Demand side management of electric vehicles with uncertainty on arrival and departure times

Abstract

Uncertainty on arrival and departure times makes the scheduling of plug-in hybrid electric vehicles an intrinsically stochastic optimization problem. To take the stochastic nature of this problem into consideration, a scalable stochastic optimization strategy has been formulated. Generally, stochastic programming methods are computationally demanding and become impractical for large-scale problems. This work reduced the dimensionality of the scheduling problem with techniques from approximate dynamic programming. To illustrate the advantage of the stochastic algorithm a deterministic method has been formulated. Compared to the deterministic method, the proposed stochastic method can help an aggregator to reduce its expensive peak charging or avoid penalties for not fully charging the batteries of its clients.