A stochastic approach for EV charging stations in demand response programs
Giovanni Gino Zanvettor,
Marta Fochesato,
Marco Casini,
John Lygeros and
Antonio Vicino
Applied Energy, 2024, vol. 373, issue C, No S0306261924012455
Abstract:
Demand response is expected to play a fundamental role in renewable energy communities to alleviate the electricity demand–supply mismatch, especially in the presence of stochastic load and generation. In this paper, we consider an electric vehicle charging station that participates in incentive-based demand response programs. A real-time charging scheme is devised to optimize the charging station operation by coordinating the charging process of the electric vehicles, and complying with the incoming demand response requests. In this context, vehicle demand is assumed uncertain, while demand response requests ask for a change in the charging profile over certain time intervals, in exchange for a monetary reward. By exploiting the probability distributions describing the vehicle charging process, a stochastic formulation is employed to devise a novel charging algorithm aimed at reducing the charging station operational cost. Such a procedure can (i) handle the uncertainty affecting the charging process in different settings and scenarios, and (ii) exploit the information collected in real-time to refine forecasts and hence ensure a higher demand flexibility. Numerical results show that the proposed approach ensures considerable cost reduction compared to the benchmarks, and features highly scalable runtimes.
Keywords: Electric vehicles; Charging stations; Demand response; Receding horizon (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:373:y:2024:i:c:s0306261924012455
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DOI: 10.1016/j.apenergy.2024.123862
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