# Closed_Loop_NCUC_Dataset-Load_RES_Feature_System
The followings are the dataset and the codes for the paper entitled "Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework"
If they are helpful in your research, please cite our paper:
X. Chen, Y. Yang, Y. Liu and L. Wu, "Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework," in IEEE Transactions on Power Systems, vol. 37, no. 4, pp. 3104-3118, July 2022, doi: 10.1109/TPWRS.2021.3128485.
The data is collected from a Belgian ISO. This dataset is saved as .xlsx and includes:
* Day-ahead prediction and actual realization of Belgian load. (From 2018/01/01 to 2020/12/31) [Load.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset/files/7584372/Load.xlsx)
* Day-ahead prediction and actual realization of 13 solar power farms in Belgium. (From 2018/01/01 to 2020/12/31) [Solar_power_farm.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset/files/7584373/Solar_power_farm.xlsx)
* Day-ahead prediction and actual realization of 7 wind power farms in Belgium. (From 2018/01/01 to 2020/12/31) [Wind_power_farm.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset/files/7584374/Wind_power_farm.xlsx)
* Configurations of modified IEEE RTS 24-bus system. [System_IEEE_24_bus.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset-Load_RES_Feature_System/files/7314314/System_IEEE_24_bus.xlsx)
* Configurations of 5655-bus system. [System_ISO_5655_bus.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset_Load_RES_Feature_System/files/7314468/System_ISO_5655_bus.xlsx)
* Well-collected feature vectors. [Feature.xlsx](https://github.com/asxadf/Closed_Loop_NCUC_Dataset-Load_RES_Feature_System/files/7314316/Feature.xlsx)
Note that the load-RES data is collected every 15 minutes, so we add the subhour label to distinguish them.
To show our modification on IEEE RTS 24-bus system, we plot a map. Just check it out! [Map.pdf](https://github.com/asxadf/Closed_Loop_NCUC_Dataset-Load_RES_Feature_System/files/7314204/Map.pdf)
Recently, we presented the work. Just check it out! [OR_Presentation.pdf](https://github.com/asxadf/Closed_Loop_NCUC_Dataset/files/7739919/OR_Presentation.pdf)
We believe it is meaningful and helpful to enable the code open-access. So we upload the codes for the 24-bus case here. It requires MATLAB as the platform, GUROBI as the solver, and YALMIP to call GUROBI.
If you are interested in our data, case studies, and codes, please feel free to contact me at xchen130@stevens.edu. It's my pleasure to share them with you. �え
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基于数据驱动的模型预测控制电力系统机组组合优化(matlab)
共28个文件
mat:16个
m:11个
md:1个
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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。 参考文献:《Feature-Driven Economic Improvement for Network-Constrained Unit Commitment: A Closed-Loop Predict-and-Optimize Framework》
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15基于数据驱动的模型预测控制电力系统机组组合优化.zip (28个子文件)
15基于数据驱动的模型预测控制电力系统机组组合优化
Necessary_Tools
makePTDF.m 6KB
ws_distance.m 2KB
Dataset
CPO_Data_WPG_DAF.mat 2.33MB
CPO_Data_Cost_perfect_ACT.mat 12KB
CPO_Data_branch.mat 325B
CPO_Data_SPG_DAF.mat 2.62MB
CPO_Data_feature_OPO.mat 4.44MB
CPO_Data_WPG_RUM.mat 2.32MB
CPO_Data_load_country.mat 402KB
CPO_Data_Cost_perfect_UC.mat 12KB
CPO_Data_feature_CPO.mat 5.83MB
CPO_Data_SPG_RUM.mat 2.58MB
CPO_Data_case24_ieee_rts.m 8KB
CPO_Data_Gen_capacity.mat 507B
CPO_Data_Gen_price.mat 520B
CPO_Data_feature_PPO.mat 5.74MB
Main_CPO.m 12KB
NCUC_Model
UC_b_ineq.mat 12KB
UC_A_ineq.mat 265KB
UC_compact.m 6KB
UC_original.m 10KB
UC_c.mat 5KB
CPO_Main
Step_03_RT_ED.m 17KB
Step_01_CPO_train.m 7KB
Step_00_Select_train_day.m 9KB
Step_02_DA_UC.m 8KB
CPO_Database_Belgium_bus24.m 12KB
README.md 3KB
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