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Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology

Author

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  • Yang Gao

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Xiaohong Zhang

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Qingyuan Yan

    (College of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China)

  • Yanxue Li

    (State Grid Integrated Energy Planning and D&R Institute Co., Ltd., Beijing 100052, China)

Abstract

With the rapid increase in electric vehicle (EV) ownership, the uncertainty of EV charging demand has become a significant concern, especially in distributed photovoltaic (PV) power distribution networks (DNs) with high penetration rates. This growing demand presents challenges in meeting the needs of EV owners and grid charging/discharging stations (GCDSs), jeopardizing the stability, efficiency, reliability, and sustainability of the DNs. To address these challenges, this study introduces innovative models, the anchoring effect, and regret theory for EV demand response (DR) decision-making, focusing on dual-sided demand management for GCDSs and EVs. The proposed model leverages the light spectrum optimizer–convolutional neural network to predict PV output and utilizes Monte Carlo simulation to estimate EV charging load, ensuring precise PV output prediction and effective EV distribution. To optimize DR decisions for EVs, this study employs time-of-use guidance optimization through a logistic–sine hybrid chaotic–hippopotamus optimizer (LSC-HO). By integrating the anchoring effect and regret theory model with LSC-HO, this approach enhances satisfaction levels for GCDSs by balancing DR, enhancing voltage quality within the DNs. Simulations on a modified IEEE-33 system confirm the efficacy of the proposed approach, validating the efficiency of the optimal scheduling methods and enhancing the stable operation, efficiency, reliability, and sustainability of the DNs.

Suggested Citation

  • Yang Gao & Xiaohong Zhang & Qingyuan Yan & Yanxue Li, 2025. "Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology," Sustainability, MDPI, vol. 17(6), pages 1-27, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2536-:d:1611789
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    References listed on IDEAS

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    1. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    2. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    3. Lin, Runzi & Xu, Zhenhui & Huang, Xiaoliang & Gao, Jinwu & Chen, Hong & Shen, Tielong, 2022. "Optimal scheduling management of the parking lot and decentralized charging of electric vehicles based on Mean Field Game," Applied Energy, Elsevier, vol. 328(C).
    4. Liu, Lu & Zhou, Kaile, 2022. "Electric vehicle charging scheduling considering urgent demand under different charging modes," Energy, Elsevier, vol. 249(C).
    5. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    6. Wu, Fuzhang & Yang, Jun & Li, Bin & Crisostomi, Emanuele & Rafiq, Hogir & Rashed, Ghamgeen Izat, 2024. "Uncertain scheduling potential of charging stations under multi-attribute uncertain charging decisions of electric vehicles," Applied Energy, Elsevier, vol. 374(C).
    7. Deng, Zhihua & Wang, Haijiang & Liu, Hao & Chen, Qihong & Zhang, Jiashun, 2024. "Degradation prediction of proton exchange membrane fuel cell using a novel neuron-fuzzy model based on light spectrum optimizer," Renewable Energy, Elsevier, vol. 234(C).
    8. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    9. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    10. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).
    11. Lu, Jiewei & Yin, Wanjun & Wang, Pengju & Ji, Jianbo, 2024. "EV charging load forecasting and optimal scheduling based on travel characteristics," Energy, Elsevier, vol. 311(C).
    12. Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    13. Scott, Connor & Ahsan, Mominul & Albarbar, Alhussein, 2023. "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Elsevier, vol. 278(C).
    14. Obeid, Hassan & Ozturk, Ayse Tugba & Zeng, Wente & Moura, Scott J., 2023. "Learning and optimizing charging behavior at PEV charging stations: Randomized pricing experiments, and joint power and price optimization," Applied Energy, Elsevier, vol. 351(C).
    15. Zhang, Kaizhe & Xu, Yinliang & Sun, Hongbin, 2024. "Bilevel optimal coordination of active distribution network and charging stations considering EV drivers' willingness," Applied Energy, Elsevier, vol. 360(C).
    16. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
    17. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
    18. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.
    19. Wu, Ji & Su, Hao & Meng, Jinhao & Lin, Mingqiang, 2023. "Electric vehicle charging scheduling considering infrastructure constraints," Energy, Elsevier, vol. 278(PA).
    20. Yin, Wanjun & Ji, Jianbo, 2024. "Research on EV charging load forecasting and orderly charging scheduling based on model fusion," Energy, Elsevier, vol. 290(C).
    21. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
    22. Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zhou, Mi & Cao, Jinxin & Cai, Li, 2024. "Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets," Energy, Elsevier, vol. 313(C).
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