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Recharge the Future

Recharge the Future seeks to understand the impacts that a large scale EVs deployment will have by investigating EV charging behaviour, forecasting EV uptake and electricity demand at high geospatial resolution, and modelling the associated network reinforcement costs.

Project-on-a-Page summary

Project data

Start date: 02/10/2017
End date: 02/01/2019
Budget: £240,000

The electrification of transport is arguably the single biggest challenge and opportunity facing the electricity networks. The uptake of EVs continues to outstrip forecasts and by 2030 there will be as many as 4.5m EVs in UK Power Networks three licence areas alone. Therefore it is paramount we are ready and prepared for this additional load so that we can be an enabler of the EV revolution. Recharge the Future is our market-leading forecasting tool to enable us to pinpoint with unprecedented accuracy when and where EV uptake will happen.

There are three work streams in the Recharge the Future project that relate to three activities which determine the EV demand characteristics, model the corresponding loading conditions and translate the impact of EV demand on the network into reinforcement costs.

The Charger Use Study activity describes the charging characteristics of EVs at various different charging location types and defines a range of parameters that are required to compute the load from EVs. The Element Energy Load Growth (EELG) model activity calculates load forecasts based on an EV load module which uses all relevant data from the Charger Use Study and other demands (domestic, commercial, industrial, heat pumps, and various generation types). Finally, the Load Related Expenditure (LRE) model activity, computes the reinforcement costs associated with each of the asset-specific load forecasts.

This project includes the most comprehensive analysis of EV charging behaviour to date; encompassing all major GB EV trials, preliminary Electric Nation data, and a public charge point data set from ZapMap. It creates the industry leading EV load forecasting model, with major advancements in accuracy and granularity.

The project aims to improve the understand of EV forecasting and transfer this learning to a tool based on the findings. These revised EV forecasts will be developed to help assess the potential impact on investment required in the medium to long term for UK Power Networks.


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