Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control

TL;DR

This paper presents a stochastic model predictive control approach for aggregating electric vehicle reserves, demonstrating improved predictability and cost savings with larger fleets, facilitating renewable integration.

Contribution

It introduces an aggregate boundary forecasting method and a two-stage stochastic control algorithm for EV fleet reserve scheduling, enhancing predictability and economic benefits.

Findings

Prediction accuracy improves with fleet size.

Cost reductions plateau at 60% for large fleets.

Average reserve per vehicle is 1.8kW.

Abstract

Controlled charging of electric vehicles, EVs, is a major potential source of flexibility to facilitate the integration of variable renewable energy and reduce the need for stationary energy storage. To offer system services from EVs, fleet aggregators must address the uncertainty of individual driving and charging behaviour. This paper introduces a means of forecasting the service volume available from EVs by considering several EV batteries as one conceptual battery with aggregate power and energy boundaries. Aggregation avoids the difficult prediction of individual driving behaviour. The predictability of the boundaries is demonstrated using a multiple linear regression model which achieves a normalised root mean square error of 20% - 40% for a fleet of 1,000 EVs. A two-stage stochastic model predictive control algorithm is used to schedule reserve services on a day-ahead basis addressing risk trade-offs by including Conditional Value-at-Risk in the objective function. A case study with 1.2 million domestic EV charge records from Great Britain illustrates that increasing fleet size improves prediction accuracy, thereby increasing reserve revenues and decreasing an aggregator's operational costs. For fleet sizes of 400 or above, cost reductions plateau at 60% compared to uncontrolled charging, with an average of 1.8kW of reserve provided per vehicle.