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

Christina Francis, Paul Hansen, Bjarnhéðinn Guðlaugsson, David M. Ingram and R. Camilla Thomson ()
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Christina Francis: School of Engineering, The University of Edinburgh, Colin Maclaurin Road, Edinburgh EH9 3DW, UK
Paul Hansen: Department of Economics, University of Otago, Dunedin 9054, New Zealand
Bjarnhéðinn Guðlaugsson: School of Engineering, The University of Edinburgh, Colin Maclaurin Road, Edinburgh EH9 3DW, UK
David M. Ingram: School of Engineering, The University of Edinburgh, Colin Maclaurin Road, Edinburgh EH9 3DW, UK
R. Camilla Thomson: School of Engineering, The University of Edinburgh, Colin Maclaurin Road, Edinburgh EH9 3DW, UK

Energies, 2022, vol. 15, issue 24, 1-17

Abstract: 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.

Keywords: multi-criteria assessment; MCA; key performance indicators; KPI; Smart Local Energy Systems; SLES; discrete choice experiments (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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