2024/02/20 by Alban Puech, Tristan Rigaut, Puech, Alban +5
Engineering · #Advanced Battery Technologies Research #Artificial Intelligence (cs.AI) #Computational Engineering #Electric Vehicles and Infrastructure #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Transportation and Mobility Innovations #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.13224
openalex publication_date 2024/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We propose a formulation of the problem of EVCS control under uncertainty, and implement two Multi-Stage Stochastic Programming approaches that leverage user-provided information, namely, Model Predictive Control and Two-Stage Stochastic Programming. The model addresses uncertainties in charging session start and end times, as well as in energy demand. A user's behavior model based on a sojourn-time-dependent stochastic process enhances cost reduction while maintaining customer satisfaction. The benefits of the two proposed methods are showcased against two baselines over a 22-day simulation using a real-world dataset. The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization. The algorithm prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline. Additionally, the algorithm striking the best balance between cost and user satisfaction exhibits a mere 3% relative cost increase compared to the theoretically optimal baseline - for which the nonanticipativity constraint is relaxed - while attaining 94% and 84% of the user satisfaction performance in the two used satisfaction metrics.