Open AccessDeep learning‐based SCUC decision‐making: An intelligent data‐driven approach with self‐learning capabilities
Author(s) -
Yang Nan,
Yang Cong,
Xing Chao,
Ye Di,
Jia Junjie,
Chen Daojun,
Shen Xun,
Huang Yuehua,
Zhang Lei,
Zhu Binxin
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12315
Subject(s) - adaptability , computer science , artificial intelligence , process (computing) , machine learning , artificial neural network , power system simulation , power (physics) , electric power system , ecology , physics , quantum mechanics , biology , operating system
Abstract This paper proposes an intelligent Deep Learning (DL) based approach for Data‐Driven Security‐Constrained Unit Commitment (DD‐SCUC) decision‐making. The proposed approach includes data pre‐processing and a two‐stage decision‐making process. Firstly, historical data is accumulated and pre‐processed. Then, the DD‐SCUC model is created based on the Gated Recurrent Unit‐Neural Network (GRU‐NN). The mapping model between system daily load and decision results is created by training the DL model with historical data and then is utilized to make SCUC decisions. The two‐stage decision‐making process outputs the decision results based on various applications and scenarios. This approach has self‐learning capabilities because the accumulation of historical data sets can revise the mapping model and therefore improve its accuracy. Simulation results from the IEEE 118‐bus test system and a real power system from China showed that compared with deterministic Physical‐Model‐Driven (PMD)‐SCUC methods, the approach has higher accuracy, better efficiency in the practical use case, and better adaptability to different types of SCUC problems.