Modeling Retail Buildings Within Renewable Energy Communities: Generation and Implementation of Reference Energy Use Profiles

Samuele Lozza, Matteo Caldera, Daniele Fava, Martina Ferrando and Francesco Causone ()
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Samuele Lozza: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Matteo Caldera: Department of Energy Technologies and Renewable Sources, Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA), Via Stezzano 87, 24126 Bergamo, Italy
Daniele Fava: Department of Energy Technologies and Renewable Sources, Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA), Via Stezzano 87, 24126 Bergamo, Italy
Martina Ferrando: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Francesco Causone: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

Energies, 2025, vol. 18, issue 9, 1-26

Abstract: In a constantly evolving context where non-dispatchable renewable energy sources (RESs) are becoming increasingly widespread, the role of Renewable Energy Communities (RECs) is gaining momentum as a way to promote distributed self-consumption. The spreading of RECs in the European Union (EU) has been supported by the development of technical tools. RECON (Renewable Energy Community ecONomic simulator) is one the most used simulation tools for energy, economic, and financial pre-assessments of RECs in Italy. This software requires, as an input to simulation, the electrical energy use profiles of buildings to estimate the shared energy and, consequently, to calculate economic incentives. However, the availability of reference electrical energy use profiles remains limited, especially when it comes to specific uses such as for organized large-scale retail. To address this issue, this study developed a data-driven model capable of generating electrical energy use profiles specific for organized large-scale retail, using a limited number of inputs, and thereby, addressing the gap in current energy modeling practices. The model validation showed that the model replicates electric energy profiles, on an annual basis, with an average deviation below 5% using minimal inputs. Monthly simulations exhibit greater discrepancies during colder months, which suggest further refinement opportunities.

Keywords: renewable energy communities; electric energy use profiles; retail buildings electric consumption; profile modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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