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Impact Assessment of Second-Life Batteries and Local Photovoltaics for Decarbonizing Enterprises Through System Digitalization and Energy Management

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  • Gerard Borrego-Orpinell

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Av. Diagonal 647, Planta 0, Pavelló G, 08028 Barcelona, Spain)

  • Jose-Fernando Forero

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Av. Diagonal 647, Planta 0, Pavelló G, 08028 Barcelona, Spain)

  • Adriano Caprara

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Av. Diagonal 647, Planta 0, Pavelló G, 08028 Barcelona, Spain)

  • Francisco Díaz-González

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Av. Diagonal 647, Planta 0, Pavelló G, 08028 Barcelona, Spain
    Serra Húnter Fellow.)

Abstract

This paper shows an impact assessment of second-life batteries (SLBs) and local photovoltaics (PV) for decarbonizing enterprises through system digitalization and energy management. SLBs from electric vehicles offer a cost-effective and environmentally sustainable energy storage solution for enterprises. These systems can significantly reduce fossil fuel dependence coupled with local PV installations. This paper proposes a methodology for developing the complete digital twin of an enterprise in combination with an optimization algorithm for energy management. This methodology can be applied to a wide range of enterprises across different sectors, both industrial and non-industrial, with diverse consumption patterns. A sensitivity analysis has been carried out to evaluate the potential of this methodology for enterprises in different contexts, where different battery sizes, PV installations, consumption types, and environmental prioritization policies are encountered. Findings indicate that combining SLBs and PV installation, supported by digital energy management, can substantially reduce carbon footprints and operational costs.

Suggested Citation

  • Gerard Borrego-Orpinell & Jose-Fernando Forero & Adriano Caprara & Francisco Díaz-González, 2025. "Impact Assessment of Second-Life Batteries and Local Photovoltaics for Decarbonizing Enterprises Through System Digitalization and Energy Management," Energies, MDPI, vol. 18(5), pages 1-31, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1198-:d:1602680
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    References listed on IDEAS

    as
    1. Luca Silvestri & Antonio Forcina & Cecilia Silvestri & Gabriella Arcese & Domenico Falcone, 2024. "Exploring the Environmental Benefits of an Open-Loop Circular Economy Strategy for Automotive Batteries in Industrial Applications," Energies, MDPI, vol. 17(7), pages 1-20, April.
    2. Mustafa Yucel & Sevgi Yucel, 2024. "Environmental, Social, and Governance (ESG) Dynamics in the Energy Sector: Strategic Approaches for Sustainable Development," Energies, MDPI, vol. 17(24), pages 1-19, December.
    3. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    4. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    5. Xiufeng Xing & Yingjia Cong & Yu Wang & Xueqing Wang, 2023. "The Impact of COVID-19 and War in Ukraine on Energy Prices of Oil and Natural Gas," Sustainability, MDPI, vol. 15(19), pages 1-16, September.
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