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The growing use of electric vehicles (EVs) may hinder their integration intothe electricity system as well as their efficient operation due to theintrinsic stochasticity associated with their driving patterns. In this work,we assume a profit-maximizer EV-aggregator who participates in the day-aheadelectricity market. The aggregator accounts for the technical aspects of eachindividual EV and the uncertainty in its driving patterns. We propose ahierarchical optimization approach to represent the decision-making of thisaggregator. The upper level models the profit-maximizer aggregator's decisionson the EV-fleet operation, while a series of lower-level problems computes theworst-case EV availability profiles in terms of battery draining and energyexchange with the market. Then, this problem can be equivalently transformedinto a mixed-integer linear single-level equivalent given the totallyunimodular character of the constraint matrices of the lower-level problems andtheir convexity. Finally, we thoroughly analyze the benefits of thehierarchical model compared to the results from stochastic and deterministicmodels.

An Efficient Robust Approach to the Day-ahead Operation of an Aggregator of Electric Vehicles