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Agent-based simulation of policy induced diffusion of smart meters

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  • Rixen, Martin
  • Weigand, Jürgen

Abstract

How can policy makers influence the path of innovation diffusion effectively and efficiently? We tackle this question in an agent-based model that integrates demand and supply for Smart Meters. Consumers adopt due to awareness and attainment of price thresholds. Suppliers act strategically upon Cournot competition. We add different policies to the simulation and analyze effects on speed and level of Smart Meter adoption. The tested interventions are: Market liberalization, information policies, and monetary grants. From our results we conclude that “One size does not fit all”. The best-suited intervention depends on the regulator's objective. Information policies speed-up adoption, but are ineffective in monopolies and if the timing is late. Monetary grants boost speed and level, but policy costs as well. Market structure is critical: Interventions in closed markets primarily favor the monopolist, while intensifying competition raises effectiveness and efficiency. Regulators may combine policies to gain synergies and utilize strategic decision making of suppliers.

Suggested Citation

  • Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    • Handle: RePEc:eee:tefoso:v:85:y:2014:i:c:p:153-167
    DOI: 10.1016/j.techfore.2013.08.011
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    Cited by:

    1. Diaz-Rainey, Ivan & Ashton, John K., 2015. "Investment inefficiency and the adoption of eco-innovations: The case of household energy efficiency technologies," Energy Policy, Elsevier, vol. 82(C), pages 105-117.
    2. Anna Kowalska-Pyzalska & Katarzyna Byrka, 2019. "Determinants of the Willingness to Energy Monitoring by Residential Consumers: A Case Study in the City of Wroclaw in Poland," Energies, MDPI, vol. 12(5), pages 1-20, March.
    3. Carlos M. Fernández-Márquez & Matías Fuentes & Juan José Martínez & Francisco J. Vázquez, 2021. "Productivity and unemployment: an ABM approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 133-151, January.
    4. 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.
    5. Kowalska-Pyzalska, Anna, 2018. "What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3570-3581.
    6. 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 Technology.
    7. Anna Kowalska-Pyzalska & Katarzyna Byrka & Jakub Serek, 2020. "How to Foster the Adoption of Electricity Smart Meters? A Longitudinal Field Study of Residential Consumers," Energies, MDPI, vol. 13(18), pages 1-19, September.
    8. Linares, Ian Marques Porto & De Paulo, Alex Fabianne & Porto, Geciane Silveira, 2019. "Patent-based network analysis to understand technological innovation pathways and trends," Technology in Society, Elsevier, vol. 59(C).
    9. Carlos M. Fernández-Márquez & Francisco Fatas-Villafranca & Francisco J. Vázquez, 2017. "A computational consumer-driven market model: statistical properties and the underlying industry dynamics," Computational and Mathematical Organization Theory, Springer, vol. 23(3), pages 319-346, September.
    10. Sudtasan, Tatcha & Mitomo, Hitoshi, 2017. "Willingness-to-pay for FTTH for secured and stable usage of OTT media streaming services," 28th European Regional ITS Conference, Passau 2017 169500, International Telecommunications Society (ITS).
    11. Swinerd, Chris & McNaught, Ken R., 2015. "Comparing a simulation model with various analytic models of the international diffusion of consumer technology," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 330-343.
    12. JinHyo Joseph Yun & DongKyu Won & EuiSeob Jeong & KyungBae Park & DooSeok Lee & Tan Yigitcanlar, 2017. "Dismantling of the Inverted U-Curve of Open Innovation," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    13. Carlos M. Fernández-Márquez & Francisco Fatás-Villafranca & Francisco J. Vázquez, 2017. "Endogenous Demand and Demanding Consumers: A Computational Approach," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 307-323, February.
    14. Carlos M. Fernández‐Márquez & Francisco J. Vázquez, 2018. "How information and communication technology affects decision‐making on innovation diffusion: An agent‐based modelling approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(3), pages 124-133, July.
    15. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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    More about this item

    Keywords

    Induced diffusion; Innovation adoption; Agent-based modeling; Smart Metering; Competitive dynamics;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection

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