Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming

• URL: http://arxiv.org/abs/2402.13224v3
• Date: Tue, 19 Mar 2024 12:28:13 GMT
• Title: Controlling Large Electric Vehicle Charging Stations via User Behavior Modeling and Stochastic Programming
• Authors: Alban Puech, Tristan Rigaut, William Templier, Maud Tournoud,
• Abstract summary: This paper introduces an Electric Vehicle Charging Station model that incorporates real-world constraints. We propose two Multi-Stage Programming approaches that leverage user-provided information. A user's behavior model based on a sojourn-time-dependent process enhances cost reduction while maintaining customer satisfaction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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