Reduction of Computational Complexity for Optimal Electric Vehicle Schedulings

Abstract

This paper proposes a model to aggregate individual electric vehicles (EVs) into virtual EVs, which is called the EV aggregation cluster model (EACM). In addition, a multi-stage optimization method is also proposed to minimize the electricity cost for model buildings. The EACM and proposed multi-stage optimization method reduce the decision variables in an objective function while considering stage-of-charge (SoC) constraints for all individual EVs. As a result, the computational time is reduced and obtained schedules for individual EVs, allows for near minimal cost, which is validated by the simulation. In the simulation, the computational time using the proposed methods are 32% of the conventional method at most. The cost gap between an optimal EV charging schedule and an approximated one obtained by the proposed method is less than 5% regardless of the number of EVs.