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Reactive Power Optimization Method of Power Network Based on Deep Reinforcement Learning Considering Topology Characteristics

Author

Listed:
  • Tianhua Chen

    (State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks, Nanjing 211106, China
    Nari Technology Co., Ltd., Nanjing 211106, China)

  • Zemei Dai

    (State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks, Nanjing 211106, China
    Nari Technology Co., Ltd., Nanjing 211106, China)

  • Xin Shan

    (State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks, Nanjing 211106, China
    Nari Technology Co., Ltd., Nanjing 211106, China)

  • Zhenghong Li

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Chengming Hu

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Yang Xue

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Ke Xu

    (School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

Abstract

Aiming at the load fluctuation problem caused by a high proportion of new energy grid connections, a reactive power optimization method based on deep reinforcement learning (DRL) considering topological characteristics is proposed. The proposed method transforms the reactive power optimization problem into a Markov decision process and models and solves it through the deep reinforcement learning framework. The Dueling Double Deep Q-Network (D3QN) algorithm is adopted to improve the accuracy and efficiency of calculation. Aiming at the problem that deep reinforcement learning algorithms are difficult to simulate the topological characteristics of power flow, the Graph Convolutional Dueling Double Deep Q-Network (GCD3QN) algorithm is proposed. The graph convolutional neural network (GCN) is integrated into the D3QN model, and the information aggregation of topological nodes is realized through the graph convolution operator, which solves the calculation problem of deep learning algorithms in non-European space and improves the accuracy of reactive power optimization. The IEEE standard node system is used for simulation analysis, and the effectiveness of the proposed method is verified.

Suggested Citation

  • Tianhua Chen & Zemei Dai & Xin Shan & Zhenghong Li & Chengming Hu & Yang Xue & Ke Xu, 2024. "Reactive Power Optimization Method of Power Network Based on Deep Reinforcement Learning Considering Topology Characteristics," Energies, MDPI, vol. 17(24), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6454-:d:1549482
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    References listed on IDEAS

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    1. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
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