NIA SSEN 0063: VFES - Vulnerability Future Energy Scenarios

Key activities

VFES is a vulnerability-based project with aims of benefiting consumers in vulnerable situations by predicting the scale and location of such situations as well as what new situations may cause vulnerability.

We are seeing a significant uptake of low carbon technologies (LCT) on our networks, typically from solar panels, electric vehicles and heat pumps, and is taking steps to manage the network to accommodate that increased electricity demand cost effectively. But the transition to new technologies carries a risk for more vulnerable customers who may be unwilling or unable to make the switch. SSEN is working to identify consumers in vulnerable positions and forecast how those communities and their needs may change, in the transition to net zero. In the face of the increasing costs of fossil fuels, it has never been more important to ensure that everyone who wishes to adopt clean, electrified technologies, can do so.

The Vulnerability Future Energy Scenarios (VFES) project will build on the annual publication of SSEN’s Distribution Future Energy Scenarios (DFES), which are scenario forecasts of future energy supply and demand that help SSEN to understand how customers’ use of the network is likely to change. The VFES by comparison, will focus on customers and communities and deploy foresighting, machine learning and expert validation to test whether a reliable forecast of vulnerability trends can be developed. The aim is to support better-informed operational practices and investment planning which in future will be able to take vulnerable communities into account.

Objective(s)

The objectives of the VFES project are to explore how the use of new foresighting techniques, along with data analytics and expert validation can be used to identity and forecast consumers in vulnerable situations as we move toward net zero.
The project will produce a report detailing how far, and how accurately, foresighting and machine learning can predict network
requirements based on customer, community, and wider societal factors.

Expected outcomes

A combined report detailing how far, and how accurately, foresighting, machine learning, and expert validation/stakeholder engagement can predict network requirements based on customer and community factors.

Funding

NIA £144,000

Start/end date

August 2022 – March 2024

Project Manager

Simon O’Loughlin