A deep reinforcement learning algorithm for the power order optimization allocation of AGC in interconnected power grids

Lei Xi; Lipeng Zhou; Lang Liu; Dongliang Duan; Yanchun Xu; Liuqing Yang; Shouxiang Wang

doi:10.17775/CSEEJPES.2019.01840

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A deep reinforcement learning algorithm for the power order optimization allocation of AGC in interconnected power grids

Regular Papers | 更新时间：2026-10-15

- A deep reinforcement learning algorithm for the power order optimization allocation of AGC in interconnected power grids
- CSEE Journal of Power and Energy Systems Vol. 6, Issue 3, Pages: 712-723(2020)
- 作者机构：
  
  1. College of Electrical Engineering and New Energy, China Three Gorges University,Yichang,China
  2. State Grid Xianning Electric Power Supply Company,Xianning,China
  3. Department of Electrical and Computer Engineering, University of Wyoming,WY,Laramie,USA
  4. Department of Electrical and Computer Engineering, Colorado State University,CO,Fort Collins,USA
  5. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University,Tianjin,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2019.01840
  CLC：
- Published：01 September 2020
- 稿件说明：
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Lei Xi, Lipeng Zhou, Lang Liu, et al. A deep reinforcement learning algorithm for the power order optimization allocation of AGC in interconnected power grids[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 712-723.
DOI：

Lei Xi, Lipeng Zhou, Lang Liu, et al. A deep reinforcement learning algorithm for the power order optimization allocation of AGC in interconnected power grids[J]. CSEE Journal of Power and Energy Systems, 2020, 6(3): 712-723. DOI： 10.17775/CSEEJPES.2019.01840.

摘要

Abstract

The integration of distributed generations (solar power

wind power)

energy storage devices

and electric vehicles

causes unpredictable disturbances in power grids. It has become a top priority to coordinate the distributed generations

loads

and energy storages in order to better facilitate the utilization of new energy. Therefore

a novel algorithm based on deep reinforcement learning

namely the deep PDWoLF-PHC (policy dynamics based win or learn fast-policy hill climbing) network (DPDPN)

is proposed to allocate power order among the various generators. The proposed algorithm combines the decision mechanism of reinforcement learning with the prediction mechanism of a deep neural network to obtain the optimal coordinated control for the source-grid-load. Consequently it solves the problem brought by stochastic disturbances and improves the utilization rate of new energy. Simulations are conducted with the case of the improved IEEE two-area and a case in the Guangdong power grid. Results show that the adaptability and control performance of the power system are improved using the proposed algorithm as compared with using other existing strategies.

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Related Author

Lei Xi

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Dongxia Zhang

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Related Institution

Yichang Key Laboratory of Defense and Control of Cyber-Physical Systems, China Three Gorges University

China Electric Power Research Institute

Nanyang Technological University

School of Electric Power, South China University of Technology

College of Electrical Engineering, Guangxi University

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