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Stochastic optimization of integrated electric vehicle charging stations under photovoltaic uncertainty and battery power constraints

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  • Dong, Xiao-Jian
  • Shen, Jia-Ni
  • Ma, Zi-Feng
  • He, Yi-Jun

Abstract

Optimal scheduling based on accurate power state prediction of key equipment is vital to enhance renewable energy utilization and alleviate charging electricity strain on the main grid in the integrated electric vehicle charging station (EVCS) with photovoltaic (PV) and battery energy storage system (BESS). However, the multi-source power prediction uncertainty of PV and the complex discharging/charging state of power (SOP) constraints of BESS were less explored in previous studies, which might result in suboptimal or even infeasible scheduling. This study proposes a novel stochastic scheduling optimization framework for the PV-BESS-EVCS integrated system. In this framework, a hybrid characterization method is developed to describe the multi-source uncertainty of PV power, and a piecewise linear model is developed to estimate the dynamic SOP constraints of BESS. The effectiveness of the proposed framework is demonstrated by a set of typical scenarios for commercial regions. The results indicate that compared to optimal results in the deterministic model, the proposed framework could improve operational profits by 0.41 % while reducing equivalent carbon emissions and power curtailment rates by 0.61 % and 3.05 %, respectively. It is thus illustrated that the proposed framework could provide a promising solution for the optimal scheduling of the PV-BESS-EVCS integrated system under uncertainty.

Suggested Citation

  • Dong, Xiao-Jian & Shen, Jia-Ni & Ma, Zi-Feng & He, Yi-Jun, 2025. "Stochastic optimization of integrated electric vehicle charging stations under photovoltaic uncertainty and battery power constraints," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224039410
    DOI: 10.1016/j.energy.2024.134163
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    as
    1. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    2. Rehman, Waqas ur & Bo, Rui & Mehdipourpicha, Hossein & Kimball, Jonathan W., 2022. "Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation," Applied Energy, Elsevier, vol. 313(C).
    3. Xu, Yuanyuan & Yang, Genke & Luo, Jiliang & He, Jianan & Sun, Haixin, 2022. "A multi-location short-term wind speed prediction model based on spatiotemporal joint learning," Renewable Energy, Elsevier, vol. 183(C), pages 148-159.
    4. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    5. Langenmayr, Uwe & Wang, Weimin & Jochem, Patrick, 2020. "Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic," Applied Energy, Elsevier, vol. 280(C).
    6. Dong, Xiao-Jian & Shen, Jia-Ni & Ma, Zi-Feng & He, Yi-Jun, 2022. "Simultaneous operating temperature and output power prediction method for photovoltaic modules," Energy, Elsevier, vol. 260(C).
    7. Wang, Meng & Peng, Jinqing & Luo, Yimo & Shen, Zhicheng & Yang, Hongxing, 2021. "Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements," Energy, Elsevier, vol. 224(C).
    8. Jiao, Feixiang & Ji, Chengda & Zou, Yuan & Zhang, Xudong, 2021. "Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV," Applied Energy, Elsevier, vol. 304(C).
    9. Dong, Xiao-Jian & Shen, Jia-Ni & Liu, Cheng-Wu & Ma, Zi-Feng & He, Yi-Jun, 2024. "Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system," Energy, Elsevier, vol. 289(C).
    10. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
    11. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    12. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    13. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).
    14. Roberts, Justo José & Mendiburu Zevallos, Andrés A. & Cassula, Agnelo Marotta, 2017. "Assessment of photovoltaic performance models for system simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1104-1123.
    15. Nikmehr, Nima, 2020. "Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs," Energy, Elsevier, vol. 199(C).
    16. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    17. Lei, Kaixuan & Chang, Jianxia & Wang, Xuebin & Guo, Aijun & Wang, Yimin & Ren, Chengqing, 2023. "Peak shaving and short-term economic operation of hydro-wind-PV hybrid system considering the uncertainty of wind and PV power," Renewable Energy, Elsevier, vol. 215(C).
    18. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    19. Wang, Frank Yifan & Chen, Zhuoxu & Hu, Zechun, 2024. "Comprehensive optimization of electrical heavy-duty truck battery swap stations with a SOC-dependent charge scheduling method," Energy, Elsevier, vol. 308(C).
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