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Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties

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  • Zhang, Meijuan
  • Yan, Qingyou
  • Guan, Yajuan
  • Ni, Da
  • Agundis Tinajero, Gibran David

Abstract

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.

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  • Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    • Handle: RePEc:eee:energy:v:298:y:2024:i:c:s0360544224011435
    DOI: 10.1016/j.energy.2024.131370
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    3. Ge, Haotian & Zhu, Yu & Zhong, Jiuming & Wu, Liang, 2024. "Day-ahead optimization for smart energy management of multi-microgrid using a stochastic-robust model," Energy, Elsevier, vol. 313(C).
    4. Wang, Can & Liu, Yuzheng & Zhang, Yu & Xi, Lei & Yang, Nan & Zhao, Zhuoli & Lai, Chun Sing & Lai, Loi Lei, 2025. "Strategy for optimizing the bidirectional time-of-use electricity price in multi-microgrids coupled with multilevel games," Energy, Elsevier, vol. 323(C).
    5. Minh Phuc Duong & My-Ha Le & Thang Trung Nguyen & Minh Quan Duong & Anh Tuan Doan, 2025. "Economic and Technical Aspects of Power Grids with Electric Vehicle Charge Stations, Sustainable Energies, and Compensators," Sustainability, MDPI, vol. 17(1), pages 1-32, January.
    6. Guo, Shiliang & He, Jianqi & Ma, Kai & Yang, Jie & Wang, Yaochen & Li, Pengpeng, 2025. "Robust economic dispatch for industrial microgrids with electric vehicle demand response," Renewable Energy, Elsevier, vol. 240(C).
    7. Gorityala, Aishvaria & Radhika, Sudha & Bhattacharjee, Ankur & Mukherjee, Joyjit, 2025. "Squirrel search-based optimization of energy storage systems for electric vehicle charging stations," Energy, Elsevier, vol. 318(C).
    8. Güven, Aykut Fatih, 2024. "Integrating electric vehicles into hybrid microgrids: A stochastic approach to future-ready renewable energy solutions and management," Energy, Elsevier, vol. 303(C).
    9. Chen, Sheng & Cheng, Hao & Zhang, Hongcai & Lv, Si & Wei, Zhinong & Jin, Yuyang, 2025. "Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands," Applied Energy, Elsevier, vol. 377(PA).
    10. Zheng, Yanchong & Chen, Yuanyi & Yang, Qiang, 2025. "A Stackelberg-based competition model for optimal participation of electric vehicle load aggregators in demand response programs," Energy, Elsevier, vol. 315(C).
    11. Zhang, Wei & Wu, Jie, 2025. "Optimal real-time flexibility scheduling for community integrated energy system considering consumer psychology: A cloud-edge collaboration based framework," Energy, Elsevier, vol. 320(C).

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