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Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach

Author

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  • Junwei Cao

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Wanlu Zhang

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Zeqing Xiao

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

  • Haochen Hua

    (Research Institute of Information Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China)

Abstract

The existence of high proportional distributed energy resources in energy Internet (EI) scenarios has a strong impact on the power supply-demand balance of the EI system. Decision-making optimization research that focuses on the transient voltage stability is of great significance for maintaining effective and safe operation of the EI. Within a typical EI scenario, this paper conducts a study of transient voltage stability analysis based on convolutional neural networks. Based on the judgment of transient voltage stability, a reactive power compensation decision optimization algorithm via deep reinforcement learning approach is proposed. In this sense, the following targets are achieved: the efficiency of decision-making is greatly improved, risks are identified in advance, and decisions are made in time. Simulations show the effectiveness of our proposed method.

Suggested Citation

  • Junwei Cao & Wanlu Zhang & Zeqing Xiao & Haochen Hua, 2019. "Reactive Power Optimization for Transient Voltage Stability in Energy Internet via Deep Reinforcement Learning Approach," Energies, MDPI, vol. 12(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1556-:d:225630
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    References listed on IDEAS

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    1. Haochen Hua & Chuantong Hao & Yuchao Qin & Junwei Cao, 2018. "A Class of Control Strategies for Energy Internet Considering System Robustness and Operation Cost Optimization," Energies, MDPI, vol. 11(6), pages 1-20, June.
    2. Haochen Hua & Yuchao Qin & Jianye Geng & Chuantong Hao & Junwei Cao, 2019. "Robust Mixed H 2 / H ∞ Controller Design for Energy Routers in Energy Internet," Energies, MDPI, vol. 12(3), pages 1-16, January.
    3. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    4. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    5. Yilun Shang, 2018. "Resilient Multiscale Coordination Control against Adversarial Nodes," Energies, MDPI, vol. 11(7), pages 1-17, July.
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    Citations

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    Cited by:

    1. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli & Marco Pasetti & Raul Igual, 2021. "Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey," Energies, MDPI, vol. 14(16), pages 1-24, August.
    2. Seok-Il Go & Sang-Yun Yun & Seon-Ju Ahn & Joon-Ho Choi, 2020. "Voltage and Reactive Power Optimization Using a Simplified Linear Equations at Distribution Networks with DG," Energies, MDPI, vol. 13(13), pages 1-23, June.
    3. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    4. Qingle Pang & Lin Ye & Houlei Gao & Xinian Li & Yang Zheng & Chenbin He, 2021. "Penalty Electricity Price-Based Optimal Control for Distribution Networks," Energies, MDPI, vol. 14(7), pages 1-16, March.
    5. Junyong Wu & Chen Shi & Meiyang Shao & Ran An & Xiaowen Zhu & Xing Huang & Rong Cai, 2019. "Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network," Energies, MDPI, vol. 12(17), pages 1-24, August.
    6. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. Linan Qu & Shujie Zhang & Hsiung-Cheng Lin & Ning Chen & Lingling Li, 2020. "Multiobjective Reactive Power Optimization of Renewable Energy Power Plants Based on Time-and-Space Grouping Method," Energies, MDPI, vol. 13(14), pages 1-15, July.

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