| Click: 1868 Download: 1006 |
|
| Data-driven decision-making for SCUC: an improved deep learning approach based on sample coding and seq2seq technique |
| Nan Yang, Senior Member, IEEE,Juncong Hao,Zhengmao Li, Member, IEEE,Di Ye,Chao Xing,,Zhi Zhang,Can Wang, Member, IEEE,Yuehua Huang, Member, IEEE,Lei Zhang, Member, IEEE |
|
|
|
| Abstract: |
| The electricity industry has witnessed increasing challenges in power system operation and rapid developments of artificial intelligence technologies in the last decades. In this context, studying the approach of security-constrained unit commitment (SCUC) decision-making with high adaptability and precision is of great importance. This paper proposes an improved data-driven deep learning (DL) approach, following the sample coding and Sequence to Sequence (Seq2Seq) technique. First, an encoding and decoding strategy is utilized for high-dimensional sample matrix dimension compression. A DL SCUC decision model based on a Seq2Seq network with gated recurrent units as neurons is then constructed, and the mapping between load and unit on/off scheme is established through massive data from historical scheduling. Numerical simulation results based on the IEEE 118-bus test system demonstrate the correctness and effectiveness of the proposed approach. |
| Key words: Data-driven, gated recurrent unit, sample coding, Sequence to Sequence. |
| DOI：10.23919/PCMP.2023.000286 |
|
| Fund:This work is supported by the National Natural Science Foundation of China (No. 62233006). |
|