Data-Driven Decision-Making for SCUC: An Improved Deep Learning Approach Based on Sample Coding and Seq2Seq Technique

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.