Electric vehicle's impacts on China's electricity load profiles based on driving patterns and demographics. (April 2022)

• Record Type: Journal Article
• Authors: Li, Bo; Chen, Minyou; Kammen, Daniel M.; Kang, Wenfa; Qian, Xiao; Zhang, Leiqi
• Is Part Of: Energy reports. Volume 8(2022)Supplement 1
• Page Start: 26
• Page End: 35
• Publication Date: 2022-04
• Subjects: Electric vehicles -- EV charging load -- Typical load profiles -- Monte Carlo -- China; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.11.003
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: 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.