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Dynamic pricing and control for EV charging stations with solar generation

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  • Cedillo, Mónica Hernández
  • Sun, Hongjian
  • Jiang, Jing
  • Cao, Yue

Abstract

Demand response is one of the most promising tools for smart grids to integrate more renewable energy sources. One critical challenge to overcome is how to establish pricing and control strategies for integrating more electric vehicles (EVs) and renewable energy sources. This paper proposes a dynamic optimal operation of a solar-powered EV charging station where onsite solar generation, number of EVs in the system, historical EV response to price, EV technical specifications and EV driving behaviour vary. A bi-level optimisation approach is proposed, where pricing tariffs ensure an economic and price responsive operation, then EV charging schedules are computed for energy bidding capacity to provide balancing services. Simulations are conduced to evaluate the performance of unidirectional and bidirectional EV charging at different charging speeds and demand elasticity. Results demonstrate the potential of extra revenue streams coming from the participation in energy markets compared to that of EV charging alone. Additionally, limitations of energy bidding with battery size, trip requirements and charging ratings are discussed to show insights into the operation of charging stations.

Suggested Citation

  • Cedillo, Mónica Hernández & Sun, Hongjian & Jiang, Jing & Cao, Yue, 2022. "Dynamic pricing and control for EV charging stations with solar generation," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922011771
    DOI: 10.1016/j.apenergy.2022.119920
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    References listed on IDEAS

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    1. Mauro Lafratta & Matthew Leach & Rex B. Thorpe & Mark Willcocks & Eve Germain & Sabeha K. Ouki & Achame Shana & Jacquetta Lee, 2021. "Economic and Carbon Costs of Electricity Balancing Services: The Need for Secure Flexible Low-Carbon Generation," Energies, MDPI, vol. 14(16), pages 1-21, August.
    2. Liu, Liansheng & Kong, Fanxin & Liu, Xue & Peng, Yu & Wang, Qinglong, 2015. "A review on electric vehicles interacting with renewable energy in smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 648-661.
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    Cited by:

    1. Zhang, Gang & Wen, Jiaxing & Xie, Tuo & Zhang, Kaoshe & Jia, Rong, 2023. "Bi-layer economic scheduling for integrated energy system based on source-load coordinated carbon reduction," Energy, Elsevier, vol. 280(C).
    2. Monica Alonso & Hortensia Amaris & David Martin & Arturo de la Escalera, 2023. "Proximal Policy Optimization for Energy Management of Electric Vehicles and PV Storage Units," Energies, MDPI, vol. 16(15), pages 1-20, July.
    3. 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).
    4. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    5. Board, Anthony & Sun, Yongjun & Huang, Pei & Xu, Tao, 2024. "Community-to-vehicle-to-community (C2V2C) for inter-community electricity delivery and sharing via electric vehicle: Performance evaluation and robustness analysis," Applied Energy, Elsevier, vol. 363(C).

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