The operation of the power grid is becoming more stressed, due to the addition of new large loads represented by electric vehicles (EVs) and a more intermittent supply due to the incorporation of renewable sources. As a consequence, the coordination and control of projected EV demand in a network of fast charging stations becomes a critical and challenging problem. In this paper, we introduce a game theoretic based decentralized control mechanism to alleviate negative impacts from the EV demand. The proposed mechanism takes into consideration the nonuniform spatial distribution of EVs that induces uneven power demand at each charging facility, and aims to: 1) avoid straining grid resources by offering price incentives, so that customers accept being routed to less busy stations; 2) maximize total revenue by serving more customers with the same amount of grid resources; and 3) provide charging service to customers with a certain level of quality-of-service (QoS), the latter defined as the long term customer blocking probability. We examine three scenarios of increased complexity that gradually approximate real world settings. The obtained results show that the proposed framework leads to substantial performance improvements in terms of the aforementioned goals when compared to current state of affairs.
In this paper, we present a scalable approach for DSM (demand side management) of PHEVs (plug-in hybrid electric vehicles). Essentially, our approach consists of three steps: aggregation, optimization, and control. In the aggregation step, individual PHEV charging constraints are aggregated upwards in a tree structure. In the optimization step, the aggregated constraints are used for scalable computation of a collective charging plan, which minimizes costs for electricity supply. In the real-time control step, this charging plan is used to create an incentive signal for all PHEVs, determined by a market-based priority scheme. These three steps are executed iteratively to cope with uncertainty and dynamism. In simulation experiments, the proposed three-step approach is benchmarked against classic, fully centralized approaches. Results show that our approach is able to charge PHEVs with comparable quality to optimal, centrally computed charging plans, while significantly improving scalability. This paper gives a structured literature overview of coordinated charging of electric vehicles (EVs). The optimization objective, scale and method of each coordination strategy are the three parameters used to characterize and compare different approaches. The correlation between the three parameters and the research category are investigated, resulting in a correlation mapping of the different approaches. This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand “valley”. The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation.