IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1198-d1602680.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1198/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1198/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ye, Li & Yang, Deling & Dang, Yaoguo & Wang, Junjie, 2022. "An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions," Energy, Elsevier, vol. 249(C).
    2. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
    3. Arouna, Aminou & Fatognon, Irene Akoko & Saito, Kazuki & Futakuchi, Koichi, 2021. "Moving toward rice self-sufficiency in sub-Saharan Africa by 2030: Lessons learned from 10 years of the Coalition for African Rice Development," World Development Perspectives, Elsevier, vol. 21(C).
    4. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    5. Magdalena Tutak & Jarosław Brodny, 2019. "Forecasting Methane Emissions from Hard Coal Mines Including the Methane Drainage Process," Energies, MDPI, vol. 12(20), pages 1-28, October.
    6. Pruethsan Sutthichaimethee & Grzegorz Mentel & Volodymyr Voloshyn & Halyna Mishchuk & Yuriy Bilan, 2024. "Modeling the Efficiency of Resource Consumption Management in Construction Under Sustainability Policy: Enriching the DSEM-ARIMA Model," Sustainability, MDPI, vol. 16(24), pages 1-17, December.
    7. Mahdi Asadi & Iman Larki & Mohammad Mahdi Forootan & Rouhollah Ahmadi & Meisam Farajollahi, 2023. "Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation," Sustainability, MDPI, vol. 15(5), pages 1-24, March.
    8. Ma, Haoran, 2022. "Prediction of industrial power consumption in Jiangsu Province by regression model of time variable," Energy, Elsevier, vol. 239(PB).
    9. Duan, Tianyao & Guo, Huan & Qi, Xiao & Sun, Ming & Forrest, Jeffrey, 2024. "A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China," Energy, Elsevier, vol. 304(C).
    10. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
    11. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada María & Ribeiro-Navarrete, Belén, 2018. "Does social network sentiment influence the relationship between the S&P 500 and gold returns?," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 57-64.
    12. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    13. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    14. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    15. GRITLI, Mohamed Ilyes, 2018. "Quel avenir du dinar tunisien face à l'euro ? Prévision avec le modèle ARIMA [What future of the Tunisian dinar against the euro? Prediction with the ARIMA model]," MPRA Paper 83937, University Library of Munich, Germany.
    16. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
    17. Hua Song & Huaizhi Chen & Yanbo Wang & Xiang-E Sun, 2024. "An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions," Energies, MDPI, vol. 17(23), pages 1-26, December.
    18. Aysha Malik & Ejaz Hussain & Sofia Baig & Muhammad Fahim Khokhar, 2020. "Forecasting CO2 emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 380-389, April.
    19. Minglu Ma & Zhuangzhuang Wang, 2019. "Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models," Energies, MDPI, vol. 13(1), pages 1-15, December.
    20. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1198-:d:1602680. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.