[go: up one dir, main page]

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

    File URL: https://libkey.io/10.1016/j.enpol.2018.01.060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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 Research Reports JRC83177, Joint Research Centre.
    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. Say, Kelvin & John, Michele, 2021. "Molehills into mountains: Transitional pressures from household PV-battery adoption under flat retail and feed-in tariffs," Energy Policy, Elsevier, vol. 152(C).
    2. Juárez-Luna, David & Urdiales, Eduardo, 2021. "Participación de la capacidad fotovoltaica instalada en México: un análisis benchmarking [Share of installed photovoltaic capacity in Mexico: a benchmarking analysis]," MPRA Paper 114589, University Library of Munich, Germany.
    3. 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.
    4. Moncada, J.A. & Tao, Z. & Valkering, P. & Meinke-Hubeny, F. & Delarue, E., 2021. "Influence of distribution tariff structures and peer effects on the adoption of distributed energy resources," Applied Energy, Elsevier, vol. 298(C).
    5. Cuenca, Juan J. & Daly, Hannah E. & Hayes, Barry P., 2023. "Sharing the grid: The key to equitable access for small-scale energy generation," Applied Energy, Elsevier, vol. 349(C).
    6. 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).
    7. 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, vol. 13(5), pages 1-20, March.
    8. 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, vol. 10(9), pages 1-25, September.
    9. Qureshi, Shahab & Phan-Van, Long & Nguyen, Linh Dan & Nguyen-Duc, Tuyen, 2023. "Rooftop solar policies feasibility assessment model: Vietnam case study," Energy Policy, Elsevier, vol. 177(C).
    10. 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).
    11. 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).
    12. 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).
    13. Hasan Dinçer & Serhat Yüksel & Tamer Aksoy & Ümit Hacıoğlu, 2022. "Application of M-SWARA and TOPSIS Methods in the Evaluation of Investment Alternatives of Microgeneration Energy Technologies," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    14. 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).
    15. 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.
    16. Zhang, Libo & Chen, Changqi & Wang, Qunwei & Zhou, Dequn, 2021. "The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China," Energy, Elsevier, vol. 232(C).
    17. 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, 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, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    3. 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.
    4. Wen, Xin & Jaxa-Rozen, Marc & Trutnevyte, Evelina, 2023. "Hindcasting to inform the development of bottom-up electricity system models: The cases of endogenous demand and technology learning," Applied Energy, Elsevier, vol. 340(C).
    5. Gjorgiev, Blazhe & Garrison, Jared B. & Han, Xuejiao & Landis, Florian & van Nieuwkoop, Renger & Raycheva, Elena & Schwarz, Marius & Yan, Xuqian & Demiray, Turhan & Hug, Gabriela & Sansavini, Giovanni, 2022. "Nexus-e: A platform of interfaced high-resolution models for energy-economic assessments of future electricity systems," Applied Energy, Elsevier, vol. 307(C).
    6. 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).
    7. 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.
    8. 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.
    9. David Huckebrink & Valentin Bertsch, 2021. "Integrating Behavioural Aspects in Energy System Modelling—A Review," Energies, MDPI, vol. 14(15), pages 1-26, July.
    10. 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.
    11. 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.
    12. Halkos, George, 2014. "The Economics of Climate Change Policy: Critical review and future policy directions," MPRA Paper 56841, University Library of Munich, Germany.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. Sachs, Julia & Meng, Yiming & Giarola, Sara & Hawkes, Adam, 2019. "An agent-based model for energy investment decisions in the residential sector," Energy, Elsevier, vol. 172(C), pages 752-768.
    18. Yongchao Zeng & Peiwu Dong & Yingying Shi & Yang Li, 2018. "On the Disruptive Innovation Strategy of Renewable Energy Technology Diffusion: An Agent-Based Model," Energies, MDPI, vol. 11(11), pages 1-21, November.
    19. Maryse Labriet & Laurent Drouet & Marc Vielle & Richard Loulou & Amit Kanudia & Alain Haurie, 2015. "Assessment of the Effectiveness of Global Climate Policies Using Coupled Bottom-up and Top-down Models," Working Papers 2015.23, Fondazione Eni Enrico Mattei.
    20. Wang, Ge & Zhang, Qi & Li, Yan & Li, Hailong, 2018. "Policy simulation for promoting residential PV considering anecdotal information exchanges based on social network modelling," Applied Energy, Elsevier, vol. 223(C), pages 1-10.

    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.

    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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.