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Long-Term Solar Photovoltaics Penetration in Single- and Two-Family Houses in Switzerland

Author

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  • Evangelos Panos

    (Paul Scherrer Institute, Energy Economics Group, Villigen 5232, Switzerland)

  • Stavroula Margelou

    (Swissgrid AG, Ancillary Services and Analytics, Aarau 5001, Switzerland)

Abstract

The Swiss energy strategy aims at increasing electricity generation from solar power by 2050, to fulfil Switzerland’s commitments in the Paris Agreement. However, the market of single- and two-family houses is characterized by low return rates for excess power injected to the grid, and the installation of rooftop solar photovoltaic (PV) is sensitive to financial incentives. We assess the drivers influencing the diffusion of rooftop solar PV systems until 2050, by employing an agent-based model. An agent is a single- or two-family house, and its decision to invest depends on the economic profitability of the investment, the agent’s income, environmental benefits (injunctive social norm), awareness and knowledge about the solar PV technology, and the impact of the social network (descriptive social norm). The model includes a synthetic population of agents, statistically equivalent to the true population. We also investigate the impact of different support policies, technology learning rates, electricity prices, and discount rates on the investment decision. We find that the concept of prosumer emerges, mainly via self-consumption strategies. The diffusion process of rooftop solar PV systems in single- and two-family houses gains momentum in the future. In the near-term, PV deployment is sensitive to the profitability of the investment, while after the year 2030, peer effects play an increasing role in the agents’ investment decisions.

Suggested Citation

  • Evangelos Panos & Stavroula Margelou, 2019. "Long-Term Solar Photovoltaics Penetration in Single- and Two-Family Houses in Switzerland," Energies, MDPI, vol. 12(13), pages 1-33, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2460-:d:243048
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    References listed on IDEAS

    as
    1. Karjalainen, Sami & Ahvenniemi, Hannele, 2019. "Pleasure is the profit - The adoption of solar PV systems by households in Finland," Renewable Energy, Elsevier, vol. 133(C), pages 44-52.
    2. Zhai, Pei & Williams, Eric D., 2012. "Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model," Renewable Energy, Elsevier, vol. 41(C), pages 350-357.
    3. Laura-Lucia Richter, 2013. "Social Effects in the Diffusion of Solar Photovoltaic Technology in the UK," Working Papers EPRG 1332, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Yael Parag & Benjamin K. Sovacool, 2016. "Electricity market design for the prosumer era," Nature Energy, Nature, vol. 1(4), pages 1-6, April.
    5. Curtius, Hans Christoph & Hille, Stefanie Lena & Berger, Christian & Hahnel, Ulf Joachim Jonas & Wüstenhagen, Rolf, 2018. "Shotgun or snowball approach? Accelerating the diffusion of rooftop solar photovoltaics through peer effects and social norms," Energy Policy, Elsevier, vol. 118(C), pages 596-602.
    6. 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.
    7. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    8. Snape, J.R. & Boait, P.J. & Rylatt, R.M., 2015. "Will domestic consumers take up the renewable heat incentive? An analysis of the barriers to heat pump adoption using agent-based modelling," Energy Policy, Elsevier, vol. 85(C), pages 32-38.
    9. Karneyeva, Yuliya & Wüstenhagen, Rolf, 2017. "Solar feed-in tariffs in a post-grid parity world: The role of risk, investor diversity and business models," Energy Policy, Elsevier, vol. 106(C), pages 445-456.
    10. Kirill Mueller & Kay W. Axhausen, 2011. "Hierarchical IPF: Generating a synthetic population for Switzerland," ERSA conference papers ersa11p305, European Regional Science Association.
    11. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    12. Laura-Lucia Richter, 2013. "Social Effects in the Diffusion of solar Photovoltaic Technology in the UK," Cambridge Working Papers in Economics 1357, Faculty of Economics, University of Cambridge.
    13. Bryan Bollinger & Kenneth Gillingham, 2012. "Peer Effects in the Diffusion of Solar Photovoltaic Panels," Marketing Science, INFORMS, vol. 31(6), pages 900-912, November.
    14. 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.
    15. Noori, Mehdi & Tatari, Omer, 2016. "Development of an agent-based model for regional market penetration projections of electric vehicles in the United States," Energy, Elsevier, vol. 96(C), pages 215-230.
    16. Lee, Timothy & Yao, Runming & Coker, Phil, 2014. "An analysis of UK policies for domestic energy reduction using an agent based tool," Energy Policy, Elsevier, vol. 66(C), pages 267-279.
    17. 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.
    18. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    19. Rai, Varun & Reeves, D. Cale & Margolis, Robert, 2016. "Overcoming barriers and uncertainties in the adoption of residential solar PV," Renewable Energy, Elsevier, vol. 89(C), pages 498-505.
    20. Modis, Theodore, 2007. "Strengths and weaknesses of S-curves," OSF Preprints r5zk7, Center for Open Science.
    21. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
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    1. Ludwik Wicki & Robert Pietrzykowski & Dariusz Kusz, 2022. "Factors Determining the Development of Prosumer Photovoltaic Installations in Poland," Energies, MDPI, vol. 15(16), pages 1-19, August.
    2. Gunkel, Philipp Andreas & Kachirayil, Febin & Bergaentzlé, Claire-Marie & McKenna, Russell & Keles, Dogan & Jacobsen, Henrik Klinge, 2023. "Uniform taxation of electricity: incentives for flexibility and cost redistribution among household categories," Energy Economics, Elsevier, vol. 127(PB).
    3. Felipe Moraes do Nascimento & Julio Cezar Mairesse Siluk & Fernando de Souza Savian & Taís Bisognin Garlet & José Renes Pinheiro & Carlos Ramos, 2020. "Factors for Measuring Photovoltaic Adoption from the Perspective of Operators," Sustainability, MDPI, vol. 12(8), pages 1-29, April.

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