Evaluating the potential of solar PV to reduce energy costs in fuel poor households

Paul McKenzie, David Gawley

Research output: Contribution to journalArticlepeer-review

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Abstract

Energy costs represent a significant proportion of household incomes and contribute to fuel poverty. Energy demand also contributes to carbon emissions which must decrease to meet net zero targets. Low Carbon Technologies (LCTs), such as solar photovoltaics (PV), offer opportunities to reduce household energy costs and emissions. However, many LCTs have strict spatial requirements that must be considered to identify buildings that can maximise benefits. This study used bespoke and transferable high-resolution solar PV models alongside government retrofit data to assess solar PV’s potential to reduce energy costs in a sample of fuel poor homes. Results showed that households can reduce electricity expenditure by an average of 26% and a maximum of over 41%. Pen Portraits revealed that savings could reduce household expenditure by up to 43%, thus reducing risk of fuel poverty. When combined with energy efficiency measures already installed, solar PV can reduce household expenditure and carbon emissions. Transferable spatial approaches can be used to lower technical barriers to adoption of LCTs while also proposing areas to promote sustainable practices and behaviours. Spatially targeted policies can then be used to allocate budgets, equipment, information and support structures to maximise self-consumption and adoption of renewable technologies.
Original languageEnglish
Article number124487
Pages (from-to)1-11
Number of pages11
JournalRenewable Energy
Volume256
Early online date23 Sept 2025
DOIs
Publication statusPublished online - 23 Sept 2025

Bibliographical note

Publisher Copyright:
© 2025 The Authors

Data Access Statement

All public data sources are referenced in the article. Data from the Affordable Warmth Scheme (AWS) are confidential.

Keywords

  • Solar PV
  • GIS (geographical information systems)
  • Fuel poverty
  • Retrofit
  • Renewable technologies
  • Area-based targeting
  • GIS
  • Renewable technology

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