EV Charging Demand Estimation in Residential Areas: Workday Commute Case Studies

The rapid increasing of electric vehicles in suburbs residential areas poses a significant challenge for low-voltage grids due to uncoordinated charging demands, potentially exceeding transformer capacity. This paper presents a data-driven framework for EV power demand forecasting, leveraging dynami...

Full description

Saved in:
Bibliographic Details
Main Authors: Vorapojpisut, Supachai, Panyasai, Tharnthip, Palakawongnaayuttaya, Jakkarin
Format: Conference Proceeding
Language:English
Subjects:
data-driven framework
Electric vehicle charging
EV charger
Power demand
Predictive models
regression model
Soft sensors
Telecommunications
Transformers
Web services
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapid increasing of electric vehicles in suburbs residential areas poses a significant challenge for low-voltage grids due to uncoordinated charging demands, potentially exceeding transformer capacity. This paper presents a data-driven framework for EV power demand forecasting, leveraging dynamic travel patterns and personalized driving data to overcome limitations of existing statistical models. Implemented as a MATLAB-based simulation software, the software integrates diverse data sources like Google Maps API and OpenWeather API to predict charging events with granularity. Numerical studies based on workday commute scenarios between suburbs and capital areas reveal that uncoordinated charging can lead to peak demands exceeding transformer capacity.
ISSN:2837-6471
DOI:10.1109/ECTI-CON60892.2024.10594903