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Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations

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  • Mousavizade, Mirsaeed
  • Bai, Feifei
  • Garmabdari, Rasoul
  • Sanjari, Mohammad
  • Taghizadeh, Foad
  • Mahmoudian, Ali
  • Lu, Junwei

Abstract

Electric Vehicles (EVs) may significantly affect the stability of microgrids. To incorporate EVs with Vehicle-to-Grid (V2G) capability into the frequency control, existing approaches have primarily focused on the charge level of batteries to derive the participation level of individual EVs. However, examining only the State of Charge (SoC) does not provide an effective evaluation measure to distinguish between EVs with varying owner expectations. Furthermore, with the proliferation of renewable energies, EVs will be required to participate in voltage and frequency control consistently. In light of these, it is imperative to effectively consider the technical limitations of EVs when providing ancillary services in a microgrid. Thus, to fully realize the potential benefits of EVs in frequency regulation, the control scheme should consider both the microgrid-side and the EV-side requirements, including the rate at which the frequency changes, the operating conditions of EVs, and the satisfaction of the owners. This paper proposes a novel decentralized adaptive control scheme to regulate the contribution of EVs to primary frequency control in an islanded microgrid. The proposed framework adapts the droop parameter to address the concerns of both the microgrid and the EV. As the EV charger continuously monitors the frequency, it responds to any changes in the load-generation balance and adjusts its contribution accordingly. Moreover, to reflect the EV-side requirements and the expectations of owners in the control process, new indices are introduced to determine the charge and discharge capabilities of EVs in real-time, considering the operating boundaries associated with the V2G application. The efficacy of the proposed method has been demonstrated through simulations of various case studies in MATLAB/Simulink.

Suggested Citation

  • Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004828
    DOI: 10.1016/j.apenergy.2023.121118
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