Weighting Key Performance Indicators of Smart Local Energy Systems: A Discrete Choice Experiment

Christina Francis, Paul Hansen, Bjarnhedinn Gudlaugsson, David Ingram, R. Camilla Thomson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

The development of Smart Local Energy Systems (SLES) in the UK is part of the energy transition tackling the energy trilemma and contributing to achieving the Sustainable Development Goals (SDGs). Project developers and other stakeholders need to independently assess the performance of these systems: how well they meet their aims to successfully deliver multiple benefits and objectives. This article describes a step undertaken by the EnergyREV Research Consortium in developing a standardised Multi-Criteria Assessment (MCA) tool—specifically a discrete choice experiment (DCE) to determine the weighting of key performance indicators (KPIs). The MCA tool will use a technology-agnostic framework to assess SLES projects, track system performance and monitor benefit realisation. In order to understand the perceived relative importance of KPIs across different stakeholders, seven DCEs were conducted via online surveys (using 1000minds software). The main survey (with 234 responses) revealed that Environment was considered the most important criterion, with a mean weight of 21.6%. This was followed by People and Living (18.9%), Technical Performance (17.8%) and Data Management (14.7%), with Business and Economics and Governance ranked the least important (13.9% and 13.1%, respectively). These results are applied as weightings to calculate overall scores in the EnergyREV MCA-SLES tool.
Original languageEnglish
Article number9305
JournalEnergies
Volume15
Issue number24
Early online date8 Dec 2022
DOIs
Publication statusE-pub ahead of print - 8 Dec 2022

Keywords / Materials (for Non-textual outputs)

  • KPI
  • MCA
  • SLES
  • Smart Local Energy Systems
  • discrete choice experiments
  • key performance indicators
  • multi-criteria assessment

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