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

Alban Puech, Tristan Rigaut, William Templier, Maud Tournoud

TL;DR

The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization, and prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline.

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.

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

TL;DR

The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization, and prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline.

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.
Paper Structure (35 sections, 11 equations, 3 figures, 1 table)

This paper contains 35 sections, 11 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. EVCS Model
  3. Charging session start
  4. Charging session end
  5. Charging dynamics
  6. Costs and objectives
  7. Customer satisfaction
  8. Electricity costs
  9. Grid import threshold overrun
  10. Optimal control formulation
  11. State, exogenous uncertainty and control variables
  12. Controller definition and Multi-Stage Stochastic Programming policies
  13. Methodology
  14. Multi-Stage Stochastic Programming algorithms
  15. Two-stage Stochastic Programming (2S)
  16. ...and 20 more sections

Figures (3)

  • Figure 1: Working principle of a single iteration of the scenario generation algorithm on a single charging slot.
  • Figure 2: Distribution of the share of the initial energy request that could not be provided by the end of the charging sessions. $\alpha = 5000$
  • Figure 3: Full satisfaction and filling rates as a function of the electricity costs. text annotations correspond to the values of $\alpha$