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. The increasing market share of electric vehicles (EVs) makes large-scale charging stations indispensable infrastructure for integrating EVs into the future smart grid. Thus their operation modes have drawn great attention from researchers. One promising mode called park-and-charge was recently proposed. It allows people to park their EVs at a parking lot, where EVs can get charged during the parking time. This mode has been experimented and demonstrated in small scale. However, the missing of an efficient market mechanism is an important gap preventing its large-scale deployment. Existing pricing policies, e.g., pay-by-use and flat-rate pricing, would jeopardize the efficiency of electricity allocation and the corresponding social welfare in the park-and-charge mode, and thus are inapplicable. To find an efficient mechanism, this paper explores the feasibility and benefits of utilizing auction mechanism in the EV park-and-charge mode. The auction allows EV users to submit and update bids on their charging demand to the charging station, which makes corresponding electricity allocation and pricing decisions. To this end, we propose Auc2Charge, an online auction framework. Auc2Charge is truthful and individual rational. Running in polynomial time, it provides an efficient electricity allocation for EV users with a close-form approximation ratio on system social welfare. Through both theoretical analysis and numerical simulation, we demonstrate the efficacy of Auc2Charge in terms of social welfare and user satisfaction. A large penetration of electric and plug-in hybrid electric vehicles would likely result in increased system peaks and overloading of power system assets if the charging of vehicles is left uncontrolled. In this paper we propose both a centralized and a decentralized smart-charging scheme which seek to minimize system-wide generation costs while respecting grid constraints. Under the centralized scheme, vehicles' batteries are aggregated to virtual storage resources at each network node, which are optimally dispatched with a multiperiod Optimal Power Flow. On the other hand, under the decentralized scheme, price profiles broadcasted to vehicles day-ahead are determined so that the optimal response of individual vehicles to this tariff achieves the goal of cost minimization. Two alternative tariffs are explored, one where the same price profile applies system-wide, and another where different prices can be defined at different nodes. Results show that compared with uncontrolled charging, these smart-charging schemes successfully avoid asset overloading, displace most charging to valley hours and reduce generation costs. Moreover they are robust in the face of forecast errors in vehicle behavior. 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.