Mar 2019
Electricity Distribution
Reflect Uncertainties around E-Vehicle Charging to Optimize Network Forecasting
ENWL 022
Live
Mar 2019
Mar 2021
Electricity North West Limited
Innovation Team
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Network Innovation Allowance
ED - Transition to low carbon future
Electric Vehicles
£192,500.00
The expected widespread adoption of EVs is a significant challenge for distribution network operators around the world, as they are expected to plan their networks to facilitate the demand growth followed by the electrification of transport. The extent of the required load related reinforcements to accommodate electric vehicle charging strongly depends not only on the future volumes of private and commercial electric vehicles, but also on the location and capacity of the charging adopted. For this purpose, the Reflect project introduces a demand forecasting framework that takes into account the effects from the uncertainties around slow and fast charging. These uncertainties could be related not only around the correlation of the traffic flows and location of existing fuel and service stations to understand the effects on networks from fast charging, but also the associated interactions with slow charging.
The Reflect project will develop the forecasting methodologies to model the uncertainties around slow EV charging from the LV networks (e.g. home and destination charging) versus ultra fast parking (e.g. at service stations).

This project supports the following primary objectives:
·         develop methodologies and tools that consider regional characteristics to frame uncertainties around slow and ultra fast charging;
·         introduce the use of probabilistic assessments within the scenario-based forecasting approaches followed by DNOs;
·         consideration of traffic flow data in modeling;
·         interoperability with EV charging profiles produced by analyses and trials from other UK and European projects (e.g. UKPN’s Recharge the Future and WPD’s CarConnect projects).
Using modeling of uncertainties around EV charging in demand forecasting to produce well informed risk and cost assessments to examine different load related reinforcement and asset replacement interventions. This modeling can lead to cost efficient and risk averse interventions regarding both traditional network options and innovative solutions (e.g. DSR, flexible services etc).