Power System Dispatch Based on Improved Scenario Division with Physical and Data-Driven Features

Wenqi Huang, Shang Cao (), Lingyu Liang, Huanming Zhang, Xiangyu Zhao, Hanju Li, Jie Ren and Liang Che
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Wenqi Huang: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Shang Cao: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Lingyu Liang: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Huanming Zhang: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Xiangyu Zhao: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Hanju Li: Southern Power Grid Digital Grid Research Institute, Guangzhou 510000, China
Jie Ren: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Liang Che: College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Energies, 2023, vol. 16, issue 22, 1-14

Abstract: In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system’s key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China.

Keywords: power system dispatch; reinforcement learning; scenario division; data-driven (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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