Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

TLDR

The paper introduces common EV battery types and charging characteristics before developing an analytical solution to predict EV charging load. The study presents a methodology to model and analyze EV battery charging load demand in distribution systems. The authors develop a stochastic model that incorporates random charging start times and state‑of‑charge, evaluate four charging scenarios—uncontrolled domestic, off‑peak, smart, and public commuter charging—within a UK distribution system using lead‑acid and lithium‑ion battery data, and assess impacts under future tariff changes. The model predicts that 10 % EV penetration raises daily peak demand by up to 17.9 %, and 20 % penetration increases peak load by 35.8 % under uncontrolled domestic charging.

Abstract

This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, "smart" domestic charging and uncontrolled public charging-commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging-the "worst-case" scenario.