Building bidding curves for an EV aggregator via stochastic adaptive robust optimization

Abstract

This paper proposes a stochastic adaptive robust optimization approach to build the bidding curves of an aggregator in charge of a set of electric vehicles participating in the day-ahead electricity market. These bidding decisions are made on an hourly basis one day in advance so that they are made within an uncertain environment. In this sense, uncertainties comprise market prices, as well as driving requirements of electric vehicle users. These uncertainties are accounted for by using a set of scenarios and confidence bounds, respectively. In this way, this paper combines classic stochastic optimization techniques with adaptive robust optimization, taking into account how the various sources of uncertainty can be realistically modeled. Electric vehicles are equipped with the vehicle-to-grid technology so that they can both buy and sell energy to the market. A case study is analyzed to illustrate the performance of the proposed approach. Results show that the bidding decisions of the EV aggregator depend on the uncertainty in driving requirements of EVs, which can be controlled through the uncertainty budget. This highlights the usefulness of the proposed approach to prevent the attainment of suboptimal bidding decisions.