  The growing use of electric vehicles (EVs) may hinder their integration into
the electricity system as well as their efficient operation due to the
intrinsic stochasticity associated with their driving patterns. In this work,
we assume a profit-maximizer EV-aggregator who participates in the day-ahead
electricity market. The aggregator accounts for the technical aspects of each
individual EV and the uncertainty in its driving patterns. We propose a
hierarchical optimization approach to represent the decision-making of this
aggregator. The upper level models the profit-maximizer aggregator's decisions
on the EV-fleet operation, while a series of lower-level problems computes the
worst-case EV availability profiles in terms of battery draining and energy
exchange with the market. Then, this problem can be equivalently transformed
into a mixed-integer linear single-level equivalent given the totally
unimodular character of the constraint matrices of the lower-level problems and
their convexity. Finally, we thoroughly analyze the benefits of the
hierarchical model compared to the results from stochastic and deterministic
models.
