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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-04-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721011471 |
| _version_ | 1818272570743455744 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-12T21:44:10Z |
| format | Article |
| id | doaj.art-231035d6f2b8448fac50be518d545c82 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-12T21:44:10Z |
| publishDate | 2022-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-231035d6f2b8448fac50be518d545c822022-12-22T00:10:57ZengElsevierEnergy Reports2352-48472022-04-0182635Electric vehicle’s impacts on China’s electricity load profiles based on driving patterns and demographicsBo Li0Minyou Chen1Daniel M. Kammen2Wenfa Kang3Xiao Qian4Leiqi Zhang5School of Electrical Engineering, Chongqing University, Chongqing, 400030, China; Renewable and Appropriate Energy Laboratory, University of California, Berkeley, CA 94720, USASchool of Electrical Engineering, Chongqing University, Chongqing, 400030, China; Corresponding authors.Renewable and Appropriate Energy Laboratory, University of California, Berkeley, CA 94720, USA; Corresponding authors.School of Electrical Engineering, Chongqing University, Chongqing, 400030, ChinaState Grid Zhejiang Electric Power Company, Hangzhou, ChinaState Grid Zhejiang Electric Power Company, Hangzhou, ChinaThis 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.http://www.sciencedirect.com/science/article/pii/S2352484721011471Electric vehiclesEV charging loadTypical load profilesMonte CarloChina |
| spellingShingle | 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 Energy Reports Electric vehicles EV charging load Typical load profiles Monte Carlo China |
| title | 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_short | 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 |
| topic | Electric vehicles EV charging load Typical load profiles Monte Carlo China |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721011471 |
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