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

TL;DR

This paper tackles the uncertainty inherent in providing grid reserves from EV fleets by introducing an aggregate-bound forecasting approach that treats all connected EVs as a single virtual battery with energy and power boundaries. It combines a multiple linear regression predictor for the aggregate boundaries with a two-stage stochastic model predictive control (SMPC) that includes Conditional Value-at-Risk (CVaR) to balance reserve revenue against non-delivery penalties. The methodology is validated on a GB case study using large-scale charging data and GB reserve-market price signals, showing that forecast accuracy improves with fleet size (up to a plateau around 400–400+ vehicles) and that average reserve provision per vehicle can reach about 1.8 kW, with cost reductions up to ~60% compared with uncontrolled charging. The work demonstrates the viability of EV fleets for day-ahead ancillary services, while outlining future directions such as intra-fleet energy transfers and enhanced prediction models for broader market conditions.

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.

Figures (12)

• Figure 1: Arrival and Departure Times for an Example EV Fleet of 3 Vehicles
• Figure 2: Power and Energy Boundaries for Example EV Fleet from Figure \ref{['fig:evtimes']}, Including a Possible Charging Trajectory (Black Dashed Line)
• Figure 3: Approximation of charging power variation with SOC
• Figure 4: Individual EV Power and Energy Boundaries with Direct Charging at the End
• Figure 5: Predicted Scenarios for Energy Boundaries