IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i24p9305-d997399.html
   My bibliography  Save this article

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

Author

Listed:
  • Christina Francis

    (School of Engineering, The University of Edinburgh, Colin Maclaurin Road, Edinburgh EH9 3DW, UK
    Current address: School of the Built Environment and Architecture, London South Bank University, 103 Borough Road, London SE1 0AA, 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)

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.

Suggested Citation

  • Christina Francis & Paul Hansen & Bjarnhéðinn Guðlaugsson & David M. Ingram & R. Camilla Thomson, 2022. "Weighting Key Performance Indicators of Smart Local Energy Systems: A Discrete Choice Experiment," Energies, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9305-:d:997399
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/24/9305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/24/9305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tobias Naegler & Lisa Becker & Jens Buchgeister & Wolfgang Hauser & Heidi Hottenroth & Tobias Junne & Ulrike Lehr & Oliver Scheel & Ricarda Schmidt-Scheele & Sonja Simon & Claudia Sutardhio & Ingela T, 2021. "Integrated Multidimensional Sustainability Assessment of Energy System Transformation Pathways," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    2. Ma, Weiwu & Xue, Xinpei & Liu, Gang, 2018. "Techno-economic evaluation for hybrid renewable energy system: Application and merits," Energy, Elsevier, vol. 159(C), pages 385-409.
    3. Hajkowicz, Stefan & Higgins, Andrew, 2008. "A comparison of multiple criteria analysis techniques for water resource management," European Journal of Operational Research, Elsevier, vol. 184(1), pages 255-265, January.
    4. Azarova, Valeriya & Cohen, Jed & Friedl, Christina & Reichl, Johannes, 2019. "Designing local renewable energy communities to increase social acceptance: Evidence from a choice experiment in Austria, Germany, Italy, and Switzerland," Energy Policy, Elsevier, vol. 132(C), pages 1176-1183.
    5. Kaya, Tolga & Kahraman, Cengiz, 2010. "Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul," Energy, Elsevier, vol. 35(6), pages 2517-2527.
    6. Hottenroth, H. & Sutardhio, C. & Weidlich, A. & Tietze, I. & Simon, S. & Hauser, W. & Naegler, T. & Becker, L. & Buchgeister, J. & Junne, T. & Lehr, U. & Scheel, O. & Schmidt-Scheele, R. & Ulrich, P. , 2022. "Beyond climate change. Multi-attribute decision making for a sustainability assessment of energy system transformation pathways," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Heo, Eunnyeong & Kim, Jinsoo & Boo, Kyung-Jin, 2010. "Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(8), pages 2214-2220, October.
    8. Gallego Carrera, Diana & Mack, Alexander, 2010. "Sustainability assessment of energy technologies via social indicators: Results of a survey among European energy experts," Energy Policy, Elsevier, vol. 38(2), pages 1030-1039, February.
    9. N. P. Ravindra Deyshappriya & Simon Feeny, 2021. "Weighting the Dimensions of the Multidimensional Poverty Index: Findings from Sri Lanka," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 1-19, July.
    10. Ananda, Jayanath & Herath, Gamini, 2009. "A critical review of multi-criteria decision making methods with special reference to forest management and planning," Ecological Economics, Elsevier, vol. 68(10), pages 2535-2548, August.
    11. Paul E. Green & Abba M. Krieger & Yoram Wind, 2001. "Thirty Years of Conjoint Analysis: Reflections and Prospects," Interfaces, INFORMS, vol. 31(3_supplem), pages 56-73, June.
    12. Zanakis, Stelios H. & Solomon, Anthony & Wishart, Nicole & Dublish, Sandipa, 1998. "Multi-attribute decision making: A simulation comparison of select methods," European Journal of Operational Research, Elsevier, vol. 107(3), pages 507-529, June.
    13. Yi, Liqi & Li, Tao & Zhang, Ting, 2021. "Optimal investment selection of regional integrated energy system under multiple strategic objectives portfolio," Energy, Elsevier, vol. 218(C).
    14. Van Oijstaeijen, Wito & Van Passel, Steven & Back, Phil & Cools, Jan, 2022. "The politics of green infrastructure: A discrete choice experiment with Flemish local decision-makers," Ecological Economics, Elsevier, vol. 199(C).
    15. Krey, Volker & Guo, Fei & Kolp, Peter & Zhou, Wenji & Schaeffer, Roberto & Awasthy, Aayushi & Bertram, Christoph & de Boer, Harmen-Sytze & Fragkos, Panagiotis & Fujimori, Shinichiro & He, Chenmin & Iy, 2019. "Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models," Energy, Elsevier, vol. 172(C), pages 1254-1267.
    16. Rae, Callum & Kerr, Sandy & Maroto-Valer, M. Mercedes, 2020. "Upscaling smart local energy systems: A review of technical barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    17. Ecaterina Resniova & Tatiana Ponomarenko, 2021. "Sustainable Development of the Energy Sector in a Country Deficient in Mineral Resources: The Case of the Republic of Moldova," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    18. Schleich, Joachim & Tu, Gengyang & Faure, Corinne & Guetlein, Marie-Charlotte, 2021. "Would you prefer to rent rather than own your new heating system? Insights from a discrete choice experiment among owner-occupiers in the UK," Energy Policy, Elsevier, vol. 158(C).
    19. Chen, Qiu, 2021. "District or distributed space heating in rural residential sector? Empirical evidence from a discrete choice experiment in South China," Energy Policy, Elsevier, vol. 148(PA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Gang, 2014. "Development of a general sustainability indicator for renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 611-621.
    2. Ishizaka, Alessio & Siraj, Sajid & Nemery, Philippe, 2016. "Which energy mix for the UK (United Kingdom)? An evolutive descriptive mapping with the integrated GAIA (graphical analysis for interactive aid)–AHP (analytic hierarchy process) visualization tool," Energy, Elsevier, vol. 95(C), pages 602-611.
    3. Mulliner, Emma & Smallbone, Kieran & Maliene, Vida, 2013. "An assessment of sustainable housing affordability using a multiple criteria decision making method," Omega, Elsevier, vol. 41(2), pages 270-279.
    4. Deveci, Muhammet & Cali, Umit & Kucuksari, Sadik & Erdogan, Nuh, 2020. "Interval type-2 fuzzy sets based multi-criteria decision-making model for offshore wind farm development in Ireland," Energy, Elsevier, vol. 198(C).
    5. Nasrollahi, Sadaf & Kazemi, Aliyeh & Jahangir, Mohammad-Hossein & Aryaee, Sara, 2023. "Selecting suitable wave energy technology for sustainable development, an MCDM approach," Renewable Energy, Elsevier, vol. 202(C), pages 756-772.
    6. Aikaterini Papapostolou & Charikleia Karakosta & Kalliopi-Anastasia Kourti & Haris Doukas & John Psarras, 2019. "Supporting Europe’s Energy Policy Towards a Decarbonised Energy System: A Comparative Assessment," Sustainability, MDPI, vol. 11(15), pages 1-26, July.
    7. Milad Kolagar & Seyed Mohammad Hassan Hosseini & Ramin Felegari & Parviz Fattahi, 2020. "Policy-making for renewable energy sources in search of sustainable development: a hybrid DEA-FBWM approach," Environment Systems and Decisions, Springer, vol. 40(4), pages 485-509, December.
    8. Karatas, Mumtaz & Sulukan, Egemen & Karacan, Ilknur, 2018. "Assessment of Turkey's energy management performance via a hybrid multi-criteria decision-making methodology," Energy, Elsevier, vol. 153(C), pages 890-912.
    9. Wu, Yunna & Xu, Chuanbo & Zhang, Ting, 2018. "Evaluation of renewable power sources using a fuzzy MCDM based on cumulative prospect theory: A case in China," Energy, Elsevier, vol. 147(C), pages 1227-1239.
    10. Mumtaz Karatas, 2017. "Multiattribute Decision Making Using Multiperiod Probabilistic Weighted Fuzzy Axiomatic Design," Systems Engineering, John Wiley & Sons, vol. 20(4), pages 318-334, July.
    11. Francesco Ciardiello & Andrea Genovese, 2023. "A comparison between TOPSIS and SAW methods," Annals of Operations Research, Springer, vol. 325(2), pages 967-994, June.
    12. Ilbahar, Esra & Kahraman, Cengiz & Cebi, Selcuk, 2022. "Risk assessment of renewable energy investments: A modified failure mode and effect analysis based on prospect theory and intuitionistic fuzzy AHP," Energy, Elsevier, vol. 239(PA).
    13. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Khalifah, Zainab & Zakuan, Norhayati & Jusoh, Ahmad & Nor, Khalil Md & Khoshnoudi, Masoumeh, 2017. "A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 216-256.
    14. Štreimikienė, Dalia & Šliogerienė, Jūratė & Turskis, Zenonas, 2016. "Multi-criteria analysis of electricity generation technologies in Lithuania," Renewable Energy, Elsevier, vol. 85(C), pages 148-156.
    15. Roszkowska Ewa & Wachowicz Tomasz, 2019. "The Impact of Decision-Making Profiles on the Consistency of Rankings Obtained by Means of Selected Multiple Criteria Decision-Aiding Methods," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 1-14, June.
    16. Mousavi, M. & Gitinavard, H. & Mousavi, S.M., 2017. "A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 774-787.
    17. Thomas L. Saaty & Daji Ergu, 2015. "When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1171-1187, November.
    18. Couraud, Benoit & Andoni, Merlinda & Robu, Valentin & Norbu, Sonam & Chen, Si & Flynn, David, 2023. "Responsive FLEXibility: A smart local energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    19. Paweł Ziemba, 2019. "Inter-Criteria Dependencies-Based Decision Support in the Sustainable wind Energy Management," Energies, MDPI, vol. 12(4), pages 1-29, February.
    20. Strantzali, Eleni & Aravossis, Konstantinos, 2016. "Decision making in renewable energy investments: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 885-898.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9305-:d:997399. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.