Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
<|reference_start|>Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming: 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.<|reference_end|>
@article{puech2024controlling,
    title={Controlling Large Electric Vehicle Charging Stations via User Behavior
  Modeling and Stochastic Programming},
    author={Alban Puech, Tristan Rigaut, William Templier, Maud Tournoud},
    journal={arXiv preprint arXiv:2402.13224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13224},
    primaryClass={math.OC cs.AI cs.CE cs.LG cs.SY eess.SY}
}