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Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids

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  • Jiao, Feixiang
  • Zou, Yuan
  • Zhou, Yi
  • Zhang, Yanyu
  • Zhang, Xibeng

Abstract

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution.

Suggested Citation

  • Jiao, Feixiang & Zou, Yuan & Zhou, Yi & Zhang, Yanyu & Zhang, Xibeng, 2023. "Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018042
    DOI: 10.1016/j.energy.2023.128410
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    References listed on IDEAS

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    Cited by:

    1. Chen, Longxiang & He, Huan & Jing, Rui & Xie, Meina & Ye, Kai, 2024. "Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning," Energy, Elsevier, vol. 307(C).
    2. Wang, Yifeng & Jiang, Aihua & Wang, Rui & Tian, Junyang, 2024. "A canonical coalitional game model incorporating motivational psychology analysis for incentivizing stable direct energy trading in smart grid," Energy, Elsevier, vol. 289(C).

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