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Design of the ANFIS based optimized frequency control module for an electric vehicle charging station

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  • Kaur, Sachpreet
  • Kaur, Tarlochan
  • Khanna, Rintu

Abstract

Compared to conventional grids, microgrids are subjected to severe frequency fluctuations, especially in islanded mode. The alternative storage elements, if used, may lead to increased system cost. To address this problem, storage batteries of electric vehicles (EVs) is cost-effective solution. The major trade-offs being handling complexities and dynamics involved with them. In this study, ‘Frequency Control Module’ is designed to monitor EVs to use them in regulating frequency in microgrids. The various dynamics considered include their sudden arrival/departure, plug-in time, rated capacities, initial state-of-charges, and travel preferences. The control arrangement alters charging/discharging powers of EVs, keeping in concern initial and final decisions made by users and service providers, respectively. Also, it ensures that EVs plug-out after fulfilling their energy demands. Negligible correlation amongst dynamics encouraged usage of intelligent neuro-fuzzy system-based controller. For optimal performance of controller, Taguchi’s approach is used. The proposed methodology is verified through case studies on microgrid system. Results reveal minimal changes in plug-in time of vehicles. The frequency variations reduced considerably, and overall frequency response observed lesser oscillations. The proposed methodology can be beneficial for EVs that decide to plug in for a shorter time. Also, this energy-efficient scheme can effectively integrate with appropriate incentive-based programs.

Suggested Citation

  • Kaur, Sachpreet & Kaur, Tarlochan & Khanna, Rintu, 2022. "Design of the ANFIS based optimized frequency control module for an electric vehicle charging station," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012004
    DOI: 10.1016/j.apenergy.2022.119943
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    References listed on IDEAS

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    1. Li, Da & Deng, Junjun & Zhang, Zhaosheng & Liu, Peng & Wang, Zhenpo, 2023. "Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles," Applied Energy, Elsevier, vol. 343(C).

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