Modeling and prediction of residential service transformer’s demand considering high penetration of electric vehicles

The rapid growth of electric vehicles adoption has introduced new challenges for distribution transformer sizing and thermal aging assessment. Traditional sizing methods based on peak demand and diversity factors fail to capture the dynamic, time-varying nature of residential loads influenced by electric vehicles charging. In this thesis, a data-driven model is developed to predict the transformer’s demand when considering large penetration of electric vehicles, which is then used to estimate the transformer’s economic capacity. The proposed model enables more accurate, economical, and computationally efficient transformer sizing while providing actionable insights to mitigate premature aging risks. The development of such a model will enable safe integration of large penetration of electric vehicles in the residential sector while ensuring economic sizing of transformers and, hence deferring premature replacement.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Aziz, Muhammad Hamdan

Advisor dc:contributor.advisor

• Ibrahim, Walid Morsi

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/2041

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/2041