Intra‐day Dynamic Optimal Dispatch for Power System Based on Deep Q‐Learning. Issue 7 (17th June 2021)

• Record Type: Journal Article
• Authors: Tang, Hao; Wang, Shiping; Chang, Kejun; Guan, Jinyu
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 16:Issue 7(2021)
• Page Start: 954
• Page End: 964
• Publication Date: 2021-06-17
• Subjects: source‐load stochasticity -- optimal dispatch -- power system -- Markov decision process -- deep Q‐learning; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.23379
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract: Source‐load stochasticity affects the safety, stability and economical operation of power grid, and makes it difficult to design optimal dispatch. Currently, due to the low accuracy of day‐ahead forecast for wind power and load, dynamic optimal dispatch based on ultra‐short‐term wind power prediction is an effective way to reduce the power deviation in power system and to relieve the operating pressure on automatic generation control (AGC) unit. In this paper, by comprehensively considering the source‐load stochasticity, the dispatch properties of unit, and the constraints of AGC regulating range, we establish a dynamic optimal dispatch model to minimize the running cost of the underlying power system, including the coal consumption cost of thermal unit, and the output adjustment cost of both the dispatchable unit and the AGC unit. Since each dynamic dispatch order has an impact, direct or indirect, on the subsequent system operation, we formulate the dynamic optimal dispatch problem as a Markov decision process (MDP) problem. In this framework, a deep Q‐learning method is provided to optimize the dispatch strategy. Finally, the feasibility and the effectiveness of the proposed method is validated by means of numerical experiments on IEEE 30‐node system. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.