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An Agent Based Simulation Of Smart Metering Technology Adoption


  • Zhang, T.
  • Nuttall, W.J.


Based on the classic behavioural theory “the Theory of Planned Behaviour”, we develop an agent-based model to simulate the diffusion of smart metering technology in the electricity market. We simulate the emergent adoption of smart metering technology under different management strategies and economic regulations. Our research results show that in terms of boosting the take-off of smart meters in the electricity market, choosing the initial users on a random and geographically dispersed basis and encouraging meter competition between energy suppliers can be two very effective strategies. We also observe an “S-curve” diffusion of smart metering technology and a “lock-in” effect in the model. The research results provide us with insights as to effective policies and strategies for the roll-out of smart metering technology in the electricity market.

Suggested Citation

  • Zhang, T. & Nuttall, W.J., 2007. "An Agent Based Simulation Of Smart Metering Technology Adoption," Cambridge Working Papers in Economics 0760, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:0760

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    References listed on IDEAS

    1. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
    2. Bunn, Derek W. & Oliveira, Fernando S., 2007. "Agent-based analysis of technological diversification and specialization in electricity markets," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1265-1278, September.
    3. Hayashi Fumiko & Klee Elizabeth, 2003. "Technology Adoption and Consumer Payments: Evidence from Survey Data," Review of Network Economics, De Gruyter, vol. 2(2), pages 1-16, June.
    4. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    5. Lopez de Haro, S. & Sanchez Martin, P. & de la Hoz Ardiz, J.E. & Fernandez Caro, J., 2007. "Estimating conjectural variations for electricity market models," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1322-1338, September.
    6. Baldwin, John & Lin, Zhengxi, 2002. "Impediments to advanced technology adoption for Canadian manufacturers," Research Policy, Elsevier, vol. 31(1), pages 1-18, January.
    7. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    8. Sugden, Robert, 1991. "Rational Choice: A Survey of Contributions from Economics and Philosophy," Economic Journal, Royal Economic Society, vol. 101(407), pages 751-785, July.
    9. Christoph H. Loch & Bernardo A. Huberman, 1999. "A Punctuated-Equilibrium Model of Technology Diffusion," Management Science, INFORMS, vol. 45(2), pages 160-177, February.
    10. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    11. Hall, Bronwyn H. & Khan, Beethika, 2003. "Adoption of New Technology," Department of Economics, Working Paper Series qt3wg4p528, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    12. Rosenberg, Nathan, 1972. "Factors affecting the diffusion of technology," Explorations in Economic History, Elsevier, vol. 10(1), pages 3-33.
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    Cited by:

    1. Elena Claire Ricci, 2013. "Smart-Grids and Climate Change. Consumer adoption of smart energy behaviour: a system dynamics approach to evaluate the mitigation potential," Working Papers 2013.71, Fondazione Eni Enrico Mattei.
    2. Zhang, T. & Nuttall, W.J., 2008. "Evaluating Government’s Policies on Promoting Smart Metering in Retail Electricity Markets via Agent Based Simulation," Cambridge Working Papers in Economics 0842, Faculty of Economics, University of Cambridge.
    3. Paul Lehmann & Felix Creutzig & Melf-Hinrich Ehlers & Nele Friedrichsen & Clemens Heuson & Lion Hirth & Robert Pietzcker, 2012. "Carbon Lock-Out: Advancing Renewable Energy Policy in Europe," Energies, MDPI, Open Access Journal, vol. 5(2), pages 1-32, February.
    4. repec:spr:endesu:v:19:y:2017:i:4:d:10.1007_s10668-016-9792-9 is not listed on IDEAS
    5. Eben Upton & William J. Nuttall, 2013. "Fuel Panics - insights from spatial agent-based simulation," Cambridge Working Papers in Economics 1309, Faculty of Economics, University of Cambridge.
    6. Newberry, D., 2012. "The roubstness of agent-based models of electricity wholesale markets," Cambridge Working Papers in Economics 1228, Faculty of Economics, University of Cambridge.
    7. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.

    More about this item


    Agent-based simulation; smart metering technology; the Theory of Planned Behaviour; technology diffusion.;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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