1. A stochastic approach for EV charging stations in demand response programs.
- Author
-
Zanvettor, Giovanni Gino, Fochesato, Marta, Casini, Marco, Lygeros, John, and Vicino, Antonio
- Subjects
- *
ELECTRIC vehicle charging stations , *ELECTRIC charge , *ELECTRIC vehicle industry , *DISTRIBUTION (Probability theory) , *OPERATING costs , *ELECTRIC vehicles - 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. • A stochastic receding horizon scheme is introduced for real-time EV charging. • Acquired online information is employed to refine load forecasts. • EV probability distributions are used to manage the uncertain demand of EVs. • The algorithm performance is validated by comparison with oracle-based optimization. • Computation times are feasible for problem instances involving high EV penetrations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF