Local forecasts of electrc vehicles for grid planning purposes
Journal article, 2022

Electrification of passenger vehicles is rapidly becoming the main alternative for decarbonizing transportation. The high power associated with charging of electric vehicles is likely to require actions from grid operators. Using machine learning and GIS analysis we produce forecasts of electric vehicles in very small cells, down to a few hundred meters for Norway. Using a baseline comparison, we find that a random forest model produces the overall lowest error, with a Mean Absolute Error of 14.0, and Mean Absolute Percentage Error of 33.9%. We find that both the existing vehicle fleet, and forecast shows that there is a large variation in electric vehicle adoption between cells. With knowledge where and when electric vehicles are adopted, grid operators can better plan their future investments related to electric vehicle charging, and thereby reduce investment costs.

power grids

electric vehicle charging

electric vehicles

geographic information systems

power engineering computing

random forests

environmental factors

load forecasting

investment

Author

Elias Hartvigsson

Chalmers, Space, Earth and Environment, Energy Technology

Ann Lillieström

Chalmers, Physics, E-commons

David Steen

Chalmers, Electrical Engineering, Electric Power Engineering

Oscar Ivarsson

Chalmers, Physics, E-commons

IET Conference Proceedings

27324494 (eISSN)

Vol. 2022 3 878-882

Driving Forces

Sustainable development

Areas of Advance

Transport

Energy

Subject Categories

Transport Systems and Logistics

Energy Systems

DOI

10.1049/icp.2022.0838

More information

Latest update

4/4/2024 1