Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence: An E-Seq2Seq-Based SCUC Expert System

Nan Yang, Cong Yang, Lei Wu, Xun Shen, Junjie Jia, Zhengmao Li, Daojun Chen, Binxin Zhu, Songkai Liu

Research output: Contribution to journalArticlepeer-review

102 Scopus citations

Abstract

Under the background of the rapid change of energy technology and the deep integration of artificial intelligence into the power system, it is of great significance to study the intelligent decision-making method of security-constrained unit commitment (SCUC) with high adaptability and high accuracy. Thus, in this article, an expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples is proposed. First, dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input-output sequence characteristics. Then, an E-Seq2Seq approach with a multiple-encoder-decoder architecture and a fully connected extension layer is proposed. On this basis, the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct deep learning models, and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system in Hunan province in China. The results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.

Original languageEnglish
Pages (from-to)3126-3137
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number5
DOIs
StatePublished - 1 May 2022

Keywords

  • Data-driven
  • deep learning
  • expanded sequence-to-sequence
  • security-constrained unit commitment
  • simple recurrent unit

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