text: 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, and occasionally be publicly available, offering flexible charging options. Ideally, each vehicle will charge during periods when electricity prices are relatively low, to minimize the cost to the consumer and maximize societ al 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 ensure the demands of the vehicles and the market are met. Our dynamic algorithm depends only on the knowledge of a few hundred 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.
  @cite_5: The paper develops and illustrates a novel decentralized charging control algorithm for large populations of plug-in electric vehicles (PEVs). The proposed algorithm is an application of the so-called Nash certainty equivalence principle (or mean-field games.) The control scheme seeks to achieve social optimality by establishing a PEV charging schedule that fills the overnight demand valley. The paper discusses implementation issues and computational complexity, and illustrates concepts with various numerical examples.
  @cite_18: This paper proposes an operating framework for aggregators of plug-in electric vehicles (PEVs). First, a minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands. The same algorithm is applicable for negotiating bilateral contracts. Second, a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day. Simulation results are used to evaluate the proposed algorithms, and to demonstrate the potential impact of an aggregated PEV fleet on the power system.
  @cite_7: For vehicle-to-grid (V2G) frequency regulation services, we propose an aggregator that makes efficient use of the distributed power of electric vehicles to produce the desired grid-scale power. The cost arising from the battery charging and the revenue obtained by providing the regulation are investigated and represented mathematically. Some design considerations of the aggregator are also discussed together with practical constraints such as the energy restriction of the batteries. The cost function with constraints enables us to construct an optimization problem. Based on the developed optimization problem, we apply the dynamic programming algorithm to compute the optimal charging control for each vehicle. Finally, simulations are provided to illustrate the optimality of the proposed charging control strategy with variations of parameters.
summary: in @cite_7 use dynamic programming to assign charging schedules that provide frequency regulation. Similarly, in the work by @cite_18 an algorithm is constructed that makes dynamic decisions for lowest-cost charging schedules of PEVs. Neither of these works considers an obligation to the market that needs to be met, and the demand of each vehicle is considered individually (i.e., not as part of a fleet); both suggested algorithms would result in an increased peak demand. In @cite_5 , establish a decentralized algorithm that determines an equilibrium price so that the total amount of charging done in the fleet fills the overnight demand valley.' This algorithm takes into account all vehicles in the fleet, but is not dynamic (i.e., it is solves an offline problem) and assumes only a few types of driving behaviors exist. A dynamic (or online) algorithm does not require that all vehicles are connected to the grid at the same time to exactly report their future driving schedules, and makes decisions for each vehicle without solving a problem that depends on knowing the demands of the entire fleet.