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Optimal Operation of Energy Storage Facilities in Incentive-Based Energy Communities

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  • Giovanni Gino Zanvettor

    (Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università di Siena, 53100 Siena, Italy)

  • Marco Casini

    (Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università di Siena, 53100 Siena, Italy)

  • Antonio Vicino

    (Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche, Università di Siena, 53100 Siena, Italy)

Abstract

The green energy transition calls for various solutions to enhance environmental sustainability. One of these is represented by renewable energy communities, which may help transition from centralized energy production to distributed renewable generation. European countries are actively promoting incentive schemes for energy communities to foster local electricity self-consumption in order to balance demand and renewable generation. In this context, energy storage facilities can be employed to gather the energy production surplus and use it in periods of low generation. In this paper, we focus on the optimal operation of an incentive-based energy community in the presence of energy storage systems. A centralized optimization problem was formulated to optimally operate storage systems at the community level. Starting from this solution, distributed charging/discharging commands were found to optimally operate the single storage units. Moreover, conditions guaranteeing the convenience of using energy storage systems inside the community were derived. Numerical simulations were performed to validate the reported results and to evaluate the potential benefits of energy storage facilities inside renewable energy communities.

Suggested Citation

  • Giovanni Gino Zanvettor & Marco Casini & Antonio Vicino, 2024. "Optimal Operation of Energy Storage Facilities in Incentive-Based Energy Communities," Energies, MDPI, vol. 17(11), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2589-:d:1403151
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    References listed on IDEAS

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    1. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    2. Giovanni Gino Zanvettor & Marco Casini & Antonio Giannitrapani & Simone Paoletti & Antonio Vicino, 2022. "Optimal Management of Energy Communities Hosting a Fleet of Electric Vehicles," Energies, MDPI, vol. 15(22), pages 1-16, November.
    3. Francesca Ceglia & Elisa Marrasso & Giovanna Pallotta & Carlo Roselli & Maurizio Sasso, 2022. "The State of the Art of Smart Energy Communities: A Systematic Review of Strengths and Limits," Energies, MDPI, vol. 15(9), pages 1-28, May.
    4. Cornélusse, Bertrand & Savelli, Iacopo & Paoletti, Simone & Giannitrapani, Antonio & Vicino, Antonio, 2019. "A community microgrid architecture with an internal local market," Applied Energy, Elsevier, vol. 242(C), pages 547-560.
    5. Bertrand Corn'elusse & Iacopo Savelli & Simone Paoletti & Antonio Giannitrapani & Antonio Vicino, 2018. "A Community Microgrid Architecture with an Internal Local Market," Papers 1810.09803, arXiv.org, revised Feb 2019.
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