A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator

This paper analyzes the bidding strategy problem of an electric vehicle aggregator that participates in the day-ahead energy market. The problem is formulated using a stochastic robust optimization model in which uncertainties in the day-ahead market prices and in the driving requirements of electric vehicles are modeled using scenarios and confidence bounds, respectively. The output of the proposed model is used to build the bidding curves to be submitted by the aggregator to the day-ahead market. We assume that the electric vehicle aggregator behaves as a price-taker in this market. A case study is analyzed to illustrate the main features of the proposed approach, as well as its applicability. We also compare the results with those achieved by considering other strategies. Results show that the proposed approach allows the aggregator to reduce the charging costs in comparison with other charging strategies. Moreover, the solution obtained is robust in the sense that driving requirements of electric vehicle users are met.