Analysing the impact of electric vehicle charging on households: An interrelated load profile generation approach

Electric vehicles (EVs) have drawn considerable attention for their role in energy security and efficiency at the household, building and grid levels. As consumers coordinate indoor activities and outdoor trips, their cohesive activity profiles lead to interrelated residential loads and EV charging loads. However, these two types of loads are often generated separately, neglecting the connection between them. This separate EV load generation approach can lead to inaccurate estimations of peak loads and demand flexibility at the household, building and residential grid levels. This study develops a bottom-up approach to simulate interrelated load profiles including the EV charging load and the loads from indoor household appliances. This approach extracts both travel activities and indoor appliance-related activities from the same individuals’ time use surveys to maintain the interconnection between them. Conversion factors, such as appliance load patterns, travel patterns, and EV charging rates, are derived from diverse data sources to translate household activities into load profiles. The output not only provides an accurate estimation of peak loads but also facilitates demand flexibility management for households and residential buildings. In a dataset comprising about 200 households, our load profile simulation indicates that a 100% EV penetration rate may increase the total electricity consumption by 12.9% to 19.0% across three charging scenarios, while peak load increases the range from 14.9% to 35.0%. Furthermore, simple rule-based EV charging power rate limitations are shown to mitigate peak load surges without significantly influencing EV usage. The validation against meter-recorded electricity measurements reveals that the generated household appliance loads capture the fluctuation of the average daily load profiles. This study presents a straightforward method for generating interrelated EV charging loads and indoor household appliance loads, offering valuable inputs and insights for impact analysis, policy-making and demand response.

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Publication Details

Journal
Energy and Buildings
Published
2025-03-04
DOI
https://doi.org/10.1016/j.enbuild.2025.115558
Citations
10
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
7.30
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article

Analysing the impact of electric vehicle charging on households: An interrelated load profile generation approach

Aruna Sivakumar, Han Wang, Fangce Guo
10 citations
Energy and Buildings
Electric Vehicles and Infrastructure
7.30
article

Analysing the impact of electric vehicle charging on households: An interrelated load profile generation approach

Aruna Sivakumar, Han Wang, Fangce Guo
article en
10 citations

Abstract

Electric vehicles (EVs) have drawn considerable attention for their role in energy security and efficiency at the household, building and grid levels. As consumers coordinate indoor activities and outdoor trips, their cohesive activity profiles lead to interrelated residential loads and EV charging loads. However, these two types of loads are often generated separately, neglecting the connection between them. This separate EV load generation approach can lead to inaccurate estimations of peak loads and demand flexibility at the household, building and residential grid levels. This study develops a bottom-up approach to simulate interrelated load profiles including the EV charging load and the loads from indoor household appliances. This approach extracts both travel activities and indoor appliance-related activities from the same individuals’ time use surveys to maintain the interconnection between them. Conversion factors, such as appliance load patterns, travel patterns, and EV charging rates, are derived from diverse data sources to translate household activities into load profiles. The output not only provides an accurate estimation of peak loads but also facilitates demand flexibility management for households and residential buildings. In a dataset comprising about 200 households, our load profile simulation indicates that a 100% EV penetration rate may increase the total electricity consumption by 12.9% to 19.0% across three charging scenarios, while peak load increases the range from 14.9% to 35.0%. Furthermore, simple rule-based EV charging power rate limitations are shown to mitigate peak load surges without significantly influencing EV usage. The validation against meter-recorded electricity measurements reveals that the generated household appliance loads capture the fluctuation of the average daily load profiles. This study presents a straightforward method for generating interrelated EV charging loads and indoor household appliance loads, offering valuable inputs and insights for impact analysis, policy-making and demand response.

Energy and BuildingsVol. 335
Transport for London (GB), Imperial College London (GB)
Openalex Percentile: Top 3%
Electric Vehicles and Infrastructure
7.30
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