Scheduling Electric Vehicle Fleets as a Virtual Battery under Uncertainty using Quantile Forecasts

Abstract

Electric vehicles have significant potential to reduce their charging costs by participating in electricity markets through electric vehicle smart charging. However, one of the main barriers to electric vehicle participation in an electricity market is the high uncertainty in their availability at the market gate closure time. Not accounting for this uncertainty when making market bids could result in high imbalance costs. This study proposes a method to determine the optimal bidding strategy for a fleet of electric vehicles under uncertainty using a scenario-based stochastic optimization algorithm. This model considers both the uncertainty in electric vehicle availability and uncertainty in imbalance prices in the electricity market, as well as the risk-aversiveness of aggregators to high charging costs using the conditional value-at-risk. It proposes to model the electric vehicle fleet as a virtual battery, and to use a set of quantile forecasts of the virtual battery parameters to account for the uncertainty in electric vehicle availability. The effectiveness of the proposed model is evaluated by testing it on an actual case study fleet. The results indicate that it is crucial to consider both the expected charging costs and the conditional value-at-risk when determining market bids for an electric vehicle fleet under uncertainty.