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Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective

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  • Zheng, Yanchong
  • Wang, Yubin
  • Yang, Qiang

Abstract

The increasing penetration of electric vehicles (EVs) poses challenges to the operation of existing power systems owing to the spatial and temporal randomness and dynamics of EV charging. Although substantial theoretical research efforts have been made for EV coordinated charging management, the implementation and validation in the context of a practical market mechanism in the presence of EV aggregators require further exploitation. This study develops a two-phase coordinated charging scheduling solution in the energy market environment to optimally schedule EV charging loads for profit maximization from the perspective of EV aggregators. In the first phase, EV aggregators bid for energy in the day-ahead markets, considering a wide variety of uncertainties from EV charging and electricity markets. In the second phase, EV aggregators manage the charging loads of EVs in real time using purchased energy. The proposed solution was implemented and assessed using the Guangdong energy market as a case study through extensive simulation experiments. The numerical results confirm that the proposed method can enable the actual consumed energy from EVs to match the day-ahead bidding energy well. In this regard, the charging demand of EVs can be met with proper planning, to fulfill the coordinated charging operation of EVs in power grids.

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  • Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators’ perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:rensus:v:171:y:2023:i:c:s1364032122008875
    DOI: 10.1016/j.rser.2022.113006
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    Cited by:

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    2. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    3. Juan Pablo Ochoa Avilés & Fernando Lessa Tofoli & Enio Roberto Ribeiro, 2023. "Novel Control Approach for Resonant Class-DE Inverters Applied in Wireless Power Transfer Systems," Energies, MDPI, vol. 16(21), pages 1-18, October.
    4. Chang, Weiguang & Yang, Qiang, 2023. "Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading," Applied Energy, Elsevier, vol. 351(C).
    5. Lei, Xiang & Yu, Hang & Shao, Ziyun & Jian, Linni, 2023. "Optimal bidding and coordinating strategy for maximal marginal revenue due to V2G operation: Distribution system operator as a key player in China's uncertain electricity markets," Energy, Elsevier, vol. 283(C).
    6. Zheng, Yanchong & Wang, Yubin & Yang, Qiang, 2023. "Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach," Energy, Elsevier, vol. 283(C).
    7. Wang, Yubin & Zheng, Yanchong & Yang, Qiang, 2023. "Optimal energy management of integrated energy systems for strategic participation in competitive electricity markets," Energy, Elsevier, vol. 278(PA).

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