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.
This work also builds on @cite_14 , that evaluated the above described charging model in a network context using real world traces obtained from the Seattle public bus system. The obtained results indicated that the spatial distribution of EVs follows a Beta distribution, which is also used in the present study. Consequently, at given times (e.g., rush hour during weekdays), some regions (e.g., downtown) are busier than others. Accordingly, stations near a high density area are busier than other stations and unless an EV routing mechanism is in place, they would fail to meet preset QoS requirements. block = [rectangle, draw,top color =white , bottom color = processblue!20, text width=5em, text centered, rounded corners, minimum height=3em] line = [draw, -triangle 45]
@cite_14
2023366187 2052144254 2047902637 2015020077
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. Access to electric vehicle (EV) charging stations will affect EV adoption rates, use decisions, electrified mile shares, petroleum demand, and power consumption across times of day. Parking information from more than 30,000 records of personal trips in the Puget Sound, Washington, Regional Council's 2006 Household Activity Survey is used to determine public (nonresidential) parking locations and durations. Regression equations predict parking demand variables (i.e., total vehicle hours per zone or neighborhood and parked time per vehicle trip) as a function of site accessibility, local job and population density, trip attributes, and other variables available in most regions and travel surveys. Several of these variables are key inputs to a mixed-integer programming problem developed to determine optimal location assignments for EV charging stations. The algorithm minimizes EV users' costs for station access while penalizing unmet demand. This useful specification is used to determine the top locations fo... This paper presents a spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M M s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M M s queueing theory with the arrival rate of discharged vehicles. This mathematical model of charging demand may allow grid's distribution planners to anticipate a charging demand profile at a charging station. A numerical example shows that the proposed model is able to capture the spatial and temporal dynamics of charging demand in a highway charging station. 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.