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Evaluating Government's Policies on Promoting Smart Metering in Retail Electricity Markets via Agent Based Simulation

Author

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  • Tao Zhang

    (Judge Business School, University of Cambridge)

  • William J. Nuttall

    (Judge Business School, University of Cambridge)

Abstract

In this paper, we develop an agent-based model of a market game in order to evaluate the effectiveness of the UK government’s 2008-2010 policy on promoting smart metering. We also consider possible supplementary strategies. With the model, we test the effectiveness of four possible strategy options and suggest their policy implications. The context of the paper is a practical application of agent-based simulation to the retail electricity market in Britain. The contribution of the research are both in the areas of policy making for electricity markets and in the methodological use of agent-based simulation for studying social complex systems involving human behaviour.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • 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.
    • Handle: RePEc:enp:wpaper:eprg0822
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    Cited by:

    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. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2014. "Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach," HSC Research Reports HSC/14/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    4. Anna Kowalska-Pyzalska, 2015. "Social acceptance of green energy and dynamic electricity tariffs - a short review," HSC Research Reports HSC/15/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    5. Weron, Tomasz & Kowalska-Pyzalska, Anna & Weron, Rafał, 2018. "The role of educational trainings in the diffusion of smart metering platforms: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 591-600.
    6. 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.
    7. Haghnevis, Moeed & Askin, Ronald G. & Armbruster, Dieter, 2016. "An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 67-87.
    8. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    9. Stagnaro, Carlo & Amenta, Carlo & Di Croce, Giulia & Lavecchia, Luciano, 2017. "La liberalizzazione del mercato elettrico - Una proposta per superare la maggior tutela [The liberalization of Italy's retail electricity market: a policy proposal]," MPRA Paper 81768, University Library of Munich, Germany.
    10. Anna Kowalska-Pyzalska, 2016. "What makes consumers adopt to innovative energy services in the energy market?," HSC Research Reports HSC/16/09, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    11. 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.
    12. Sovacool, Benjamin K. & Kivimaa, Paula & Hielscher, Sabine & Jenkins, Kirsten, 2017. "Vulnerability and resistance in the United Kingdom's smart meter transition," Energy Policy, Elsevier, vol. 109(C), pages 767-781.
    13. Claire Bergaentzlé, 2012. "Particularités d'adoption des compteurs intelligents au Royaume-Uni et en Allemagne : entre marchés de comptage libéralisé et règles à mettre en place pour un réel smart grid intégré," Post-Print halshs-00793322, HAL.

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    More about this item

    Keywords

    agent-based simulation; smart metering technology; the Theory of Planned Behaviour; retail electricity market;
    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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