Renewable Energy Communities (RECs) hold great promise as a key driver in the global shift toward sustainable energy systems. To realize their full potential, it is essential to evaluate their performance effectively and ensure their long-term sustainability. Key Performance Indicators (KPIs) play a pivotal role in this process, offering a structured way to measure the success of RECs across critical dimensions such as energy production, economic viability, social, and environmental impact. KPIs not only quantify REC achievements but also provide early insights into emerging trends and challenges. This enables stakeholders to make informed, data-driven decisions that optimize performance and contribute to long-term success. Recognizing the need for a more systematic approach to REC performance evaluation, this study conducts an extensive literature review, examining over 200 research papers to identify and categorize the most relevant KPIs to consider. The main contribution of this research work is a KPI Reference List, featuring 25 indicators that make a comprehensive toolkit for assessing REC performance across diverse operational areas. The KPIs were categorized across four key Sector Domains, energy, economic, social, and environmental, and assessed for their applicability across various Usages such as planning, operations, monitoring, and benchmarking. Additionally, KPIs were selected to address the specific needs of specific Target groups, including policymakers, REC managers, stakeholders, and community members. This research aims to deepen the understanding of REC evaluation but also highlights the ongoing need for thoughtful refinement of KPIs to better capture the complexities of these communities, and to avoid restricted and partial analyses.
Assessment of renewable energy communities: A comprehensive review of key performance indicators / Giannuzzo, Lorenzo; Minuto, Francesco Demetrio; Schiera, Daniele Salvatore; Branchetti, Samuele; Petrovich, Carlo; Gessa, Nicola; Frascella, Angelo; Lanzini, Andrea. - In: ENERGY REPORTS. - ISSN 2352-4847. - ELETTRONICO. - 13:(2025), pp. 6609-6630. [10.1016/j.egyr.2025.05.082]
Assessment of renewable energy communities: A comprehensive review of key performance indicators
Lorenzo Giannuzzo;Francesco Demetrio Minuto;Daniele Salvatore Schiera;Andrea Lanzini
2025
Abstract
Renewable Energy Communities (RECs) hold great promise as a key driver in the global shift toward sustainable energy systems. To realize their full potential, it is essential to evaluate their performance effectively and ensure their long-term sustainability. Key Performance Indicators (KPIs) play a pivotal role in this process, offering a structured way to measure the success of RECs across critical dimensions such as energy production, economic viability, social, and environmental impact. KPIs not only quantify REC achievements but also provide early insights into emerging trends and challenges. This enables stakeholders to make informed, data-driven decisions that optimize performance and contribute to long-term success. Recognizing the need for a more systematic approach to REC performance evaluation, this study conducts an extensive literature review, examining over 200 research papers to identify and categorize the most relevant KPIs to consider. The main contribution of this research work is a KPI Reference List, featuring 25 indicators that make a comprehensive toolkit for assessing REC performance across diverse operational areas. The KPIs were categorized across four key Sector Domains, energy, economic, social, and environmental, and assessed for their applicability across various Usages such as planning, operations, monitoring, and benchmarking. Additionally, KPIs were selected to address the specific needs of specific Target groups, including policymakers, REC managers, stakeholders, and community members. This research aims to deepen the understanding of REC evaluation but also highlights the ongoing need for thoughtful refinement of KPIs to better capture the complexities of these communities, and to avoid restricted and partial analyses.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/3000634