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Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-based Simulation


  • Palmer, Johannes

    () (RWTH Aachen University)

  • Sorda, Giovanni

    () (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    () (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))


We propose an agent-based model to simulate the diffusion of small PV systems among single- or two-family homes in Italy over the 2006-2026 period. To this end,we explicitly model the geographical distribution of the agents in order to account for regional differences across the country. The adoption decision is assumed to be influenced predominantly by (1) the payback period of the investment, (2) its environmental benefit, (3) the household’s income, and (4) the influence of communication with other agents. For the estimation of the payback period, the model considers investment costs, local irradiation levels, governmental support, earnings from using self-produced electricity vs. buying electricity from the grid, as well as various administrative fees and maintenance costs. The environmental benefit is estimated by a proxy for the CO2 emissions saved. The level of the household income is associated with the specific economic conditions of the region where the agent is located, as well as the agent’s socio-economic group (age group, level of education, household type). Finally, the influence of communication is measured by the number of links with other households that have already adopted a PV system. In each simulation step, the program dynamically updates the social system and the communication network, while the evolution of the PV system’s investment costs depend on a one-factor experience curve model that is based on the exogeneous development of the global installed PV capacity. Our results show that Italy’s domestic PV installations are already beyond an initial stage of rapid growth and, though likely to spread further, they will do so at a significantly slower rate of diffusion.

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  • Palmer, Johannes & Sorda, Giovanni & Madlener, Reinhard, 2013. "Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-based Simulation," FCN Working Papers 9/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
  • Handle: RePEc:ris:fcnwpa:2013_009

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    References listed on IDEAS

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    Cited by:

    1. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    2. Katarzyna Byrka & Arkadiusz Jedrzejewski & Katarzyna Sznajd-Weron & Rafal Weron, 2015. "Difficulty is critical: Psychological factors in modeling diffusion of green products and practices," HSC Research Reports HSC/15/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Rohlfs, Wilko & Madlener, Reinhard, 2013. "Challenges in the Evaluation of Ultra-Long-Lived Projects: Risk Premia for Projects with Eternal Returns or Costs," FCN Working Papers 13/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Huotari, Pontus & Järvi, Kati & Kortelainen, Samuli & Huhtamäki, Jukka, 2017. "Winner does not take all: Selective attention and local bias in platform-based markets," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 313-326.
    5. repec:eee:rensus:v:81:y:2018:i:p2:p:1879-1886 is not listed on IDEAS
    6. repec:eee:rensus:v:81:y:2018:i:p2:p:3131-3139 is not listed on IDEAS
    7. repec:eee:rensus:v:82:y:2018:i:p3:p:3570-3581 is not listed on IDEAS
    8. Boateng, Mark K. & Awuah-Offei, Kwame, 2017. "Agent-based modeling framework for modeling the effect of information diffusion on community acceptance of mining," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 1-11.
    9. Galassi, Veronica & Madlener, Reinhard, 2014. "Identifying Business Models for Photovoltaic Systems with Storage in the Italian Market: A Discrete Choice Experiment," FCN Working Papers 19/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    10. Simpson, Genevieve & Clifton, Julian, 2016. "Subsidies for residential solar photovoltaic energy systems in Western Australia: Distributional, procedural and outcome justice," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 262-273.
    11. McCoy, Daire & Lyons, Sean, 2014. "The diffusion of electric vehicles: An agent-based microsimulation," MPRA Paper 54560, University Library of Munich, Germany.
    12. repec:eee:enepol:v:109:y:2017:i:c:p:270-278 is not listed on IDEAS
    13. 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.

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    PV; Technological diffusion; Agent-based modeling; Italy;

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