Open data for modelling the impacts of electric vehicles on UK distribution networks: Opportunities for a digital spine

Abstract

This paper provides a detailed overview of the current snapshot of available open data for modelling the impacts of electric vehicles (EVs) on the UK distribution network, highlighting opportunities for a digital spine. We are the first to review open data available for UK distribution networks, focusing on spatial data. We also explore data for census small geographies, vehicle ownership, EV charger locations and data on their usage. Several issues are identified, including inconsistencies in dataset availability, file naming conventions, feature definitions and geographic discrepancies. We specifically analyse EV charger connection data for secondary distribution substations from two UK Distribution Network Operators (DNOs). The validity of the data is assessed by comparing it to known public charger locations from OpenChargeMap. While one DNO provides data coverage for >95% of its substations, it is valid for only 24.1% of substations with at least one public charger. Conversely, the other DNO provides data coverage for 1% of its substations due to privacy-related obfuscation, with data valid for 98.3% of substations with at least one public charger. Addressing these challenges through standardised data-sharing practices and implementing a digital spine could enhance the accuracy and reliability of EV-grid integration models. These improvements are essential for facilitating the seamless integration of EVs into the grid and supporting the transition to a sustainable energy system.