Renewable Energy Communities are a new paradigm promoted by the European Union as a promising solution to reduce both the carbon footprint and the impact of Renewable Energy Sources in distribution grids. Promoting the transition to a sustainable energy system involves several strategies and initiatives. A key aspect is the regulatory framework established by each member state to effectively manage the aggregation and management of new customers. In Italy for example, specific eligible configurations, incentives, and bureaucratic procedures have been defined to facilitate the creation of Energy Communities. However, these regulatory frameworks do not provide guidelines on the optimal design and management of such communities. To address this gap, new research projects such as “Samothrace - SiciliAn MicronanOTecH Research And innovation CEnter” aim at developing new technologies and approaches that can promote the development of Energy Communities. In this context, this paper presents the definition of an algorithm to choose the optimal mix of consumers within the energy community to maximize the economic benefit from self-consumption for a given PV plant or conversely to choose the best size of the PV system, for a given set of consumers. In both cases, loads will not be either shifted or curtailed and electricity will not be stored. In order to ease the widespread adoption of the method, the optimization problem was conceived to be developed in a spreadsheet environment. The algorithm was applied to the case study of the island of Pantelleria, thus deriving the potential economic benefits for the components of the community.

Di Silvestre Maria Luisa, Montana Francesco, Riva Sanseverino Eleonora, Sciume Giuseppe, Zizzo Gaetano (2023). An Algorithm for Renewable Energy Communities Designing by Maximizing Shared Energy. In Proceedings of 2023 AEIT International Annual Conference (AEIT) (pp. 6) [10.23919/AEIT60520.2023.10330365].

An Algorithm for Renewable Energy Communities Designing by Maximizing Shared Energy

Di Silvestre Maria Luisa;Montana Francesco;Riva Sanseverino Eleonora;Sciume Giuseppe;Zizzo Gaetano
2023-12-01

Abstract

Renewable Energy Communities are a new paradigm promoted by the European Union as a promising solution to reduce both the carbon footprint and the impact of Renewable Energy Sources in distribution grids. Promoting the transition to a sustainable energy system involves several strategies and initiatives. A key aspect is the regulatory framework established by each member state to effectively manage the aggregation and management of new customers. In Italy for example, specific eligible configurations, incentives, and bureaucratic procedures have been defined to facilitate the creation of Energy Communities. However, these regulatory frameworks do not provide guidelines on the optimal design and management of such communities. To address this gap, new research projects such as “Samothrace - SiciliAn MicronanOTecH Research And innovation CEnter” aim at developing new technologies and approaches that can promote the development of Energy Communities. In this context, this paper presents the definition of an algorithm to choose the optimal mix of consumers within the energy community to maximize the economic benefit from self-consumption for a given PV plant or conversely to choose the best size of the PV system, for a given set of consumers. In both cases, loads will not be either shifted or curtailed and electricity will not be stored. In order to ease the widespread adoption of the method, the optimization problem was conceived to be developed in a spreadsheet environment. The algorithm was applied to the case study of the island of Pantelleria, thus deriving the potential economic benefits for the components of the community.
dic-2023
978-88-87237-60-3
Di Silvestre Maria Luisa, Montana Francesco, Riva Sanseverino Eleonora, Sciume Giuseppe, Zizzo Gaetano (2023). An Algorithm for Renewable Energy Communities Designing by Maximizing Shared Energy. In Proceedings of 2023 AEIT International Annual Conference (AEIT) (pp. 6) [10.23919/AEIT60520.2023.10330365].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/621410
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