Bases para el desarrollo y aplicación de Gemelos Digitales en la industria de la energía eléctrica

Main Article Content

Gonzalo Alvarez
Dan Kröhling
Ernesto Martinez

Abstract

Nowadays, the expansion and improvement of electric power systems is carried out in a comprehensive manner. This means that the incorporation of new technologies and approaches are being sought in the current systems to implement Industry 4.0. As a result, one of the technologies that has gained importance in recent years is the so-called digital twins.


A digital twin is a partial or complete virtual representation of a physical system or process that evolves continuously along with the real system or process. A vehicle, a wind turbine or an entire city can be represented by digital twins. To implement and operate these digital twins, it is necessary to use sensors in the physical system or process to collect real-time information of the operating state so that the simulated behavior can be adapted.


This study aims to promote the application of digital twin technology in electrical systems, as well as discuss the challenges for its implementation. A proposal is presented for the development and application of digital twin technology in different fields, preferably in the field of power generation. The objective of this study is to provide a reference for the applications of digital twin technology in the smart energy industry.

Article Details

How to Cite
Alvarez, G., Kröhling, D., & Martinez, E. (2023). Bases para el desarrollo y aplicación de Gemelos Digitales en la industria de la energía eléctrica. Ingenio Tecnológico, 5, e043. Retrieved from https://ingenio.frlp.utn.edu.ar/index.php/ingenio/article/view/82
Section
Trabajos destacados del “XI Seminario de Energía y su Uso Eficiente”

References

Al-Waisi, Z., & Agyeman, M. O. (2018). On the Challenges and Opportunities of Smart Meters in Smart Homes and Smart Grids. Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, 1–6. https://doi.org/10.1145/3284557.3284561

Alvarez, G. (2022). Integrated modeling of the peer-to-peer markets in the energy industry. International Journal of Industrial Engineering Computations, 13(1), 101–118. https://doi.org/10.5267/j.ijiec.2021.7.002

Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817–820. https://doi.org/10.1177/2399808318796416

Bridge, G., & Gailing, L. (2020). New energy spaces: Towards a geographical political economy of energy transition. Environment and Planning A: Economy and Space, 52(6), 1037–1050. https://doi.org/10.1177/0308518X20939570

Cacciari, M., & Singhal, R. (2022, October 31). How Can Digital Technologies Help Companies Overcome the Decarbonization Challenges? Day 2 Tue, November 01, 2022. https://doi.org/10.2118/210980-MS

Chakraborty, S., & Adhikari, S. (2021). Machine learning based digital twin for dynamical systems with multiple time-scales. Computers & Structures, 243, 106410. https://doi.org/10.1016/j.compstruc.2020.106410

Chow, J. H., & Sanchez‐Gasca, J. J. (2019). Power System Modeling, Computation, and Control. Wiley. https://doi.org/10.1002/9781119546924

Cioara, T., Anghel, I., Antal, M., Salomie, I., Antal, C., & Ioan, A. G. (2021). An Overview of Digital Twins Application Domains in Smart Energy Grid. http://arxiv.org/abs/2104.07904

Darbali-Zamora, R., Johnson, J., Summers, A., Jones, C. B., Hansen, C., & Showalter, C. (2021). State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin. Energies, 14(3), 774. https://doi.org/10.3390/en14030774

DHL. (2022). Digital Twins. Insights & Innovation. https://www.dhl.com/global-en/home/insights-and-innovation/thought-leadership/trend-reports/virtual-reality-digital-twins.html#:~:text=As unique%2C virtual representations of,maintain their assets more effectively.

Fichera, A., Pluchino, A., & Volpe, R. (2020). From self-consumption to decentralized distribution among prosumers: A model including technological, operational and spatial issues. Energy Conversion and Management, 217, 112932. https://doi.org/10.1016/j.enconman.2020.112932

Galuzin, V., Galitskaya, A., Grachev, S., Larukhin, V., Novichkov, D., Skobelev, P., & Zhilyaev, A. (2022). Autonomous Digital Twin of Enterprise: Method and Toolset for Knowledge-Based Multi-Agent Adaptive Management of Tasks and Resources in Real Time. Mathematics, 10(10), 1662. https://doi.org/10.3390/math10101662

Gelernter, D. (1991). Mirror Worlds: Or the Day Software Puts the Universe in a Shoebox...How It Will Happen and What It Will Mean. Oxford University Press.

Ghenai, C., Husein, L. A., Al Nahlawi, M., Hamid, A. K., & Bettayeb, M. (2022). Recent trends of digital twin technologies in the energy sector: A comprehensive review. Sustainable Energy Technologies and Assessments, 54, 102837. https://doi.org/10.1016/j.seta.2022.102837

Grieves, M. (2016). Origins of the Digital Twin Concept. Florida Institute of Technology / NASA. https://doi.org/10.13140/RG.2.2.26367.61609

Hamid, I., Alam, M. S., Kanwal, A., Jena, P. K., Murshed, M., & Alam, R. (2022). Decarbonization pathways: the roles of foreign direct investments, governance, democracy, economic growth, and renewable energy transition. Environmental Science and Pollution Research, 29(33), 49816–49831. https://doi.org/10.1007/s11356-022-18935-3

Hart, W. E., Watson, J.-P., & Woodruff, D. L. (2011). Pyomo: modeling and solving mathematical programs in Python. Mathematical Programming Computation, 3(3), 219–260. https://doi.org/10.1007/s12532-011-0026-8

Kaur, M. J., Mishra, V. P., & Maheshwari, P. (2020). The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action (pp. 3–17). https://doi.org/10.1007/978-3-030-18732-3_1

Kröhling, D. E., Chiotti, O. J. A., & Martínez, E. C. (2023). Artificial Theory of Mind in contextual automated negotiations within peer-to-peer markets. Engineering Applications of Artificial Intelligence, 120, 105887. https://doi.org/10.1016/j.engappai.2023.105887

Kröhling, D. E., & Martínez, E. C. (2019). Contract Settlements for Exchanging Utilities through Automated Negotiations between Prosumers in Eco-Industrial Parks using Reinforcement Learning (pp. 1675–1680). https://doi.org/10.1016/B978-0-12-818634-3.50280-0

Kröhling, D. E., Mione, F., Hernández, F., & Martínez, E. C. (2022). A peer-to-peer market for utility exchanges in Eco-Industrial Parks using automated negotiations. Expert Systems with Applications, 191(September 2021), 116211. https://doi.org/10.1016/j.eswa.2021.116211

Ladj, A., Wang, Z., Meski, O., Belkadi, F., Ritou, M., & Da Cunha, C. (2021). A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective. Journal of Manufacturing Systems, 58(August), 168–179. https://doi.org/10.1016/j.jmsy.2020.07.018

McKenna, E., & Thomson, M. (2016). High-resolution stochastic integrated thermal–electrical domestic demand model. Applied Energy, 165, 445–461. https://doi.org/10.1016/j.apenergy.2015.12.089

Palensky, P., Cvetkovic, M., Gusain, D., & Joseph, A. (2022). Digital twins and their use in future power systems. Digital Twin, 1, 4. https://doi.org/10.12688/digitaltwin.17435.2

Papathanassiou, S., Hatziargyriou, N., & Strunz, K. (2005). A benchmark low voltage microgrid network. Proceedings of the CIGRE Symposium: Power Systems with Dispersed Generation. CIGRE, 1–8.

Plewnia, F. (2019). The Energy System and the Sharing Economy: Interfaces and Overlaps and what to Learn from them. Energies, 12(3), 339. https://doi.org/10.3390/en12030339

Podvalny, S. L., & Vasiljev, E. M. (2021). Digital twin for smart electricity distribution networks. IOP Conference Series: Materials Science and Engineering, 1035(1), 012047. https://doi.org/10.1088/1757-899X/1035/1/012047

Qiao, H., Zheng, F., Jiang, H., & Dong, K. (2019). The greenhouse effect of the agriculture-economic growth-renewable energy nexus: Evidence from G20 countries. Science of The Total Environment, 671, 722–731. https://doi.org/10.1016/j.scitotenv.2019.03.336

Randles, B. M., Pasquetto, I. V., Golshan, M. S., & Borgman, C. L. (2017). Using the Jupyter Notebook as a Tool for Open Science: An Empirical Study. 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 1–2. https://doi.org/10.1109/JCDL.2017.7991618

So much ice is melting that Earth’s crust is moving. (2021). Nature, 597(7874), 10–10. https://doi.org/10.1038/d41586-021-02285-0

Tao, F., & Qi, Q. (2019). Make more digital twins. Nature, 573(7775), 490–491. https://doi.org/10.1038/d41586-019-02849-1

Tollefson, J. (2021). IPCC climate report: Earth is warmer than it’s been in 125,000 years. Nature, 596(7871), 171–172. https://doi.org/10.1038/d41586-021-02179-1

Trauer, J., Schweigert-Recksiek, S., Engel, C., Spreitzer, K., & Zimmermann, M. (2020). WHAT IS A DIGITAL TWIN? – DEFINITIONS AND INSIGHTS FROM AN INDUSTRIAL CASE STUDY IN TECHNICAL PRODUCT DEVELOPMENT. Proceedings of the Design Society: DESIGN Conference, 1, 757–766. https://doi.org/10.1017/dsd.2020.15

Zhang, G., Huo, C., Zheng, L., & Li, X. (2020). An Architecture Based on Digital Twins for Smart Power Distribution System. 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), 29–33. https://doi.org/10.1109/ICAIBD49809.2020.9137461