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

Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability