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

arXiv: https://arxiv.org/abs/2402.13224 · HTML: https://mlsanity.com/paper/2402.13224/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Builds on (most cited first, 5 of 5)

- [Parallelizing the dual revised simplex method](https://mlsanity.com/paper/1503.01889/) (2015)
- [Carbon-Aware EV Charging](https://mlsanity.com/paper/2209.12373/) (2022)
- [Real-Time Electric Vehicle Smart Charging at Workplaces: A Real-World Case Study](https://mlsanity.com/paper/2203.06847/) (2022)
- [Controlling Microgrids Without External Data: A Benchmark of Stochastic Programming Methods](https://mlsanity.com/paper/2304.14808/) (2023)
- [Optimal Battery Charge Scheduling For Revenue Stacking Under Operational Constraints Via Energy Arbitrage](https://mlsanity.com/paper/2305.04566/) (2023)

## Abstract (arXiv)

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.