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An Agent Based Simulation of Smart Metering Technology Adoption

Author

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  • Tao Zhang

    (Judge Business School, University of Cambridge)

  • William J. Nuttall

    (Judge Business School, University of Cambridge)

Abstract

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Tao Zhang & William J. Nuttall, 2007. "An Agent Based Simulation of Smart Metering Technology Adoption," Working Papers EPRG 0727, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg0727
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    References listed on IDEAS

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    6. 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.
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    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.
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    Citations

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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. Tao Zhang & William J. Nuttall, 2008. "Evaluating Government's Policies on Promoting Smart Metering in Retail Electricity Markets via Agent Based Simulation," Working Papers EPRG 0822, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Kyle Bahr & Masami Nakagawa, 2017. "The effect of bidirectional opinion diffusion on social license to operate," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(4), pages 1235-1245, August.
    4. Martin Rixen & Jürgen Weigand, 2013. "Agent-Based Simulation Of Consumer Demand For Smart Metering Tariffs," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 10(05), pages 1-26.
    5. 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, vol. 5(2), pages 1-32, February.
    6. Eben Upton & William J. Nuttall, 2013. "Fuel Panics: insights from spatial agent-based simulation," Working Papers EPRG 1305, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. 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.
    8. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.

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    More about this item

    Keywords

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

    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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