Plug-in electric vehicle charging demand estimation based on queueing network analysis

Liang, Hao; Sharma, Isha; Zhuang, Weihua; Bhattacharya, Kankar

doi:10.1109/pesgm.2014.6939530

Cited by 47 publications

(47 citation statements)

References 12 publications

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“…For further research, we intend to carry out more theoretical investigation on related topics (e.g., investigating the impact of EV wireless charging on microgrids [36] and developing techniques for EV optimal battery management [37], [38]). In addition, the design and incorporation of charging stations in the proposed system is another interesting research topic [39]. The experimental or field evaluation of on-road wireless charging system will be conducted when the implementation of wireless charging system becomes mature.…”

Section: Discussionmentioning

confidence: 99%

Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market

Liang

Zhuang

2015

IEEE Trans. Ind. Electron.

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To avoid inconvenient vehicle stops at charging stations, the on-road wireless charging of electric vehicles (EVs) is a promising application in the future smart grid. In this paper, we study a critical yet open problem for this application, i.e., the impact of wireless charging and mobility of EVs on the wholesale electricity market based on locational marginal price (LMP), which is mainly determined by the EV mobility patterns. To capture the dynamics in vehicle traffic flow and state of charge (SOC) of EV batteries, we model the EV mobility as a queueing network based on the statistics obtained via traffic information systems. Then, the load on each power system bus with respect to EV wireless charging is obtained using the stationary distribution of the queueing network. An economic dispatch problem is formulated to incorporate the EV wireless charging demand, and the LMP of each power system bus is obtained. Further, a pricing mechanism based on the LMP variations of power system buses is investigated to enhance the social welfare. The performance of our proposed analytical model is verified by a realistic road traffic simulator (SUMO) based on a 3-bus test system and an IEEE 30-bus test system, respectively. Simulation results indicate that our proposed analytical model can accurately provide an estimation of the LMP variations due to EV wireless charging.

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Section: Discussionmentioning

confidence: 99%

Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market

Liang

Zhuang

2015

IEEE Trans. Ind. Electron.

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“…Stochastic CS design was considered in [28][29][30][31][32][33][34]. In such studies, the system performance is usually measured by the average waiting time or the probability of getting blocked.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [28] presented a spatial and temporal model for the EV charging demand at the CSs based on the traffic flows, where each CS was modeled as an M/M/c queuing system. The study in [29] presented a BCMP queuing model to obtain the EV charging demands based on the stationary distribution of the number of EVs in each CS. The authors in [30] proposed a capacity planning framework for the CSs, which considered the multi-class customers and modeled each CS to be a M/G/c/c queuing system.…”

Section: Related Workmentioning

confidence: 99%

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

et al. 2017

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Abstract:Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.

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“…Moreover, this estimation can be used when utilities need to establish new generation sources [13], to dispatch of current generation sources, and to apply demand side management (DSM) programs [14]; • Single charging facility demand estimation, which is used to estimate the charging profile of a certain charging station or parking lot. Utilities use this type of estimation to assess the impact of certain charging facility-Service providers also use this type of estimation in determination of charging station capacity, such as the numbers of chargers and waiting positions of a new charging facility based on the targeted blocking probability of new customers [15]; • Network of charging station demand estimation, which is used to analyze the interactions among multiple charging stations [16]-utilities and service providers use this estimation to allocate power among charging facilities based on the expected charging profile [17]. Moreover, this estimation is used to allocate (route) customers among charging stations either by direct control or by incentives such as charging price [17].…”

Section: Modelling Of Pev Charging Demandmentioning

confidence: 99%

“…This problem can be formulated, given the customer arrival rate to each station. Modelling the interaction among charging stations is presented in [16], in which the arrivals of PEV users to charging stations vary in response to charging prices. Charging demands of adjacent charging facilities are correlated to each other and rely upon the reaction of PEV users to charging prices.…”

Section: Demand Modeling Of Pev Charging Station Networkmentioning

confidence: 99%

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Abdalrahman

Zhuang

2017

Energies

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Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

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Plug-in electric vehicle charging demand estimation based on queueing network analysis

Cited by 47 publications

References 12 publications

Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market

Investigating Wireless Charging and Mobility of Electric Vehicles on Electricity Market

A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

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