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Collaborative participation of wind power producer and charging station aggregator in electricity markets

Author

Listed:
  • Abbasi, Mohammad Hossein
  • Mishra, Dillip Kumar
  • Arjmandzadeh, Ziba
  • Zhang, Jiangfeng
  • Xu, Bin
  • Krovi, Venkat

Abstract

The widespread adoption of electric vehicles (EVs) is hindered by two major challenges: limited fast-charging infrastructure and reliance on fossil-fuel-based electricity. Expanding fast-charging stations (FCSs) requires optimal scheduling, which is complicated by the stochastic behavior of EV users. Additionally, rapid fluctuations in renewable power availability, typically mitigated by fossil-fuel generation, can limit EVs’ environmental benefits. This paper addresses these challenges through the coordinated operation of a wind power producer (WPP) and an FCS aggregator, aiming to optimize the revenue of both parties while considering EV battery degradation and FCS charging limits. The problem is formulated as a bi-level optimization problem: the WPP and FCS aggregator maximize their own profits, linked via a peer-to-peer (P2P) energy trading agreement. It is then cast within a Lyapunov optimization framework, decomposing the problem into single-step subproblems, which reduces the impact of EV charging uncertainty. Collaboration with the aggregator decreases WPP’s imbalance by an average of 45.77 % in a case study, while the P2P energy trading increases the renewable share of power delivered to EVs by 11.17 % on average. Furthermore, a reinforcement learning agent is trained to improve FCS energy storage utilization. Simulation results show that the proposed approach can reduce daily FCS operating costs by up to 58 % and increase daily WPP profit by up to 31 %.

Suggested Citation

  • Abbasi, Mohammad Hossein & Mishra, Dillip Kumar & Arjmandzadeh, Ziba & Zhang, Jiangfeng & Xu, Bin & Krovi, Venkat, 2025. "Collaborative participation of wind power producer and charging station aggregator in electricity markets," Applied Energy, Elsevier, vol. 401(PC).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pc:s0306261925015120
    DOI: 10.1016/j.apenergy.2025.126782
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    References listed on IDEAS

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    1. Jiang, Benben & Berliner, Marc D. & Lai, Kun & Asinger, Patrick A. & Zhao, Hongbo & Herring, Patrick K. & Bazant, Martin Z. & Braatz, Richard D., 2022. "Fast charging design for Lithium-ion batteries via Bayesian optimization," Applied Energy, Elsevier, vol. 307(C).
    2. Lei, Nuo & Zhang, Hao & Hu, Jingjing & Hu, Zunyan & Wang, Zhi, 2025. "Sim-to-real design and development of reinforcement learning-based energy management strategies for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 393(C).
    3. Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    4. Wang, Kang & Wang, Haixin & Yang, Zihao & Feng, Jiawei & Li, Yanzhen & Yang, Junyou & Chen, Zhe, 2023. "A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 343(C).
    5. Ju, Liwei & Lv, ShuoShuo & Zhang, Zheyu & Li, Gen & Gan, Wei & Fang, Jiangpeng, 2024. "Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism," Applied Energy, Elsevier, vol. 362(C).
    6. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    7. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    8. Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    9. Cui, Jingshi & Cao, Yi & Wang, Bo & Wu, Jiaman, 2025. "Multi-stage adaptive expansion of EV charging stations considering impacts from the transportation network and power grid," Applied Energy, Elsevier, vol. 386(C).
    10. 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).
    11. Nesma M Ashraf & Reham R Mostafa & Rasha H Sakr & M Z Rashad, 2021. "Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-24, June.
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