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Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 19 Jul 2016Publisher:MDPI AGJournal:Energies, volume 9, page 560 (eissn: 1996-1073,

Authors: Jun Yang; Wanmeng Hao; Lei Chen; Jiejun Chen; Jing Jin; Feng Wang;

doi: 10.3390/en9070560

Risk Assessment of Distribution Networks Considering the Charging-Discharging Behaviors of Electric Vehicles

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Abstract

Electric vehicles (EVs) have received wide attention due to their higher energy efficiency and lower emissions. However, the random charging and discharging behaviors of substantial numbers of EVs may lead to safety risk problems in a distribution network. Reasonable price incentives can guide EVs through orderly charging and discharging, and further provide a feasible solution to reduce the operational risk of the distribution network. Considering three typical electricity prices, EV charging/discharging load models are built. Then, a Probabilistic Load Flow (PLF) method using cumulants and Gram-Charlier series is proposed to obtain the power flow of the distribution network including massive numbers of EVs. In terms of the risk indexes of node voltage and line flow, the operational risk of the distribution network can be estimated in detail. From the simulations of an IEEE-33 bus system and an IEEE 69-bus system, the demonstrated results show that reasonable charging and discharging prices are conducive to reducing the peak-valley difference, and consequently the risks of the distribution network can be decreased to a certain extent.

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Keywords

Technology, vehicle to grid, T, risk assessment, time-of-use price, electric vehicles; charging or discharging load; vehicle to grid; time-of-use price; probabilistic load flow; risk assessment, charging or discharging load, probabilistic load flow, electric vehicles

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	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%