IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2018-64-3.html
   My bibliography  Save this article

Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model

Author

Listed:
  • Francesco Pasimeni

Abstract

The electricity generation and distribution system in many developed economies is based primarily on the centralised grid. However, there is a need to shift from this traditional system to a newly more decentralised electricity system. This paper explores possible scenarios of adoption and diffusion of Micro-Grids (MGs) in Italy. An agent-based model is formulated to simulate the diffusion process as function of regional factors, subsidies and people's attitude. It assumes that MGs are purchased directly by communities of neighbours, which benefit from cost sharing. Results show high dependence of the diffusion process on regional factors: electricity demand, renewable potential and population. The model confirms that subsidies boost diffusion, mainly when they are regional-based rather than national-based. Higher green attitude accelerates diffusion and reduces environmental impact of the electricity system.

Suggested Citation

  • Francesco Pasimeni, 2019. "Community-Based Adoption and Diffusion of Micro-Grids: Analysis of the Italian Case with Agent-Based Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-11.
    • Handle: RePEc:jas:jasssj:2018-64-3
    as

    Download full text from publisher

    File URL: https://www.jasss.org/22/1/11/11.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. M.P. Swann, 2009. "The Economics of Innovation," Books, Edward Elgar Publishing, number 13211.
    2. Cristiano Antonelli & Gianluigi Ferraris, 2011. "Innovation as an Emerging System Property: An Agent Based Simulation Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 14(2), pages 1-1.
    3. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    4. Safarzyńska, Karolina & Frenken, Koen & van den Bergh, Jeroen C.J.M., 2012. "Evolutionary theorizing and modeling of sustainability transitions," Research Policy, Elsevier, vol. 41(6), pages 1011-1024.
    5. Eker, Sibel & van Daalen, Els, 2015. "A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty," Energy Policy, Elsevier, vol. 82(C), pages 178-196.
    6. Dennis E. Smallwood & John Conlisk, 1979. "Product Quality in Markets Where Consumers are Imperfectly Informed," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(1), pages 1-23.
    7. Eric Abrahamson & Lori Rosenkopf, 1997. "Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation," Organization Science, INFORMS, vol. 8(3), pages 289-309, June.
    8. Editors The, 2008. "From the Editors," Basic Income Studies, De Gruyter, vol. 3(1), pages 1-1, July.
    9. L. Andrew Bollinger & Martti J. van Blijswijk & Gerard P.J. Dijkema & Igor Nikolic, 2016. "An Energy Systems Modelling Tool for the Social Simulation Community," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-1.
    10. Albert Faber & Koen Frenken, 2008. "Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics," Innovation Studies Utrecht (ISU) working paper series 08-15, Utrecht University, Department of Innovation Studies, revised Apr 2008.
    11. Diamond, David, 2009. "The impact of government incentives for hybrid-electric vehicles: Evidence from US states," Energy Policy, Elsevier, vol. 37(3), pages 972-983, March.
    12. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    13. Faber, Albert & Valente, Marco & Janssen, Peter, 2010. "Exploring domestic micro-cogeneration in the Netherlands: An agent-based demand model for technology diffusion," Energy Policy, Elsevier, vol. 38(6), pages 2763-2775, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pia Szichta & Ingela Tietze, 2020. "Sharing Economy in der Elektrizitätswirtschaft: Treiber und Hemmnisse [Title sharing economy in the electricity sector: drivers and barriers]," NachhaltigkeitsManagementForum | Sustainability Management Forum, Springer, vol. 28(3), pages 109-125, December.
    2. Sebastian Hoffmann & Fabian Adelt & Johannes Weyer, 2020. "Modelling End-User Behavior and Behavioral Change in Smart Grids. An Application of the Model of Frame Selection," Energies, MDPI, vol. 13(24), pages 1-26, December.
    3. Francesco Pasimeni, 2020. "The Origin of the Sharing Economy Meets the Legacy of Fractional Ownership," SPRU Working Paper Series 2020-19, SPRU - Science Policy Research Unit, University of Sussex Business School.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Pasimeni & Tommaso Ciarli, 2018. "Diffusion of Shared Goods in Consumer Coalitions. An Agent-Based Model," SPRU Working Paper Series 2018-24, SPRU - Science Policy Research Unit, University of Sussex Business School.
    2. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    3. Francesco Pasimeni, 2017. "Adoption and Diffusion of Micro-Grids in Italy. An Analysis of Regional Factors Using Agent-Based Modelling," SPRU Working Paper Series 2017-09, SPRU - Science Policy Research Unit, University of Sussex Business School.
    4. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    5. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.
    6. Hidayatno, Akhmad & Jafino, Bramka Arga & Setiawan, Andri D. & Purwanto, Widodo Wahyu, 2020. "When and why does transition fail? A model-based identification of adoption barriers and policy vulnerabilities for transition to natural gas vehicles," Energy Policy, Elsevier, vol. 138(C).
    7. Iršová, Zuzana & Havránek, Tomáš, 2013. "Determinants of Horizontal Spillovers from FDI: Evidence from a Large Meta-Analysis," World Development, Elsevier, vol. 42(C), pages 1-15.
    8. Papachristos, George, 2017. "Diversity in technology competition: The link between platforms and sociotechnical transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 291-306.
    9. Cristiano Antonelli & Gianluigi Ferraris, 2018. "The creative response and the endogenous dynamics of pecuniary knowledge externalities: an agent based simulation model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 561-599, October.
    10. Lopolito, A. & Morone, P. & Taylor, R., 2013. "Emerging innovation niches: An agent based model," Research Policy, Elsevier, vol. 42(6), pages 1225-1238.
    11. Cristiano Antonelli, 2017. "Endogenous innovation: the creative response," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 26(8), pages 689-718, November.
    12. Katarzyna Szczepanska-Woszczyna & Mohammed Nadeem, 2016. "Multi-level Approach to the ¡°Social Marketing¡± Context for Innovation: Impact on Organizational Relationships," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 8(2), pages 24-37, April.
    13. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    14. Zhang, Qi & Li, Hailong & Zhu, Lijing & Campana, Pietro Elia & Lu, Huihui & Wallin, Fredrik & Sun, Qie, 2018. "Factors influencing the economics of public charging infrastructures for EV – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 500-509.
    15. Krupa, Joseph S. & Rizzo, Donna M. & Eppstein, Margaret J. & Brad Lanute, D. & Gaalema, Diann E. & Lakkaraju, Kiran & Warrender, Christina E., 2014. "Analysis of a consumer survey on plug-in hybrid electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 64(C), pages 14-31.
    16. Kemp-Benedict, Eric, 2014. "Shifting to a Green Economy: Lock-in, Path Dependence, and Policy Options," MPRA Paper 60175, University Library of Munich, Germany.
    17. Cristiano Antonelli, 2011. "The Economic Complexity of Technological Change: Knowledge Interaction and Path Dependence," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 1, Edward Elgar Publishing.
    18. Cheng He & O. Cem Ozturk & Chris Gu & Jorge Mario Silva-Risso, 2021. "The End of the Express Road for Hybrid Vehicles: Can Governments’ Green Product Incentives Backfire?," Marketing Science, INFORMS, vol. 40(1), pages 80-100, January.
    19. Zhao, Xingrong & Ma, Ye & Shao, Shuai & Ma, Tieju, 2022. "What determines consumers' acceptance of electric vehicles: A survey in Shanghai, China," Energy Economics, Elsevier, vol. 108(C).
    20. KWON Seokbeom & MOTOHASHI Kazuyuki, 2015. "How Institutional Arrangements in the National Innovation System Affect Industrial Competitiveness: A study of Japan and the United States with multiagent simulation," Discussion papers 15065, Research Institute of Economy, Trade and Industry (RIETI).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2018-64-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.