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Unintended consequences: The snowball effect of energy communities

Author

Listed:
  • Ibrahim Abada

    (ENGIE)

  • Andreas Ehrenmann

    (ENGIE, EPRG)

  • Xavier Lambin

    (Toulouse School of Economics)

Abstract

Following the development of decentralized generation and smart appliances, energy communities have become a phenomenon of increased interest. While the benefits of such communities have been discussed, there is increasing concern that inadequate grid tariffs may lead to excess adoption of such business models. Furthermore, snowball effects may be observed following the effects these communities have on grid tariffs. We show that restraining the study to a simple cost-benefit analysis is far from satisfactory. Therefore, we use the framework of cooperative game theory to take account of the ability of communities to share gains between members. The interaction between energy communities and the DSO then results in a non-cooperative equilibrium. We provide mathematical formulations and intuitions of such effects, and carry out realistic numerical applications where communities can invest jointly in solar panels and batteries. We show that such a snowball effect may be observed, but its magnitude and its welfare effects will depend on the grid tariff structure that is implemented, leading to possible PV over-investments.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Ibrahim Abada & Andreas Ehrenmann & Xavier Lambin, 2018. "Unintended consequences: The snowball effect of energy communities," Working Papers EPRG 1812, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1812
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    Cited by:

    1. Cortade, Thomas & Poudou, Jean-Christophe, 2022. "Peer-to-peer energy platforms: Incentives for prosuming," Energy Economics, Elsevier, vol. 109(C).
    2. Zacharie De Grève & Jérémie Bottieau & David Vangulick & Aurélien Wautier & Pierre-David Dapoz & Adriano Arrigo & Jean-François Toubeau & François Vallée, 2020. "Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities," Energies, MDPI, vol. 13(18), pages 1-17, September.

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    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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