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.
There has been an increasing body of literature on control mechanisms for EV charging, while the literature on designs for charging stations is rather sparse. The predominant assumption for the control of EV charging is that the vehicles are located either in their drivers' residencies or at large parking lots and also depending on the charger technology the duration of the charging session is of the order of hours. The vast majority of the literature threats EVs as smart loads" and management of their demand is achieved by optimally adjusting the charging current. Overall, related literature can be classified into two categories: centralized and decentralized (distributed) control @cite_20 . In centralized control a network operator (dispatcher) possesses global information about all users and to a large extent controls and mandates charging times, rates etc. For instance, the authors of @cite_17 propose a direct load management for EV chargings. Similarly, in our previous work @cite_14 , we introduce a central EV allocation framework for a network of fast charging stations.
@cite_14 @cite_20 @cite_17
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In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging and complex problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked (and hence not served) by the closest charging station to their location. Overall, the paper addresses key issues related to the efficient operation, both from the perspective of the power grid and the drivers satisfaction, of a network of charging stations. Research predicts that in 2030, around 30 of all vehicles in Belgium will be plug-in hybrid electric vehicles (PHEVs). Because most PHEVs are charged after working hours, the existing peak load in the evening will increase significantly. Large peak loads cause more expensive production and can even damage the electricity infrastructure. In a Smart Grid, the charging of PHEVs can be controlled to reduce peak load, denoted as demand side management (DSM). The goal of our research is to compare several solutions for DSM of PHEVs. This paper takes a first step by benchmarking a multi-agent solution against an optimal quadratic programming (QP) scheduler solution. Simulations show that a QP scheduler is able to optimally flatten peak loads while sufficiently charging the PHEV batteries. However, this solution is unfeasible in practice because it scales poorly and requires complete information on when and how much PHEVs need to charge beforehand, which is not available. The MAS solution proves to be scalable and adaptable to incomplete and unpredictable information while peaks are still reduced with an efficiency up to 95 compared to the QP scheduler. Accurate real-time load forecasting is essential for the reliable and efficient operation of a power system. Assuming that the number of electrical vehicles will increase substantially in the near future, the load profiles of the system will become too volatile and unpredictable for the current forecasting techniques. We propose to utilize the accurate reporting of the emerging Advanced Metering Infrastructure (AMI) to track the incoming PHEV load requests and their statistics. We propose a model for the PHEV loads statistics and an optimization of the generation dispatch that uses the full statistical information. This model offers an example of the potential impact of the smart metering infrastructure currently being deployed.