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Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization

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  • Cai, Sinan
  • Matsuhashi, Ryuji

Abstract

Maintaining frequency stability in a power system with a large scale of renewable energy resources (RES) requires extra frequency regulation resources due to the intermittent generation of RES. As one of the most promising solutions, electric vehicles (EVs) have faster response characteristics and can provide considerable regulation capacity when aggregated. When participating in load frequency control (LFC), the EV aggregator needs to dispatch the LFC signal into every single EV. However, since the aggregated EVs have different travel profiles and state-of-charge (SOC), it is challenging for the aggregator to decide the dispatching method so that both regulation requirements and users’ transport usage can be best satisfied. In this paper, an optimal dispatching control is designed for the EV aggregator. With the proposed control scheme, the EV aggregator can provide the regulation capacity to the system while ensuring that each individual EV will have enough SOC before the next trip. Compared with the optimal dispatching methods in existing literature, the proposed control operates at a faster time-step and allows EV aggregators to utilize EVs more efficiently, therefore more capacity payment from the market can be obtained by the EV aggregators. Simulation is performed in Matlab and Simulink to examine the performance and the effectiveness of the proposed dispatching controller.

Suggested Citation

  • Cai, Sinan & Matsuhashi, Ryuji, 2022. "Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization," Applied Energy, Elsevier, vol. 319(C).
    • Handle: RePEc:eee:appene:v:319:y:2022:i:c:s0306261922005955
    DOI: 10.1016/j.apenergy.2022.119233
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    Citations

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    Cited by:

    1. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(C).
    2. 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).
    3. Yang, Shaohua & Lao, Keng-Weng & Hui, Hongxun & Chen, Yulin, 2023. "A robustness-enhanced frequency regulation scheme for power system against multiple cyber and physical emergency events," Applied Energy, Elsevier, vol. 350(C).
    4. Jianhong Hao & Ting Huang & Qiuming Xu & Yi Sun, 2023. "Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game," Sustainability, MDPI, vol. 15(24), pages 1-15, December.
    5. Ma, Qianli & Wei, Wei & Mei, Shengwei, 2024. "Health-aware coordinate long-term and short-term operation for BESS in energy and frequency regulation markets," Applied Energy, Elsevier, vol. 356(C).
    6. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    7. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).

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