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A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles

Author

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  • 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)

  • Zhibo Zhang

    (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)

  • Chao Xi

    (State Grid Harbin Power Supply Company, Harbin 150001, China)

  • Boyu Liu

    (School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW 2052, Australia)

Abstract

As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

Suggested Citation

  • Bowen Zhou & Zhibo Zhang & Chao Xi & Boyu Liu, 2023. "A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles," Mathematics, MDPI, vol. 11(21), pages 1-33, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4563-:d:1275169
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    References listed on IDEAS

    as
    1. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    2. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    3. Shi, Mengshu & Huang, Yuansheng & Lin, Hongyu, 2023. "Research on power to hydrogen optimization and profit distribution of microgrid cluster considering shared hydrogen storage," Energy, Elsevier, vol. 264(C).
    4. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    Full references (including those not matched with items on IDEAS)

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