The effect of different charging strategies on EV load frequency control

Wei, Wenxiao; Guo, Xijuan; Li, Peng; Jian, Ganyang; Zhan, Kaiqiao; Tan, Qinxue; Meng, Jian; Jin, Xiaolong

doi:10.1109/icsgce.2016.7876045

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…If there are well-designed incentives for EV users to take part in grid-support, the value of driving an EV and having EVs in the electrical system increases. The opportunity of using EVs as grid ancillary services was studied in [18][19][20][21][22]. While modeling approaches in the literature focus on modeling average hourly power profiles, modeling the demand-side management and demand response events requires minutes or even seconds resolution.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data

Uimonen

Lehtonen

2020

Energies

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Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.

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