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An optimal energy management among the electric vehicle charging stations and electricity distribution system using GPC-RERNN approach

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  • Rajani, B.
  • Kommula, Bapayya Naidu

Abstract

This paper proposes a hybrid strategy to manage the energy in electric vehicle charging station (EVCS) and distribution system (DS). The proposed hybrid approach is the joint implementation of Giza Pyramids Construction (GPC) and recalling-enhanced recurrent neural network (RERNN) hence it is named as GPC-RERNN. The main purpose is to give maximum amount of energy as restrictions are irregular and volatile nature of renewable energy sources, stochastic nature of EV, and local meteorological conditions. Likewise, minimum system cost which incorporates land cost, station equipment, operating and preservation cost and also reduces the voltage deviation and power loss on distribution system. At first, RERNN is used to originate the quality-of-service constrained decision form for EVCSs. The utility of DS is increase when scheduling the charging plans of EVCSs. To analyze energy interaction, GPC approach is used. Here, every EVCS is considered as leader and DS is considered as follower. It is used to establish, an optimization issue by equilibrium restrictions. The proposed approach analysis the bidirectional trading of energy, effect of PV uncertainty under EMS operation, cost analysis based on selling energy. Finally the performance of the proposed approach is performed with the MATLAB/Simulink working platform and likened with several existing approaches.

Suggested Citation

  • Rajani, B. & Kommula, Bapayya Naidu, 2022. "An optimal energy management among the electric vehicle charging stations and electricity distribution system using GPC-RERNN approach," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222000834
    DOI: 10.1016/j.energy.2022.123180
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    References listed on IDEAS

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    2. Lai, Chun Sing & Chen, Dashen & Zhang, Jinning & Zhang, Xin & Xu, Xu & Taylor, Gareth A. & Lai, Loi Lei, 2022. "Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks," Energy, Elsevier, vol. 259(C).
    3. Tostado-Véliz, Marcos & Jordehi, Ahmad Rezaee & Mansouri, Seyed Amir & Jurado, Francisco, 2023. "A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots," Energy, Elsevier, vol. 263(PD).
    4. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    5. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).

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