A probability load modeling method for the charging demand of large-scale PEVs accounting users’ charging willingness

Hourly charging profiles for electric vehicles and their effect on the aggregated consumption profile in Denmark

“…However, comparing the profiles in the Figures 8 and 9 with the profile in Figure 7, consumption by EVs mainly prolong the evening peak adding to the consumption during the evening and nights, especially during the winter. Comparing to a study for Manitoba [29] simulating up to 40% penetration of EV they find that the aggregate peak for winter workdays will be shifted from 6 to 7 pm, but their study has different profiles for working days and identify Thursday and particular Friday as the peak days. Finally, comparing the home charge scenario to the sensitivity scenario where 25% of the EVs are dumb charged after work, the dumb charge scenario adds considerably to the peak consumption in 2040, but the effect is limited in 2030.…”

Cost‐based optimal siting and sizing of electric vehicle charging stations considering demand response programmes

“…While existing researches have mainly put their emphasis on economic dimensions and transportation sector, few researches are available to study the load demand due to considerable EV charging behaviours and their influence over load characteristics of distribution network. A strategy to model the charging power demand on a residential power distribution system has been proposed in [27]. This strategy can ensure a high utilisation of the battery capacity, and the aggregated charging demand resulted is more rational and credible.…”

Use of EV hosting capacity for management of low‐voltage distribution systems

“…The resulting charging profiles indicate that EV charging start time is after 10 a.m., mostly after 6 p.m. Only residential charging is considered. Xu et al [14] also provide a detailed stochastic model for EV charging behaviour and, in addition to the aspects above, it considers the different charging frequencies among users. However, its charging profiles have only an hourly resolution.…”

“…On the one hand, some methods are oversimplified and do not capture the realistic behaviour of charging impacts. For example, the only random variable used to model EV charging in [9,10], is charging duration, while works such as [11][12][13][14][15] introduced charging start time as a stochastic variable. Ul-Haq et al [11] consider a very detailed EV charging model, which accounts for various factors such as battery capacity, state-of-charge (SOC), trip purpose etc.…”

“…Xu et al . [14] also provide a detailed stochastic model for EV charging behaviour and, in addition to the aspects above, it considers the different charging frequencies among users. However, its charging profiles have only an hourly resolution.…”