Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics

This paper presents a stochastic model to quantify the impact of the electric vehicle (EV) on China’s electricity load profiles. Most of the existing literature utilized travel data to model EV charging behavior and ignored the influence of people’s social attributes that are significant for the acc...

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Autores principales:	Bo Li, Minyou Chen, Daniel M. Kammen, Wenfa Kang, Xiao Qian, Leiqi Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Electric vehicles EV charging load Typical load profiles Monte Carlo China Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/231035d6f2b8448fac50be518d545c82
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id	oai:doaj.org-article:231035d6f2b8448fac50be518d545c82
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spelling	oai:doaj.org-article:231035d6f2b8448fac50be518d545c822021-12-04T04:34:45ZElectric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics2352-484710.1016/j.egyr.2021.11.003https://doaj.org/article/231035d6f2b8448fac50be518d545c822022-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011471https://doaj.org/toc/2352-4847This paper presents a stochastic model to quantify the impact of the electric vehicle (EV) on China’s electricity load profiles. Most of the existing literature utilized travel data to model EV charging behavior and ignored the influence of people’s social attributes that are significant for the accuracy of EV charging behavior. Based on the dataset of the national household travel survey, the most significant influencing factors, e.g. age, location, and weekday/weekend, are identified. Markov-chain is used to construct a sequence of destinations of each vehicle trip, depending on EV’s driver, day of the week, and time of day. Vehicle-driven distance, driving time, and parking duration are used to model electricity demand and potential EV charging flexibility. The charging infrastructure accessibility in a certain parking location has an influence on EV charging decisions. The model’s outputs are used to assess the impacts of various EV charging strategies on electricity load profiles on a national scale. It is found that at 60% gasoline vehicle replacement with EVs by 2050, the electricity demand of EV will be 510 TWh, accounting for 4.5% of the national demand in 2050. The national peak loads will further increase by 8.2% under the unmanaged charging strategy of EV. In contrast, implying last-minute charging strategy only increases peak demand by 2.6% relative to the unmanaged charging strategy.Bo LiMinyou ChenDaniel M. KammenWenfa KangXiao QianLeiqi ZhangElsevierarticleElectric vehiclesEV charging loadTypical load profilesMonte CarloChinaElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 8, Iss , Pp 26-35 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electric vehicles EV charging load Typical load profiles Monte Carlo China Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electric vehicles EV charging load Typical load profiles Monte Carlo China Electrical engineering. Electronics. Nuclear engineering TK1-9971 Bo Li Minyou Chen Daniel M. Kammen Wenfa Kang Xiao Qian Leiqi Zhang Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
description	This paper presents a stochastic model to quantify the impact of the electric vehicle (EV) on China’s electricity load profiles. Most of the existing literature utilized travel data to model EV charging behavior and ignored the influence of people’s social attributes that are significant for the accuracy of EV charging behavior. Based on the dataset of the national household travel survey, the most significant influencing factors, e.g. age, location, and weekday/weekend, are identified. Markov-chain is used to construct a sequence of destinations of each vehicle trip, depending on EV’s driver, day of the week, and time of day. Vehicle-driven distance, driving time, and parking duration are used to model electricity demand and potential EV charging flexibility. The charging infrastructure accessibility in a certain parking location has an influence on EV charging decisions. The model’s outputs are used to assess the impacts of various EV charging strategies on electricity load profiles on a national scale. It is found that at 60% gasoline vehicle replacement with EVs by 2050, the electricity demand of EV will be 510 TWh, accounting for 4.5% of the national demand in 2050. The national peak loads will further increase by 8.2% under the unmanaged charging strategy of EV. In contrast, implying last-minute charging strategy only increases peak demand by 2.6% relative to the unmanaged charging strategy.
format	article
author	Bo Li Minyou Chen Daniel M. Kammen Wenfa Kang Xiao Qian Leiqi Zhang
author_facet	Bo Li Minyou Chen Daniel M. Kammen Wenfa Kang Xiao Qian Leiqi Zhang
author_sort	Bo Li
title	Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
title_short	Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
title_full	Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
title_fullStr	Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
title_full_unstemmed	Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics
title_sort	electric vehicle’s impacts on china’s electricity load profiles based on driving patterns and demographics
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/231035d6f2b8448fac50be518d545c82
work_keys_str_mv	AT boli electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics AT minyouchen electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics AT danielmkammen electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics AT wenfakang electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics AT xiaoqian electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics AT leiqizhang electricvehiclesimpactsonchinaselectricityloadprofilesbasedondrivingpatternsanddemographics
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Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographics

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