  A total 19% of generation capacity in California is offered by PV units and
over some months, more than 10% of this energy is curtailed. In this research,
a novel approach to reduce renewable generation curtailments and increasing
system flexibility by means of electric vehicles' charging coordination is
represented. The presented problem is a sequential decision making process, and
is solved by fitted Q-iteration algorithm which unlike other reinforcement
learning methods, needs fewer episodes of learning. Three case studies are
presented to validate the effectiveness of the proposed approach. These cases
include aggregator load following, ramp service and utilization of
non-deterministic PV generation. The results suggest that through this
framework, EVs successfully learn how to adjust their charging schedule in
stochastic scenarios where their trip times, as well as solar power generation
are unknown beforehand.
