Optimal scheduling for charging electric vehicles with fixed setup costs.

摘要

The increasing popularity of electric vehicles (EV) will pose great challenge to the nation's existing power grid by adding extra load during evening peak hours. This thesis develops a centralized optimal charging scheduling (OCS) model with a mixed integer nonlinear program to mitigate the negative impact of extra load from EVs on the power grid. The objective of the OCS model is to minimize the energy cost of the entire system and fixed setup costs for day-time charging, which essentially levels the load of the entire power grid throughout a day under the dynamic pricing environment. Furthermore, a rolling horizon heuristic algorithm is proposed as an alternative solution that addresses large scale OCS instances. Finally, when centralized scheduling is impractical, this thesis proposes a decentralized optimal charging heuristic using the concepts of game theory and coordinate search. Numerical results show that the optimal charging scheduling model can significantly lower the total energy cost and the peak-to-average ratio (PAR) for a power system. When compared to uncontrolled charging, the decentralized charging heuristic yields considerable energy savings as well, although not as efficient as the centralized optimal charging solutions.