Joint chance-constrained program based electric vehicles optimal dispatching strategy considering drivers' response uncertainty

Abstract

Electric vehicles (EVs) bring both opportunities and challenges to the distribution network due to their features of random behavior, storage capacity and charging flexibility. EV aggregator (EVA) can serve to unlock the dispatching potential of large scale of EVs and participate in the joint energy and reserve electricity market. In this paper, the improved EVA model for ancillary services is firstly established. Then, the dispatching potential of EVs considering drivers' response willingness and its uncertainty through EVA is explored. To obtain the optimal incentive price considering the driver response uncertainty, the joint chance-constrained program (JCCP) model is proposed. Due to the nonlinearity and nonconvexity of probability constraints, the developed JCCP model is intractable. A Monte Carlo based sequential convex approximation (SCA) algorithm is further developed to achieve the tractability and solve the JCCP model effectively. Case studies show that the proposed incentive program can reduce 15.1% net cost of EVA compared to uncoordinated charging. Besides, the proposed SCA algorithm can solve the JCCP problem with more accurate and less conservative results compared to the existing method, while achieving a decent computational efficiency for the day-ahead dispatching.