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Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion

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  • Faber, Albert
  • Valente, Marco
  • Janssen, Peter

Abstract

Micro-cogeneration (micro-CHP) is a new technology at the household level, producing electricity in cogeneration with domestic heating, thereby increasing the overall efficiency of domestic energy production. We have developed a prototypical agent-based simulation model for energy technologies competing for demand at the consumer level. The model is specifically geared towards the competition between micro-CHP and incumbent condensing boilers. In the model, both technologies compete on purchase price and costs of usage, to which various (types of) consumers decide on the installation of either technology. Simulations with various gas and electricity prices show that micro-CHP diffusion could be seriously inhibited if demand for natural gas decreases, e.g. due to insulation measures. Further simulations explore various subsidy schemes. A subsidy for purchase is only found to be effective within a limited range of [euro]1400-3250. A subsidy based on decreasing price difference between the competing technologies is much more cost effective than fixed purchase subsidies. Simulations of a subsidy scheme for usage show that a fast market penetration can be reached, but this does not yet take full advantage of technological progress in terms of decreasing CO2 emissions. Selection of the most effective scheme thus depends on the policy criteria assumed.

Suggested Citation

  • Faber, Albert & Valente, Marco & Janssen, Peter, 2010. "Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion," Energy Policy, Elsevier, vol. 38(6), pages 2763-2775, June.
  • Handle: RePEc:eee:enepol:v:38:y:2010:i:6:p:2763-2775
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    References listed on IDEAS

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    1. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.
    2. Arfaoui, Nabila & Brouillat, Eric & Saint Jean, Maïder, 2014. "Policy design and technological substitution: Investigating the REACH regulation in an agent-based model," Ecological Economics, Elsevier, vol. 107(C), pages 347-365.
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    4. Chung, Mo & Park, Chuhwan & Lee, Sukgyu & Park, Hwa-Choon & Im, Yong-Hoon & Chang, Youngho, 2012. "A decision support assessment of cogeneration plant for a community energy system in Korea," Energy Policy, Elsevier, vol. 47(C), pages 365-383.
    5. Kwon, Seokbeom & Motohashi, Kazuyuki, 2017. "How institutional arrangements in the National Innovation System affect industrial competitiveness: A study of Japan and the U.S. with multiagent simulation," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 221-235.
    6. 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.
    7. 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.
    8. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    9. Lee, Timothy & Yao, Runming, 2013. "Incorporating technology buying behaviour into UK-based long term domestic stock energy models to provide improved policy analysis," Energy Policy, Elsevier, vol. 52(C), pages 363-372.
    10. Lopolito, A. & Morone, P. & Taylor, R., 2013. "Emerging innovation niches: An agent based model," Research Policy, Elsevier, vol. 42(6), pages 1225-1238.
    11. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    12. Tomas Balint & Francesco Lamperti & Antoine Mandel & Mauro Napoletano & Andrea Roventini & Sandro Sapio, 2017. "Complexity and the economics of climate change : a survey and a look foreward," Sciences Po publications info:hdl:2441/1nlv566svi8, Sciences Po.
    13. 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.
    14. Francesco Pasimeni, 2017. "Adoption and Diffusion of Micro-Grids in Italy. An Analysis of Regional Factors Using Agent-Based Modelling," SPRU Working Paper Series 2017-09, SPRU - Science and Technology Policy Research, University of Sussex.
    15. Zhang, Tao & Siebers, Peer-Olaf & Aickelin, Uwe, 2012. "A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK," Energy Policy, Elsevier, vol. 47(C), pages 102-110.
    16. Busch, Jonathan & Roelich, Katy & Bale, Catherine S.E. & Knoeri, Christof, 2017. "Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks," Energy Policy, Elsevier, vol. 100(C), pages 170-180.
    17. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    18. Bodo, Peter, 2016. "MADness in the method: On the volatility and irregularity of technology diffusion," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 2-11.
    19. Lee, Timothy & Yao, Runming & Coker, Phil, 2014. "An analysis of UK policies for domestic energy reduction using an agent based tool," Energy Policy, Elsevier, vol. 66(C), pages 267-279.
    20. KWON Seokbeom & MOTOHASHI Kazuyuki, 2015. "How Institutional Arrangements in the National Innovation System Affect Industrial Competitiveness: A study of Japan and the United States with multiagent simulation," Discussion papers 15065, Research Institute of Economy, Trade and Industry (RIETI).

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