Predictive Control of EV Overnight Charging with Multi-Session Flexibility

TL;DR

This paper presents a model predictive control approach for EV overnight charging that leverages multi-session flexibility to significantly reduce CO2 emissions over multiple days, considering user behavior and regional energy mixes.

Contribution

It introduces a multi-session planning method that relaxes the full-charge requirement, enabling better emission reductions in EV charging control.

Findings

40-46% emission reduction compared to uncontrolled charging

19-26% reduction compared to single-session planning

Significant regional variation in emission savings

Abstract

The majority of electric vehicles (EVs) are charged domestically overnight, where the precise timing of power allocation is not important to the user, thus representing a source of flexibility that can be leveraged by charging control algorithms. In this paper, we relax the common assumption, that EVs require full charge every morning, enabling additional flexibility to defer charging of surplus energy to subsequent nights, which can enhance the performance of controlled charging. In particular, we consider a simple domestic smart plug, scheduling power delivery with the objective to minimize CO$_2$ emissions over prediction horizons of multiple sessions -- up to seven days ahead -- utilising model predictive control (MPC). Based on carbon intensity data from the UK National Grid, we demonstrate significant potential for emission reductions with multi-session planning of 40 to 46\% compared to uncontrolled charging and 19 to 26\% compared to single-session planning. Furthermore, we assess, how the driving and charging behaviour of EV users affects the available flexibility and consequentially the potential for emission reductions. Finally, using grid carbon intensity data from 14 different UK regions, we report significant variations in absolute emission reductions based on the local energy mix.