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Power System Operation Mode Calculation Based on Improved Deep Reinforcement Learning

Author

Listed:
  • Ziyang Yu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110004, China)

  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, Shenyang 110004, China)

  • Dongsheng Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110004, China)

  • Weirong Wu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110004, China)

  • Chen Lv

    (China Electric Power Research Institute, Beijing 100192, China)

  • Yong Cui

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 201507, China)

Abstract

Power system operation mode calculation (OMC) is the basis for unit commitment, scheduling arrangement, and stability analyses. In dispatch centers at all levels, OMC is usually realized by manually adjusting the parameters of power system components. In a new-type power system scenario, a large number of new energy sources lead to a significant increase in the complexity and uncertainty of a system structure, thus further increasing the workload and difficulty of manual adjustment. Therefore, improving efficiency and quality is of particular importance for power system OMC. This paper first considers generator power adjustment and line switching, and it then models the power flow adjustment process in OMC as a Markov decision process. Afterward, an improved deep Q-network (improved DQN) method is proposed for OMC. A state space, action space, and reward function that conform to the rules of the power system are designed. In addition, the action mapping strategy for generator power adjustment is improved to reduce the number of action adjustments and to speed up the network training process. Finally, 14 load levels under normal and N-1 fault conditions are designed. The experimental results on an IEEE-118 bus system show that the proposed method can effectively generate the operation mode under a given load level, and that it has good robustness.

Suggested Citation

  • Ziyang Yu & Bowen Zhou & Dongsheng Yang & Weirong Wu & Chen Lv & Yong Cui, 2023. "Power System Operation Mode Calculation Based on Improved Deep Reinforcement Learning," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:134-:d:1311086
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    References listed on IDEAS

    as
    1. Xi, Lei & Chen, Jianfeng & Huang, Yuehua & Xu, Yanchun & Liu, Lang & Zhou, Yimin & Li, Yudan, 2018. "Smart generation control based on multi-agent reinforcement learning with the idea of the time tunnel," Energy, Elsevier, vol. 153(C), pages 977-987.
    2. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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