  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. This avoids the impossible
task of predicting individual driving behaviour by taking advantage of the law
of large numbers. The forecastability of the boundaries is demonstrated in a
multiple linear regression model which achieves an $R^2$ of 0.7 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 shows that
increasing fleet size improves prediction accuracy, thereby increasing reserve
revenues and decreasing effective charging costs. For fleet sizes of 400 or
above, charging cost reductions plateau at 60\%, with an average of 1.8kW of
reserve provided per vehicle.
