Simulation Tools for Renewable Energy Communities: A Comparative Multi-Scenario Analysis in Residential Contexts with High Energy Sharing Potential

Renewable Energy Communities (RECs) represent a key instrument for enabling decentralized energy systems and enhancing local renewable energy utilization. Preliminary assessment of REC performance relies on simulation tools that differ in computational complexity, assumptions, and input data. Despite the growing literature, a systematic comparison of tools applied to identical community configurations is still missing. This study provides a systematic cross-comparison of four tools representing different modelling paradigms: a VBA-based prefeasibility model (MERCm), a MATLAB-based detailed framework (UNIPGm), a national open-access simulator (RECON), and a commercial platform (COMMm). The tools were applied to six residential configurations in three Italian provinces representing different solar irradiation levels. Scenarios are defined to ensure high energy sharing potential, considering a ratio of shared energy to energy fed into the grid above 60%. Key performance indicators, including physical self-consumption and shared energy, are analyzed. Results show broadly consistent trends across tools, although these findings refer to PV-only, residential RECs and may differ in more complex community configurations, with coefficients of variation below 15% for most relevant indicators, particularly shared energy, while confirming that differences in input data and modelling assumptions can still influence outcomes. These findings support the reliability of simplified simulation tools for preliminary REC feasibility assessments and provide guidance for policymakers and technical operators.

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Publication Details

Journal
Energies
Published
2026-03-17
DOI
https://doi.org/10.3390/en19061490
Primary Topic
Smart Grid Energy Management
Type
article
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article

Simulation Tools for Renewable Energy Communities: A Comparative Multi-Scenario Analysis in Residential Contexts with High Energy Sharing Potential

Andrea Presciutti, Laura Martiniello, Lucia Fagotti, Elisa Moretti
Energies
Smart Grid Energy Management
article

Simulation Tools for Renewable Energy Communities: A Comparative Multi-Scenario Analysis in Residential Contexts with High Energy Sharing Potential

Andrea Presciutti, Laura Martiniello, Lucia Fagotti, Elisa Moretti
article en

Abstract

Renewable Energy Communities (RECs) represent a key instrument for enabling decentralized energy systems and enhancing local renewable energy utilization. Preliminary assessment of REC performance relies on simulation tools that differ in computational complexity, assumptions, and input data. Despite the growing literature, a systematic comparison of tools applied to identical community configurations is still missing. This study provides a systematic cross-comparison of four tools representing different modelling paradigms: a VBA-based prefeasibility model (MERCm), a MATLAB-based detailed framework (UNIPGm), a national open-access simulator (RECON), and a commercial platform (COMMm). The tools were applied to six residential configurations in three Italian provinces representing different solar irradiation levels. Scenarios are defined to ensure high energy sharing potential, considering a ratio of shared energy to energy fed into the grid above 60%. Key performance indicators, including physical self-consumption and shared energy, are analyzed. Results show broadly consistent trends across tools, although these findings refer to PV-only, residential RECs and may differ in more complex community configurations, with coefficients of variation below 15% for most relevant indicators, particularly shared energy, while confirming that differences in input data and modelling assumptions can still influence outcomes. These findings support the reliability of simplified simulation tools for preliminary REC feasibility assessments and provide guidance for policymakers and technical operators.

EnergiesVol. 19(6)
University of Perugia (IT), Mercatorum University (IT)
Affordable and clean energy
Openalex Percentile: Top 19%
Smart Grid Energy Management
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