Bibliometric Study of Digital Twins in the Solar Energy Sector
Bibliometric Study of Digital Twins in the Solar Energy Sector
DOI:
https://doi.org/10.51473/rcmos.v1i2.2025.1777Keywords:
Digital Twins, Bibliometrics, OptimizationAbstract
Digital Twins (DTs) represent an emerging technology capable of emulating real systems in virtual environments, offering promising solutions for the energy sector. This work presents a bibliometric analysis of the application of Digital Twins in the solar energy sector, aiming to identify trends, gaps, and main research topics. The methodology was based on data collection from the Web of Science and Scopus databases, covering the period from 2009 to 2024, followed by term correlation processing using the VOSviewer software. The results indicate that the topic is recent and growing, with a predominant focus on systems management, performance optimization, and intensive use of Machine Learning for solar resource and power forecasting. It is concluded that research in this area is intrinsically linked to the modernization of power plants and operational efficiency, pointing to a vast field of future investigations.
Downloads
References
ARTETXE, E.; URALDE, J.; BARAMBONES, O.; CALVO, I.; MARTIN, I. Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin. Mathematics, v. 11, 2023. DOI: 10.3390/math11092166. DOI: https://doi.org/10.3390/math11092166
BROADUS, R. N. Toward a definition of “bibliometrics.” Scientometrics, v. 12, p. 373–379, 1987. DOI: 10.1007/BF02016680. DOI: https://doi.org/10.1007/BF02016680
CHENG, T.; ZHU, X.; YANG, F.; WANG, W. Machine learning enabled learning-based optimization algorithm in digital twin simulator for management of smart islanded solar-based microgrids. Solar Energy, v. 250, p. 241–247, 2023. DOI: 10.1016/j.solener.2022.12.040. DOI: https://doi.org/10.1016/j.solener.2022.12.040
GHENAI, C.; HUSEIN, L. A.; AL NAHLAWI, M.; HAMID, A. K.; BETTAYEB, M. Recent trends of digital twin technologies in the energy sector: A comprehensive review. Sustainable Energy Technologies and Assessments, v. 54, 2022. DOI: 10.1016/j.seta.2022.102837. DOI: https://doi.org/10.1016/j.seta.2022.102837
GUI, Y. et al. Automatic voltage regulation application for PV inverters in low-voltage distribution grids – A digital twin approach. International Journal of Electrical Power & Energy Systems, v. 149, 109022, 2023. DOI: 10.1016/j.ijepes.2023.109022. DOI: https://doi.org/10.1016/j.ijepes.2023.109022
GUO, Z. et al. Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System. Energies, v. 16, 2023. DOI: 10.3390/en16062806. DOI: https://doi.org/10.3390/en16062806
HONG, Y. Y.; PULA, R. A. Diagnosis of PV faults using digital twin and convolutional mixer with LoRa notification system. Energy Reports, v. 9, p. 1963–1976, 2023. DOI: 10.1016/j.egyr.2023.01.011. DOI: https://doi.org/10.1016/j.egyr.2023.01.011
HUANG, J.; KOROTEEV, D. D.; RYNKOVSKAYA, M. Machine learning-based demand response in PV-based smart home considering energy management in digital twin. Solar Energy, v. 252, p. 8–19, 2023. DOI: 10.1016/j.solener.2023.01.044. DOI: https://doi.org/10.1016/j.solener.2023.01.044
KHALED, N.; PATTEL, B.; SIDDIQUI, A. Digital twin model creation of solar panels. In: Digital Twin Development and Deployment on the Cloud. Elsevier, 2020. p. 137–162. DOI: 10.1016/b978-0-12-821631-6.00006-2. DOI: https://doi.org/10.1016/B978-0-12-821631-6.00006-2
LI, Y.; TAO, Q.; GONG, Y. Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids. Solar Energy, v. 251, p. 306–314, 2023. DOI: 10.1016/j.solener.2023.01.013. DOI: https://doi.org/10.1016/j.solener.2023.01.013
MINGERS, J.; LEYDESDORFF, L. A review of theory and practice in scientometrics. European Journal of Operational Research, v. 246, p. 1–19, 2015. DOI: 10.1016/j.ejor.2015.04.002. DOI: https://doi.org/10.1016/j.ejor.2015.04.002
NATGUNANATHAN, I. et al. Deakin Microgrid Digital Twin and Analysis of AI Models for Power Generation Prediction. Energy Conversion and Management: X, 100370, 2023. DOI: 10.1016/j.ecmx.2023.100370. DOI: https://doi.org/10.1016/j.ecmx.2023.100370
RAZO, D. E. G. et al. A genetic algorithm approach as a self-learning and optimization tool for PV power simulation and digital twinning. Energies, v. 13, 2020. DOI: 10.3390/en13246712. DOI: https://doi.org/10.3390/en13246712
SEHRAWAT, N.; VASHISHT, S.; SINGH, A. Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison. International Journal of Intelligent Networks, v. 4, p. 90–102, 2023. DOI: 10.1016/j.ijin.2023.04.001. DOI: https://doi.org/10.1016/j.ijin.2023.04.001
SEMERARO, C.; LEZOCHE, M.; PANETTO, H.; DASSISTI, M. Digital twin paradigm: A systematic literature review. Computers in Industry, v. 130, 2021. DOI: 10.1016/j.compind.2021.103469. DOI: https://doi.org/10.1016/j.compind.2021.103469
SLEITI, A. K.; KAPAT, J. S.; VESELY, L. Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Reports, v. 8, p. 3704–3726, 2022. DOI: 10.1016/j.egyr.2022.02.305. DOI: https://doi.org/10.1016/j.egyr.2022.02.305
TAO, F.; XIAO, B.; QI, Q.; CHENG, J.; JI, P. Digital twin modeling. Journal of Manufacturing Systems, v. 64, p. 372–389, 2022. DOI: 10.1016/j.jmsy.2022.06.015. DOI: https://doi.org/10.1016/j.jmsy.2022.06.015
TUOMIRANTA, A. et al. Auto-Parametrizing the Digital Twin of Photovoltaic Power Systems. In: 38th European Photovoltaic Solar Energy Conference and Exhibition, 2021. p. 1022–1027. DOI: 10.4229/EUPVSEC20212021-5DO.1.4.
ULLAH, S. M. S. et al. Techno-economic impacts of Volt-VAR control on the high penetration of solar PV interconnection. Cleaner Energy Systems, v. 5, 100067, 2023. DOI: 10.1016/j.cles.2023.100067. DOI: https://doi.org/10.1016/j.cles.2023.100067
YAO, J. F. et al. Systematic review of digital twin technology and applications. Visual Computing for Industry, Biomedicine, and Art, v. 6, 2023. DOI: 10.1186/s42492-023-00137-4. DOI: https://doi.org/10.1186/s42492-023-00137-4
YOU, M. et al. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, v. 305, 2022. DOI: 10.1016/j.apenergy.2021.117899. DOI: https://doi.org/10.1016/j.apenergy.2021.117899
YUAN, G.; XIE, F. Digital Twin-Based economic assessment of solar energy in smart microgrids using reinforcement learning technique. Solar Energy, v. 250, p. 398–408, 2023. DOI: 10.1016/j.solener.2022.12.031. DOI: https://doi.org/10.1016/j.solener.2022.12.031
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Lucas Emanuel Almeida Barboza (Autor)
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means you are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The use of this material is conditioned upon proper attribution to the original author(s), providing a link to the license, and indicating if changes were made. The license does not require permission from the author or the publisher as long as these conditions are met.
The Creative Commons license logo is displayed permanently in the footer of the journal.
The copyright of the manuscript can be retained by the authors without restrictions and can be requested at any time, even after publication in the journal.
