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Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network

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  • Zirui Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Bowen Zhou

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Chen Lv

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

  • Hongming Yang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China)

  • Quan Ma

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

  • Zhao Yang

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

  • Yong Cui

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

Abstract

With the continuous expansion of power grids and the gradual increase in operational uncertainty, it is progressively challenging to meet the capacity requirements for power grid development based on manual experience. In order to further improve the efficiency of the operation mode calculation, reduce the consumption of manpower and material resources, and consider the sustainability of energy development, this paper proposes a typical power grid operation mode generation method based on Q-learning and the deep belief network (DBN) for the first time. Firstly, the operation modes of different generator combinations located in different regions are obtained through Q-learning intelligent generation. Subsequently, the generated operation modes are clustered as different operation mode sets according to the data characteristics. Furthermore, comprehensive evaluation indexes are proposed from the perspectives of the steady state, transient state, and the economy. These multi-dimensional indexes are integrated via the analytical hierarchy process–entropy weight method (AHP-EWM) to enhance the comprehensibility of the evaluation system. Finally, DBN is introduced to construct a rapid operation mode evaluation model to realize the evaluation of operation mode sets, and typical operation mode sets are obtained accordingly. In this way, the system calculator only needs to compare the composite values to obtain the typical operation modes. The proposed method is validated by the Northeast Power Grid in China. The experimental results show that the proposed method can quickly generate typical power grid operation modes according to actual demand and greatly improve the efficiency of operation mode calculation.

Suggested Citation

  • Zirui Wang & Bowen Zhou & Chen Lv & Hongming Yang & Quan Ma & Zhao Yang & Yong Cui, 2023. "Typical Power Grid Operation Mode Generation Based on Reinforcement Learning and Deep Belief Network," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14844-:d:1259084
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    References listed on IDEAS

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    1. Sheeraz Iqbal & Salman Habib & Noor Habib Khan & Muhammad Ali & Muhammad Aurangzeb & Emad M. Ahmed, 2022. "Electric Vehicles Aggregation for Frequency Control of Microgrid under Various Operation Conditions Using an Optimal Coordinated Strategy," Sustainability, MDPI, vol. 14(5), pages 1-25, March.
    2. Bin Chen & Chengfeng Tao & Jie Tao & Yuyan Jiang & Ping Li, 2023. "Bearing Fault Diagnosis Using ACWGAN-GP Enhanced by Principal Component Analysis," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    3. Sheeraz Iqbal & Salman Habib & Muhammad Ali & Aqib Shafiq & Anis ur Rehman & Emad M. Ahmed & Tahir Khurshaid & Salah Kamel, 2022. "The Impact of V2G Charging/Discharging Strategy on the Microgrid Environment Considering Stochastic Methods," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
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