Spatial and Temporal Model of Electric Vehicle Charging Demand

Bae, Sungwoo; Kwasinski, Alexis

doi:10.1109/tsg.2011.2159278

Cited by 404 publications

(161 citation statements)

References 24 publications

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“…Also, it focused primarily on determining whether the existing or planned generation capacity will be sufficient to supply enough power to meet demand result by EV charging [1][2][3][4]. Similar studies are found in [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 94%

A Study on Voltage Sag Considering Real-Time Traffic Volume of Electric Vehicles in South Korea

Go¹,

Kim²,

Kim³

et al. 2015

Journal of Electrical Engineering and Technology

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-This paper analyzes the effect of voltage sag on distribution systems due to the connection of Electric Vehicles (EVs). In order to study the impact of the voltage sag on the power system, two scenarios have been selected in this paper. The distribution system and EVs are modeled using the Electro Magnetic Transients Program (EMTP). The numbers of EVs are predicted based on the number of vehicles in distribution system of Seoul. In addition, the number of EVs is set up using realtime traffic in Seoul to simulate Scenario I and II. The simulation results show that voltage sag can occur if the distribution system has more than 30% of the total number of vehicles.

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Section: Introductionmentioning

confidence: 94%

A Study on Voltage Sag Considering Real-Time Traffic Volume of Electric Vehicles in South Korea

Go¹,

Kim²,

Kim³

et al. 2015

Journal of Electrical Engineering and Technology

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“…We will provide a more indepth analysis and identify different types of charging behavior (with different characteristics in terms of both arrival/departure times and flexibility). We further note that our data covers residential charging as well as public charging at roadside parking spots (hence, our work is complementary to fast charging station models along highways, e.g., the fluid traffic model as presented in [10]). …”

Section: Related Workmentioning

confidence: 99%

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

Develder

Sadeghianpourhamami

Strobbe

et al. 2016

2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources).This paper analyzes two data sets, one from a charging-athome field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility:(1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + ∆.

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“…J. Liu [3] established an evaluation model of impact of charging stations distribution in Beijing on power grid, pointing out that the small the charging station service scope is, the small the charging station interference on the power grid is. S. Bae [4] proposed 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 queuing theory.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When α= 0.5, Z=-(Z 1 +Z 2 )=0.5S 3 +1.5S 4 . Starting point A and four destinations B,C,D,E are randomly generated within the Fifth Ring Road in Beijing and the data is shown in Table 2.…”

Section: Model Validationmentioning

confidence: 99%

Analysis of Charging Stations' Layout based on the Study about Electric Vehicle Routine Optimizing

Chen²,

Jiaotong³

2014

IJCA

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Spatial and Temporal Model of Electric Vehicle Charging Demand

Cited by 404 publications

References 24 publications

A Study on Voltage Sag Considering Real-Time Traffic Volume of Electric Vehicles in South Korea

A Study on Voltage Sag Considering Real-Time Traffic Volume of Electric Vehicles in South Korea

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

Analysis of Charging Stations' Layout based on the Study about Electric Vehicle Routine Optimizing

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