Robust Scheduling of Plug-In Electric Vehicles Aggregator in Day-Ahead and Reserve Markets

Abstract

In recent years, the role of electric vehicles in the transportation system is growing because of the unfavorable effects of the use of fossil fuels. Aggregation of plug-in electric vehicles can provide enough energy for them to act as demand-side resources and participate in electricity markets. The operation schedule of plug-in electric vehicles aggregators needs to be optimized in order to maximize their benefits. In this chapter, the optimal scheduling problem of plug-in electric vehicles aggregators for participation in day-ahead and reserve markets is studied. The uncertainty of market price is applied using robust optimization approach. The proposed model is a mixed-integer linear programming model. The model was implemented on a test system and solved using the General Algebraic Modeling System (GAMS) software. The results indicate that with a 2.51% decrease of aggregator’s total profit, the aggregator will be robust against 20% changes in the market price.