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From investment optimization to fair benefit distribution in renewable energy community modelling

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  • Casalicchio, Valeria
  • Manzolini, Giampaolo
  • Prina, Matteo Giacomo
  • Moser, David

Abstract

Energy communities are becoming a key topic in the decarbonization process as they can simultaneously guarantee economic, environmental, and social benefits. In this paper, an integrated method for the implementation of a linear bottom-up optimization model has been developed in order to address these aspects of an energy community: (i) definition of the dispatch and the best technology mix; (ii) assessment of the role of the Demand Side Management; (iii) definition of an original and fair method to allocate the benefit among the participants and of a Fairness Index to compare different business models. The developed method has been applied to an illustrative case study through the implementation of the Italian regulatory framework definitions and costs. The outcomes highlight how Demand Side Management and the energy community composition of the energy community impact on the overall investment: a case study, with heterogeneous composition and characterized by a 20% of flexible load, presents a reduction of 13% in photovoltaic and 93% in storage system capacity with respect to the case without Demand Side Management. The renewable source consumption with a more homogeneous case study decreases by around 20%–33% and bill savings by around 30%. These results impact also on each participant contribution, which underpins the introduced fair distribution method. Leading thus to a different and more proper distribution of the benefit, in order to guarantee everyone the fairest economic return. Moreover, a Fairness Index has been introduced to assess the consistency of other Business Models with respect to the fair distribution.

Suggested Citation

  • Casalicchio, Valeria & Manzolini, Giampaolo & Prina, Matteo Giacomo & Moser, David, 2022. "From investment optimization to fair benefit distribution in renewable energy community modelling," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s030626192101672x
    DOI: 10.1016/j.apenergy.2021.118447
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    7. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
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    9. Shoaib Ahmed & Amjad Ali & Antonio D’Angola, 2024. "A Review of Renewable Energy Communities: Concepts, Scope, Progress, Challenges, and Recommendations," Sustainability, MDPI, vol. 16(5), pages 1-34, February.
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    11. Barone, G. & Buonomano, A. & Forzano, C. & Palombo, A. & Russo, G., 2023. "The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
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    14. Dorahaki, Sobhan & Rashidinejad, Masoud & Fatemi Ardestani, Seyed Farshad & Abdollahi, Amir & Salehizadeh, Mohammad Reza, 2023. "An integrated model for citizen energy communities and renewable energy communities based on clean energy package: A two-stage risk-based approach," Energy, Elsevier, vol. 277(C).
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    17. Myriam Caratù & Valerio Brescia & Ilaria Pigliautile & Paolo Biancone, 2023. "Assessing Energy Communities’ Awareness on Social Media with a Content and Sentiment Analysis," Sustainability, MDPI, vol. 15(8), pages 1-28, April.

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