EV/PHEV Bidirectional Charger Assessment for V2G Reactive Power Operation

Kisacikoglu, Mithat C.; Ozpineci, Burak; Tolbert, Leon M.

doi:10.1109/tpel.2013.2251007

Cited by 248 publications

(94 citation statements)

References 22 publications

(32 reference statements)

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“…Many parametric models are based on real power injections [18,19] considering a power factor equal to 1. Without loss of generality, due to regulatory and operational reasons [20], EVs can be requested to provide voltage and reactive power support. In this case, apparent, real and reactive power are modeled using a non-linear function [21] and a fixed power factor angle φ p Di,t , respectively:…”

Section: Pq and Ev Load Modelingmentioning

confidence: 99%

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

2018

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This paper presents a three-phase loss allocation procedure for distribution networks. The key contribution of the paper is the computation of specific marginal loss coefficients (MLCs) per bus and per phase expressly considering non-linear load models for Electric Vehicles (EV).The method was applied in a unbalanced 12.47 kV feeder with 12,780 households and 1000 EVs under peak and off-peak load conditions. Results obtained were also compared with the traditional roll-in embedded allocation procedure (pro rata) using non-linear and standard constant power models. Results show the influence of the non-linear load model in the energy losses allocated. This result highlights the importance of considering an appropriate EV load model to appraise the overall losses encouraging the use and further development of the methodology

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Section: Pq and Ev Load Modelingmentioning

confidence: 99%

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

2018

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“…Application coordination supports not only an efficient integration of renewable energy to the grid power but also inter-EV cooperation for balanced charging. Particularly, trend prediction makes it possible to shift EV charging load, locates the malfunctioning part, and even invites V2G (Vehicle-to-Grid) applications [10].…”

Section: Service Time Analysis For Electric Vehicle Charging Infrastrmentioning

confidence: 99%

Service Time Analysis For Electric Vehicle Charging Infrastructure

Lee¹,

Park²

2018

IJECE

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This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration.

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“…Recent studies have shown that the battery is not affected due to reactive power operation [11]. Reference [12] have discussed the design, control and assessment of onboard charger reactive power support, however the reactive power support by onboard charger is limited by the mobility and power rating. In [13], the authors managed to supply the reactive power compensation service in a fast charging station and proposed a control strategy for the off-board charger.…”

Section: Introductionmentioning

confidence: 99%

Reactive power support of electrical vehicle charging station upgraded with flywheel energy storage system

Sun

Dragičević

Savaghebi

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-Electrical vehicles (EVs) are presenting increasingly potential to replace the conventional fossil fuel based vehicles due to environmental friendly characteristic. Accordingly, Charging Stations (CS), as an intermediate between grid and large numbers of EVs, are supposed to have more critical influence on future smart transportation network. This paper explores an off-board charging station upgraded with flywheel energy storage system that could provide a reactive power support to the grid utility. A supervisory control scheme based on distributed bus signaling is proposed to coordinate the operation of each component in the system. As a result, the charging station could supply the reactive power support to the utility grid without compromising the charging algorithm and preserve the battery's lifetime. Finally, the real-time simulation results based on dSPACE1006 verifies the proposed strategy.

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EV/PHEV Bidirectional Charger Assessment for V2G Reactive Power Operation

Cited by 248 publications

References 22 publications

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration

Service Time Analysis For Electric Vehicle Charging Infrastructure

Reactive power support of electrical vehicle charging station upgraded with flywheel energy storage system

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