IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v116y2018icp95-111.html
   My bibliography  Save this article

Feed-in tariffs for solar microgeneration: Policy evaluation and capacity projections using a realistic agent-based model

Author

Listed:
  • Pearce, Phoebe
  • Slade, Raphael

Abstract

Since 2010, over 700,000 small-scale solar photovoltaic (PV) systems have been installed by households in Great Britain and registered under the feed-in tariff (FiT) scheme. This paper introduces a new agent-based model which simulates this adoption by considering decision-making of individual households based on household income, social network, total capital cost of the PV system, and the payback period of the investment, where the final factor takes into account the economic effect of FiTs. After calibration using Approximate Bayesian Computation, the model successfully simulates observed cumulative and average capacity installed over the period 2010–2016 using historically accurate FiTs; setting different tariffs allows investigation of alternative policy scenarios. Model results show that using simple cost control measures, more installation by October 2016 could have been achieved at lower subsidy cost. The total cost of supporting capacity installed during the period 2010–2016, totalling 2.4 GW, is predicted to be £14 billion, and costs to consumers significantly exceed predictions. The model is further used to project capacity installed up to 2022 for several PV cost, electricity price, and FiT policy scenarios, showing that current tariffs are too low to significantly impact adoption, and falling PV costs are the most important driver of installation.

Suggested Citation

  • Pearce, Phoebe & Slade, Raphael, 2018. "Feed-in tariffs for solar microgeneration: Policy evaluation and capacity projections using a realistic agent-based model," Energy Policy, Elsevier, vol. 116(C), pages 95-111.
  • Handle: RePEc:eee:enepol:v:116:y:2018:i:c:p:95-111
    DOI: 10.1016/j.enpol.2018.01.060
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421518300697
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Scarpa, Riccardo & Willis, Ken, 2010. "Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies," Energy Economics, Elsevier, vol. 32(1), pages 129-136, January.
    2. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    3. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Grubler, Arnulf, 2012. "Energy transitions research: Insights and cautionary tales," Energy Policy, Elsevier, vol. 50(C), pages 8-16.
    6. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    7. Richard Loulou & Maryse Labriet, 2008. "ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure," Computational Management Science, Springer, vol. 5(1), pages 7-40, February.
    8. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    9. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    10. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    11. Druckman, A. & Jackson, T., 2008. "Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model," Energy Policy, Elsevier, vol. 36(8), pages 3167-3182, August.
    12. Jan C. Thiele & Winfried Kurth & Volker Grimm, 2014. "Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-11.
    13. Richard Loulou, 2008. "ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation," Computational Management Science, Springer, vol. 5(1), pages 41-66, February.
    14. P. Capros & Denise Van Regemorter & Leonidas Paroussos & P. Karkatsoulis & C. Fragkiadakis & S. Tsani & I. Charalampidis & Tamas Revesz, 2013. "GEM-E3 Model Documentation," JRC Working Papers JRC83177, Joint Research Centre (Seville site).
    15. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    16. Messner, Sabine & Schrattenholzer, Leo, 2000. "MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively," Energy, Elsevier, vol. 25(3), pages 267-282.
    17. Jager, W. & Janssen, M. A. & De Vries, H. J. M. & De Greef, J. & Vlek, C. A. J., 2000. "Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model," Ecological Economics, Elsevier, vol. 35(3), pages 357-379, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Castaneda, Monica & Zapata, Sebastian & Cherni, Judith & Aristizabal, Andres J. & Dyner, Isaac, 2020. "The long-term effects of cautious feed-in tariff reductions on photovoltaic generation in the UK residential sector," Renewable Energy, Elsevier, vol. 155(C), pages 1432-1443.
    2. Dong, Changgui & Zhou, Runmin & Li, Jiaying, 2021. "Rushing for subsidies: The impact of feed-in tariffs on solar photovoltaic capacity development in China," Applied Energy, Elsevier, vol. 281(C).
    3. Ahmed Gailani & Tracey Crosbie & Maher Al-Greer & Michael Short & Nashwan Dawood, 2020. "On the Role of Regulatory Policy on the Business Case for Energy Storage in Both EU and UK Energy Systems: Barriers and Enablers," Energies, MDPI, Open Access Journal, vol. 13(5), pages 1-20, March.
    4. August Wierling & Valeria Jana Schwanitz & Jan Pedro Zeiß & Celine Bout & Chiara Candelise & Winston Gilcrease & Jay Sterling Gregg, 2018. "Statistical Evidence on the Role of Energy Cooperatives for the Energy Transition in European Countries," Sustainability, MDPI, Open Access Journal, vol. 10(9), pages 1-25, September.
    5. Li, Hong Xian & Zhang, Yitao & Li, Yan & Huang, Jiaxin & Costin, Glenn & Zhang, Peng, 2021. "Exploring payback-year based feed-in tariff mechanisms in Australia," Energy Policy, Elsevier, vol. 150(C).
    6. Say, Kelvin & John, Michele & Dargaville, Roger, 2019. "Power to the people: Evolutionary market pressures from residential PV battery investments in Australia," Energy Policy, Elsevier, vol. 134(C).
    7. Nuñez-Jimenez, Alejandro & Knoeri, Christof & Rottmann, Fabian & Hoffmann, Volker H., 2020. "The role of responsiveness in deployment policies: A quantitative, cross-country assessment using agent-based modelling," Applied Energy, Elsevier, vol. 275(C).
    8. Nolden, C. & Barnes, J. & Nicholls, J., 2020. "Community energy business model evolution: A review of solar photovoltaic developments in England," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    9. Few, Sheridan & Djapic, Predrag & Strbac, Goran & Nelson, Jenny & Candelise, Chiara, 2020. "Assessing local costs and impacts of distributed solar PV using high resolution data from across Great Britain," Renewable Energy, Elsevier, vol. 162(C), pages 1140-1150.
    10. Felipe Moraes do Nascimento & Julio Cezar Mairesse Siluk & Fernando de Souza Savian & Taís Bisognin Garlet & José Renes Pinheiro & Carlos Ramos, 2020. "Factors for Measuring Photovoltaic Adoption from the Perspective of Operators," Sustainability, MDPI, Open Access Journal, vol. 12(8), pages 1-29, April.

    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. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    2. Liu, Xueying & Madlener, Reinhard, 2019. "The Sky is the Limit: Assessing Aircraft Market Diffusion with Agent-Based Modeling," FCN Working Papers 16/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
    4. Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2019. "A review of designing empirically grounded agent-based models of innovation diffusion: Development process, conceptual foundation and research agenda," Contributions of the Institute for Infrastructure and Resources Management 01/2019, University of Leipzig, Institute for Infrastructure and Resources Management.
    5. Barazza, Elsa & Strachan, Neil, 2020. "The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition," Energy Policy, Elsevier, vol. 138(C).
    6. Dai, Hancheng & Mischke, Peggy & Xie, Xuxuan & Xie, Yang & Masui, Toshihiko, 2016. "Closing the gap? Top-down versus bottom-up projections of China’s regional energy use and CO2 emissions," Applied Energy, Elsevier, vol. 162(C), pages 1355-1373.
    7. Li, Francis G.N. & Trutnevyte, Evelina & Strachan, Neil, 2015. "A review of socio-technical energy transition (STET) models," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 290-305.
    8. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, Open Access Journal, vol. 12(19), pages 1-22, September.
    9. Niamir, Leila & Filatova, Tatiana & Voinov, Alexey & Bressers, Hans, 2018. "Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes," Energy Policy, Elsevier, vol. 118(C), pages 325-345.
    10. Herbert Dawid & Reinhold Decker & Thomas Hermann & Hermann Jahnke & Wilhelm Klat & Rolf König & Christian Stummer, 2017. "Management science in the era of smart consumer products: challenges and research perspectives," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 203-230, March.
    11. Tang, Bao-Jun & Wang, Xiang-Yu & Wei, Yi-Ming, 2019. "Quantities versus prices for best social welfare in carbon reduction: A literature review," Applied Energy, Elsevier, vol. 233, pages 554-564.
    12. Guo, Yingjian & Hawkes, Adam, 2019. "Asset stranding in natural gas export facilities: An agent-based simulation," Energy Policy, Elsevier, vol. 132(C), pages 132-155.
    13. Halkos, George, 2014. "The Economics of Climate Change Policy: Critical review and future policy directions," MPRA Paper 56841, University Library of Munich, Germany.
    14. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    15. Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
    16. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    17. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    18. Dale, M. & Krumdieck, S. & Bodger, P., 2012. "Global energy modelling — A biophysical approach (GEMBA) part 1: An overview of biophysical economics," Ecological Economics, Elsevier, vol. 73(C), pages 152-157.
    19. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    20. Hiromi Yamamoto & Masahiro Sugiyama & Junichi Tsutsui, 2014. "Role of end-use technologies in long-term GHG reduction scenarios developed with the BET model," Climatic Change, Springer, vol. 123(3), pages 583-596, April.

    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:eee:enepol:v:116:y:2018:i:c:p:95-111. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nithya Sathishkumar). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.