Driving change: Electric vehicle charging behavior and peak loading

Type of content
Journal Article
Thesis discipline
Degree name
Elsevier BV
Journal Title
Journal ISSN
Volume Title
Williams B
Bishop, Daniel
Hooper G
Chase, Geoff

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Williams B, Bishop D, Hooper G, Chase JG (2024). Driving change: Electric vehicle charging behavior and peak loading. Renewable and Sustainable Energy Reviews. 189. 113953-113953.
electric vehicle, charging demand, agent-based modeling, user behaviour, Monte Carlo modeling, electricity demand
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
40 - Engineering::4008 - Electrical engineering::400805 - Electrical energy transmission, networks and systems
40 - Engineering::4002 - Automotive engineering::400205 - Hybrid and electric vehicles and powertrains
40 - Engineering::4005 - Civil engineering::400512 - Transport engineering
40 - Engineering::4005 - Civil engineering::400508 - Infrastructure engineering and asset management
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).