end
%% ------------------Prepare box for training details------------------- %%
% Training detail
CPO_TRA_Predictor_H = cell(Number_period, 1);
CPO_TRA_Predictor_H_ele = cell(Number_period, 1);
CPO_TRA_obj = zeros(Number_period, 1);
CPO_TRA_cost_ERM = zeros(Number_period, 1);
CPO_TRA_regulation = zeros(Number_period, 1);
CPO_TRA_time = zeros(Number_period, 1);
%% --------------------------Prepare box for pick----------------------- %%
Picked_TRA_intuition = zeros(Number_training_day,Number_period);
Picked_TRA_feature = cell(Number_period,1);
Picked_TRA_load_city = cell(Number_period,1);
Picked_TRA_reserve_load_req = cell(Number_period,1);
Picked_TRA_reserve_RES_req = cell(Number_period,1);
Picked_TRA_cost_perfect = cell(Number_period,1);
%% ------------------------------Let's go------------------------------- %%
for Current_period = 1:Number_period
Number_dispatch_day = Period_size_list(Current_period);
Dispatch_day_1st = Period_1st_list(Current_period);
Dispatch_day_end = Period_end_list(Current_period);
%% -----------------------Select training day----------------------- %%
[Picked_TRA_intuition(:,Current_period),...
Picked_TRA_feature{Current_period},...
Picked_TRA_load_city{Current_period},...
Picked_TRA_reserve_load_req{Current_period},...
Picked_TRA_reserve_RES_req{Current_period},...
Picked_TRA_cost_perfect{Current_period}]...
= Step_00_Select_train_day(Dispatch_day_1st,...
Dispatch_day_end,...
Number_training_day,...
Number_dispatch_day,...
Scaler_load,...
Scaler_SPG,...
Scaler_WPG,...
R_for_load,...
R_for_RES,...
Number_historic_day);
%% -----------------------------Setp 01----------------------------- %%
[CPO_TRA_Predictor_H{Current_period},...
CPO_TRA_Predictor_H_ele{Current_period},...
CPO_TRA_obj(Current_period),...
CPO_TRA_cost_ERM(Current_period),...
CPO_TRA_regulation(Current_period),...
CPO_TRA_time(Current_period)]...
= Step_01_CPO_train(lamda,...
Scaler_load,...
Scaler_SPG,...
Scaler_WPG,...
Solver_flag, Solver_gap, Solver_time,...
Picked_TRA_feature{Current_period},...
Picked_TRA_load_city{Current_period},...
Picked_TRA_reserve_load_req{Current_period},...
Picked_TRA_reserve_RES_req{Current_period},...
Picked_TRA_cost_perfect{Current_period},...
Number_training_day,...
Method_flag);
3 程序结果
