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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.

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Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0760.

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Length: 24
Date of creation: Sep 2007
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Handle: RePEc:cam:camdae:0760
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  1. Bronwyn H. Hall & Beethika Khan, 2004. "Adoption of New Technology," Development and Comp Systems 0401001, EconWPA.
  2. Baldwin, John & Lin, Zhengxi, 2002. "Impediments to advanced technology adoption for Canadian manufacturers," Research Policy, Elsevier, vol. 31(1), pages 1-18, January.
  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. Oriana Bandiera & Imran Rasul, 2002. "Social networks and technology adoption in Northern Mozambique," LSE Research Online Documents on Economics 3539, London School of Economics and Political Science, LSE Library.
  5. 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.
  6. Sugden, Robert, 1991. "Rational Choice: A Survey of Contributions from Economics and Philosophy," Economic Journal, Royal Economic Society, vol. 101(407), pages 751-85, July.
  7. 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.
  8. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind and Society: Cognitive Studies in Economics and Social Sciences, Fondazione Rosselli, vol. 1(1), pages 57-72, March.
  9. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-31, March.
  10. Rosenberg, Nathan, 1972. "Factors affecting the diffusion of technology," Explorations in Economic History, Elsevier, vol. 10(1), pages 3-33.
  11. Christoph H. Loch & Bernardo A. Huberman, 1999. "A Punctuated-Equilibrium Model of Technology Diffusion," Management Science, INFORMS, vol. 45(2), pages 160-177, February.
  12. 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.
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