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A Predictive Framework for Photovoltaic Waste Quantities and Recovery Values: Insights and Application to the Italian Context Cover

A Predictive Framework for Photovoltaic Waste Quantities and Recovery Values: Insights and Application to the Italian Context

Open Access
|Jul 2024

Abstract

The global surge in photovoltaic (PV) panel deployment since the 2000s has contributed to advancing the renewable energy sector. However, this proliferation raises concerns about the increasing number of PV modules that will end their operational life in the coming years, necessitating effective planning for their decommissioning and recovery. This paper addresses this imminent challenge by presenting a predictive model to estimate the volume of decommissioned PV modules from existing installations. To consider the variability associated with the operational life duration of PV panels, two different scenarios were considered: early loss and regular loss, both modelled through the Weibull function. Furthermore, the article proposes a methodology for the economic valorization of materials recovered from decommissioned PV modules, according to the different technologies employed. This approach encourages sustainable practices by assigning an economic value to recovered materials and promoting a circular economy in the renewable energy sector. The economic valuation methodology adds practicality to dismantling, emphasising responsible waste management’s potential economic benefits. To illustrate the applicability of the model, the study focuses on the Italian case, providing a detailed regional breakdown. The regional analysis not only improves the accuracy of the predictive model but also offers insights into localised PV module disposal patterns. By adapting the methodology to the individual Italian regions, the article serves as a concrete and valuable resource during the programming and planning phases, facilitating the implementation of a strategy to efficiently recover PV modules and minimising the environmental impact associated with decommissioning activities.

DOI: https://doi.org/10.2478/rtuect-2024-0020 | Journal eISSN: 2255-8837 | Journal ISSN: 1691-5208
Language: English
Page range: 243 - 257
Submitted on: Apr 1, 2024
Accepted on: May 13, 2024
Published on: Jul 17, 2024
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2024 Andrea Franzoni, Chiara Leggerini, Mariasole Bannò, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.