A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles

Plug-in electric vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, offering flexible charging options. Ideally, each vehicle will charge when electricity prices are relatively low, to minimize the cost to the consumer and maximize societal benefits. A demand response (DR) service for a fleet of PEVs could yield such charging schedules by regulating consumer electricity use during certain time periods, in order to meet an obligation to the market. We construct an automated DR mechanism for a fleet of PEVs that facilitates vehicle charging to meet the needs of the vehicles and satisfy a load scheduling obligation. Our dynamic algorithm depends only on the knowledge of driving behaviors from a previous similar day, and uses a simple adjusted pricing scheme to instantly assign feasible and satisfactory charging schedules to thousands of vehicles in a fleet as they plug-in. The charging schedules generated using our adjusted pricing scheme can ensure that a new demand peak is not created and can reduce the consumer cost by over 30% when compared to standard charging, which may also increase peak demand by 3.5%. In this paper, we present our formulation, algorithm, and results.