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.
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.
@cite_5 @cite_18 @cite_7
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We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guide their updates. The algorithm converges to optimal charging profiles (that are as “flat” as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation. We address the optimal bidding problem of an aggregator that manages the charging of a group of plug-in electric vehicles. The aggregator purchases energy in the day-ahead market, and offers capacity in the frequency regulation market. The charging flexibility of the fleet is expressed as a set of time-varying power and energy constraints. The impact of the aggregator's demand on day-ahead market prices is endogenously modelled by formulating the problem as a bilevel problem: The upper level represents the aggregator's cost minimisation, and the lower level represents the market clearing. We explore several settings of the regulation market, analysing the impact of restrictions that may apply on the capacity offers (e.g., symmetric bids). Moreover, we compare the options of providing regulation with unidirectional charging, and with vehicle-to-grid (V2G). We test the proposed method in a case study for Switzerland, with historical day-ahead and regulation market data, and realistic driving patterns. 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. Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2 sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management coordination unit, gaining robustness against single-link node failures, and being scalable in terms of single-node computations.