Revista UIS Ingenierías

Vol. 23 No. 2 (2024): Revista UIS Ingenierías
Articles

Study of the use and contribution of artificial intelligence to the operation in electric power girds

PDF (Español (España))
  • Luis Ferney Ortiz-Torres ,
  • Eduardo Gómez-Luna,
  • Eduardo Marlés Sáenz
Luis Ferney Ortiz-Torres
Universidad del Valle
Eduardo Gómez-Luna
Universidad del Valle
Eduardo Marlés Sáenz
Universidad del Valle
DOI: https://doi.org/10.18273/revuin.v23n2-2024003

Published 2024-04-23

Keywords

  • machine learning,
  • data,
  • artificial intelligence,
  • power grid,
  • technique,
  • tecnology
    • ...More
    Less

How to Cite

Ortiz-Torres , L. F. ., Gómez-Luna, E., & Sáenz , E. M. (2024). Study of the use and contribution of artificial intelligence to the operation in electric power girds. Revista UIS Ingenierías, 23(2), 31–46. https://doi.org/10.18273/revuin.v23n2-2024003

Copyright (c) 2024 Revista UIS Ingenierías

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Abstract

The objective of this study is to show the current panorama of the operation of electrical grids with the influence of artificial intelligence (AI), which through its techniques and algorithms that support it, has been making significant contributions. A wide literature was covered, managing to show the perspective of its benefits, and its contribution in general, highlighting its different uses and contributions for compliance in the operation of power grids. The study shows characteristics and uses of AI in power distribution system operation. It associates the techniques that have been most highlighted to contribute to the operation of electrical distribution networks, generating value from large amounts of data. It highlights the necessary processes for the implementation of AI in electric grids comprising physical, human and virtual elements. The advantages projected by AI include efficiency, comfort and reliability, and among its disadvantages were identified the lack of: hardware, software, regulatory policy, security, scarce updating of technologies and human training. It was found that AI in power grids requires digitalization in order to enable all its benefits and generate greater resilience in these. Finally, it was concluded that AI is a key tool for the present and future operation of electric grids, where the incorporation of technologies, hardware, software, regulatory policies and human training is recommended, in order to take the step towards a more optimal and decentralized progress.

PDF (Español (España))

Downloads

Download data is not yet available.

References

  1. S. Das, T. K. Choudhury, S. K. Mohapatra, “Data analytics to increase efficiency of the AI based energy consumption predictor”, 2020 International Conference on Computational Intelligence for Smart Power System and Sustainable Energy (CISPSSE), 2020, pp. 1-4, doi: https://doi.org/10.1109/CISPSSE49931.2020.9212200
  2. A. Ameyoud, R. Touileb, M. Boudour, “Interest of Power System Data Analysis with Renewable Energy Sources”, 2019 Algerian Large Electrical Network Conference (CAGRE), 2019, pp. 1-6, doi: https://doi.org/10.1109/CAGRE.2019.8713310
  3. L. Lugnani, M. R. A. Paternina, D. Dotta, J. H. Chow, Y. Liu, “Power System Coherency Detection From Wide-Area Measurements by Typicality-Based Data Analysis”, IEEE Transactions on Power Systems, vol. 37, n.o 1, pp. 388-401, 2022, doi: https://doi.org/10.1109/TPWRS.2021.3088261
  4. D. Feng, T. Wang, C. Liu, S. Su, “Extracting Log Patterns Based on Association Analysis for Power Quality Disturbance Detection”, 2017 14th Web Information Systems and Applications Conference (WISA), 2017, pp. 80-83, doi: https://doi.org/10.1109/WISA.2017.15
  5. S. Mitra, B. Chakraborty, P. Mitra, “Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions”, Energy, vol. 289, p. 129920, 2024, doi: https://doi.org/10.1016/j.energy.2023.129920
  6. D. Osorio-Vásquez, S. Pérez-Londoño, J. Mora-Flórez, “New data-based load modeling for active distribution networks”, Revista UIS Ingenierías, vol. 22, n.o 4, pp. 93-102, 2023, doi: https://doi.org/10.18273/revuin.v22n4
  7. A. Woodie, “Global DataSphere to Hit 175 Zettabytes by 2025, IDC Says”. [En línea]. Disponible en: https://www.datanami.com/2018/11/27/globaldatasphere-to-hit-175-zettabytes-by-2025-idc-says/
  8. J. Taylor, “Inteligencia artificial e Internet de la energía (IoE)”, SparkCognition, 2017. [En línea]. Disponible en: https://www.ourenergypolicy.org/wpcontent/uploads/2017/08/SparkCognition_Artificial_Intelligence_and_the_Internet_of_Energy__IoE_.pdf
  9. A. Rhodes, “Digitalisation of Energy An Energy Futures Lab Briefing Paper”, 2020. [En línea]. Disponible en: http://imperial.ac.uk/energy-futures-lab
  10. D. Amodei, D. Hernandez, “AI and Compute”, OPENAI. [En línea]. Disponible en: https://openai.com/blog/ai-and-compute/
  11. L. Hu, Y. Miao, G. Wu, M. M. Hassan, I. Humar, “iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing”, Future Generation Computer Systems, vol. 90, pp. 569-577, 2019, doi: https://doi.org/10.1016/j.future.2018.08.006
  12. F. Li, Y. Shi, A. Shinde, J. Ye, W. Song, “Enhanced Cyber-Physical Security in Internet of Things Through Energy Auditing”, IEEE Internet Things J, vol. 6, n.o 3, pp. 5224-5231, 2019, doi: https://doi.org/10.1109/JIOT.2019.2899492
  13. A. Sahu, V. Venkatraman, R. Macwan, “Reinforcement Learning Environment for CyberResilient Power Distribution System”, IEEE Access, vol. 11, pp. 127216-127228, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3282182
  14. M. Marzband, F. Azarinejadian, M. Savaghebi, E. Pouresmaeil, J. M. Guerrero, G. Lightbody, “Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations”, Renew Energy, vol. 126, pp. 95-106, 2018, doi: https://doi.org/10.1016/j.renene.2018.03.021
  15. M. Qin, W. Hu, X. Qi, T. Chang, “Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy”, Energy Econ, vol. 131, p. 107403, 2024, doi: https://doi.org/10.1016/j.eneco.2024.107403
  16. N. Shaukat et al., “A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid”, Renewable and Sustainable Energy Reviews, vol. 81. pp. 1453-1475, 2018, doi: https://doi.org/10.1016/j.rser.2017.05.208
  17. X. Zhang, K. Khan, X. Shao, C. Oprean-Stan, Q. Zhang, “The rising role of artificial intelligence in renewable energy development in China”, Energy Econ, vol. 132, p. 107489, 2024, doi: https://doi.org/10.1016/j.eneco.2024.107489
  18. General Electric, “Digital Wind Operations Optimization from GE Renewable Energy”, 2017.
  19. K. Nandi, B. Chatterjee, S. Dalai, “Classification of Power Quality Disturbances using Artificial Intelligence: A Review”, 2023 IEEE 3rd Applied Signal Processing Conference (ASPCON), 2023, pp. 171-175. doi: https://doi.org/10.1109/ASPCON59071.2023.10395948
  20. C.M. Medina, J.C. Blandón A., C.M. Zapata, J. I. Ríos, “IoT Best Practices and their Components: A Systematic Literature Review”, EEE LATIN AMERICA TRANSACTIONS, vol. 20, n.o 10, 2022.
  21. K. G. di Santo, S. G. di Santo, R. M. Monaro, y M. A. Saidel, “Active demand side management for households in smart grids using optimization and artificial intelligence”, Measurement (Lond), vol. 115, pp. 152-161, 2018, doi: https://doi.org/10.1016/j.measurement.2017.10.010
  22. Y. Yang, Q. Guo, Z. He, H. Gao, “Artificial Intelligence Based Fault Diagnosis and Analysis in the Distribution Network”, en 2023 Panda Forum on Power and Energy (PandaFPE), 2023, pp. 1771-1775, doi: https://doi.org/10.1109/PandaFPE57779.2023.10140738
  23. M. Zakizadeh y M. Zand, “Transforming the Energy Sector: Unleashing the Potential of AI-Driven Energy Intelligence, Energy Business Intelligence, and Energy Management System for Enhanced Efficiency and Sustainability”, en 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 2024, pp. 1-7, doi: https://doi.org/10.1109/AISP61396.2024.10475298
  24. U. Lubo-Matallana, A. Marquez-Martínez, “DERs-Load Flow Convergence Sensitivity Analysis Using Topological Reconfiguration”, Revista UIS Ingenierías, vol. 23, no. 1, 2024, doi: https://doi.org/10.18273/revuin.v23n1-2024001
  25. T. Ahmad et al., “Energetics Systems and artificial intelligence: Applications of industry 4.0”, Energy Reports, vol. 8. pp. 334-361, 2022. doi: https://doi.org/10.1016/j.egyr.2021.11.256
  26. B. Makala, T. Bakovic, “Artificial Intelligence in the Power Sector”, Int Financ Corp, 2020.
  27. W. Luan, J. Peng, M. Maras, J. Lo, B. Harapnuk, “Smart Meter Data Analytics for Distribution Network Connectivity Verification”, IEEE Trans Smart Grid, vol. 6, n.o 4, pp. 1964 - 1971, 2015, doi: https://doi.org/10.1109/TSG.2015.2421304
  28. IEA, “Smart Grids”, 2021. [En línea]. Disponible en: https://www.iea.org/reports/smart-grids
  29. F. Dkhichi, B. Oukarfi, “Intelligent control in photovoltaic systems by neural network”, 2015 Intelligent Systems and Computer Vision (ISCV), 2015, pp. 1-5. doi: https://doi.org/10.1109/ISACV.2015.7106181
  30. P. Delanoë, D. Tchuente, G. Colin, “Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions”, J Environ Manage, vol. 331, 2023, doi: https://doi.org/10.1016/j.jenvman.2023.117261
  31. T. S. Sidhu, Z. Ao, “On-line evaluation of capacity and energy losses in power transmission systems by using artificial neural networks”, IEEE Transactions on Power Delivery, vol. 10, no. 4, doi: https://doi.org/10.1109/61.473363
  32. K. Jha, A. G. Shaik, “A comprehensive review of power quality mitigation in the scenario of solar PV integration into utility grid”, Electronics and Energy, vol. 3, 2023, doi: https://doi.org/10.1016/j.prime.2022.100103
  33. M. M. Hosseini y M. Parvania, “Artificial intelligence for resilience enhancement of power distribution systems”, Electricity Journal, vol. 34, no. 1, 2021, doi: https://doi.org/10.1016/j.tej.2020.106880
  34. R. Franco-Manrique, E. Gómez-Luna, C. A. Ramos-Sánchez, “Smart grid analysis and management in Colombia towards ETAP Real Time solution”, Revista chilena de ingeniería, 2017.
  35. S. Stock, D. Babazadeh, y C. Becker, “Applications of artificial intelligence in distribution power system operation”, IEEE Access, vol. 9, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3125102
  36. A. Paudel, K. Chaudhari, C. Long, H. B. Gooi, “Peer-to-Peer Energy Trading in a Prosumer-Based Community Microgrid: A Game-Theoretic Model”, IEEE Transactions on Industrial Electronics, vol. 66, no. 8, pp. 6087-6097, 2019, doi: https://doi.org/10.1109/TIE.2018.2874578
  37. W. Shi, X. Han, X. Wang, S. Gu, Y. Hao, “Application of Artificial Intelligence and Its Interpretability Analysis in Power Grid Dispatch and Control”, en 2023 8th Asia Conference on Power and Electrical Engineering (ACPEE), 2023, doi: https://doi.org/10.1109/ACPEE56931.2023.10135998
  38. C. Zhang et al., “A multifunctional ternary Cu (II) carboxylate coordination polymeric nanocomplex for cancer thermochemotherapy”, Int J Pharm, vol. 549, 2018, doi: https://doi.org/10.1016/j.ijpharm.2018.06.048
  39. M. A. Khan, A. M. Saleh, M. Waseem, I. A. Sajjad, “Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment”, IEEE Access, vol. 11, 2023, doi: https://doi.org/10.1109/ACCESS.2022.3231444
  40. H. Quan, A. Khosravi, D. Yang, D. Srinivasan, “A Survey of Computational Intelligence Techniques for Wind Power Uncertainty Quantification in Smart Grids”, IEEE Trans Neural Netw Learn Syst, vol. 31, no. 11, pp. 4582-4599, 2020, doi: https://doi.org/10.1109/TNNLS.2019.2956195
  41. V. Sámano-Ortega et al., “Electrical energy consumption monitoring system in the residential sector using IoT”, IEEE LAT AM T, vol. 21, no. 1, pp. 158–166, Oct. 2022.
  42. T. Ahmad, H. Chen, “Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches”, Energy Build, vol. 166, pp. 460-476, 2018, doi: https://doi.org/10.1016/j.enbuild.2018.01.066
  43. T. Ahmad et al., “Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities”, J Clean Prod, vol. 289, p. 125834, 2021, doi: https://doi.org/10.1016/j.jclepro.2021.125834
  44. K. Zhou, C. Fu, S. Yang, “Big data driven smart energy management: From big data to big insights”, Renewable and Sustainable Energy Reviews, vol. 56, pp. 215-225, 2016, doi: https://doi.org/10.1016/j.rser.2015.11.050
  45. C. Li, “Deep Reinforcement Learning”, Reinforcement Learning for Cyber-Physical Systems, pp. 125-154, 2019, doi: https://doi.org/10.1201/9781351006620-6
  46. S. Zhao, F. Blaabjerg, H. Wang, “An Overview of Artificial Intelligence Applications for Power Electronics”, IEEE Trans Power Electron, vol. 36, n.o 4, pp. 4633 - 4658, 2021, doi: https://doi.org/10.1109/TPEL.2020.3024914
  47. D. P. Kingma, J. L. Ba, “Adam: A method for stochastic optimization”, 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1-15, 2015.
  48. A. E. Lazzaretti et al., “A monitoring system for online fault detection and classification in photovoltaic plants”, Sensors (Switzerland), vol. 20, pp. 1-30, 2020, doi: https://doi.org/10.3390/s20174688
  49. L. M. Singkang et al., “Model based-testing of spatial and time domain artificial intelligence smart antenna for ultra-high frequency electric discharge detection in digital power substations”, Progress In Electromagnetics Research M, vol. 99, pp. 91-101, 2021, doi: https://doi.org/ 10.2528/pierm20090301.
  50. L. Abualigah, A. Diabat, “Advances in Sine Cosine Algorithm: A comprehensive survey,” vol. 54, Artif Intell Rev, 2021, doi: https://doi.org/10.1007/s10462-020-09909-3
  51. S. S. Mohar, S. Goyal, R. Kaur, “Fruit Fly Optimization Algorithm for Intelligent IoT Applications”, Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications, pp. 287-309, 2020, doi: https://doi.org/10.1002/9781119670087.ch16
  52. T. Nurcahyadi, C. Blum, “Adding negative learning to ant colony optimization: A comprehensive study”, Mathematics, vol. 9, no. 4, pp. 1-23, 2021, doi: https://doi.org/10.3390/math9040361
  53. S. Katoch, S. S. Chauhan, V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications, vol. 80, no. 5, 2021, doi: https://doi.org/10.1007/s11042-020-10139-6
  54. V. Yadav, A. K. Yadav, M. Kaur, D. Singh, “Trigonometric mutation and successful-parent-selection based adaptive asynchronous differential evolution”, J. Ambient Intell Humaniz Comput, 2021, doi: https://doi.org/10.1007/s12652-021-03269-8
  55. J. K. Pattanaik, M. Basu, D. P. Dash, “Optimal power flow with FACTS devices using artificial immune systems”, Proceedings of 2017 IEEE International Conference on Technological Advancements in Power and Energy: Exploring Energy Solutions for an Intelligent Power Grid, TAP Energy 2017, pp. 1-6, 2018, doi: https://doi.org/10.1109/TAPENERGY.2017.8397213
  56. R. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, M. Fischl, “Artificial intelligence in supply chain management: A systematic literature review”, J Bus Res, vol. 122, 2021, doi: https://doi.org/10.1016/j.jbusres.2020.09.009
  57. EK Burke y G. Kendall, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer: New York, 2014.
  58. W. Deng, R. Yao, H. Zhao, X. Yang, G. Li, “A novel intelligent diagnosis method using optimal LSSVM with improved PSO algorithm”, Soft comput, vol. 23, n.o 7, pp. 2445-2462, 2019.
  59. B. Naama, H. Bouzeboudja, A. Allali, “Application of Tabu Search and Genetic Algorithm in Minimize Losses in Power System. Using the BCoefficient Method”, Energy Procedia, vol. 36, pp. 687693, 2013, doi: https://doi.org/10.1016/j.egypro.2013.07.079
  60. J. Olamaei, T. Niknam, S. B. Arefi, “Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm”, Energy Procedia, 2012, pp. 304-311. doi: https://doi.org/10.1016/j.egypro.2011.12.934
  61. W. Hou, J. Li, J. Xu, K. Y. Lee, Y. Huang, “Visual-detection based fruit fly optimization algorithm for robust analysis of integrated energy systems”, en IFAC-PapersOnLine, 2020, doi: https://doi.org/10.1016/j.ifacol.2020.12.801
  62. K. R. Chowdhary, Fundamentals of artificial intelligence. Springer, 2020.
  63. L. Caponetti, Fuzzy Logic for image processing A gentle introduction Using Java. Springer, 2017.
  64. L. Yin, S. Luo, Y. Wang, F. Gao, J. Yu, “Coordinated complex-valued encoding dragonfly algorithm and artificial emotional reinforcement learning for coordinated secondary voltage control and automatic voltage regulation in multi-generator power systems”, IEEE Access, vol. 8, pp. 180520-180533, 2020.
  65. M. Zhou, Y. Wang, A. K. Srivastava, Y. Wu, P. Banerjee, “Ensemble-based algorithm for synchrophasor data anomaly detection”, IEEE Trans Smart Grid, vol. 10, no. 3, pp. 2979-2988, 2018.
  66. I. Srivastava, S. Bhat, V. S. G. Thadikemalla, A. R. Singh, “A hybrid machine learning and meta‐heuristic algorithm based service restoration scheme for radial power distribution system”, International Transactions on Electrical Energy Systems, vol. 31, no. 6, p. e12894, 2021.
  67. H. B. Guliyev, “Reactive power adaptive control system in networks with distributed generation based on fuzzy set theory”, 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 2019, pp. 1-5.
  68. C. Hotz, C. Becker, “Online monitoring of power system small signal stability using artificial neural networks”, NEIS 2019; Conference on Sustainable Energy Supply and Energy Storage Systems, VDE, 2019, pp. 1-6.
  69. L. Chao, Z. Lei, L. Yuhang, “Topology checking method for low voltage distribution network based on fuzzy C-means clustering algorithm”, 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), IEEE, 2020, pp. 10771080.
  70. X. Wang, Y. Wang, D. Shi, J. Wang, Z. Wang, “Two-stage WECC composite load modeling: A double deep Q-learning networks approach”, IEEE Trans Smart Grid, vol. 11, no. 5, pp. 4331-4344, 2020.
  71. K. v. Blazakis, T. N. Kapetanakis, y G. S. Stavrakakis, “Effective electricity theft detection in power distribution grids using an adaptive neuro fuzzy inference system”, Energies (Basel), vol. 13, no. 12, , 2020, doi: https://doi.org/10.3390/en13123110
  72. K. Andresen, Design and Use Patterns of Adaptability in Enterprise Systems, vol. 5. GITO mbH Verlag, 2006.
  73. N. Hatziargyriou et al., “Definition and classification of power system stability–revisited & extended”, IEEE Transactions on Power Systems, vol. 36, no. 4, pp. 3271-3281, 2020.
  74. S. Möws, V. Scheffer, C. Becker, “Probabilistic power forecast of renewable distributed generation for provision of control reserve using vine copulas”, IET Generation, Transmission & Distribution, vol. 14, no. 25, pp. 6312-6318, 2020.
  75. R. Monaco, C. Bergaentzlé, J. A. Leiva Vilaplana, E. Ackom, P. S. Nielsen, “Digitalization of power distribution grids: Barrier analysis, ranking and policy recommendations”, Energy Policy, vol. 188, p. 114083, 2024, doi: https://doi.org/10.1016/j.enpol.2024.114083
  76. K. Zhou, C. Fu, S. Yang, “Big data driven smart energy management: From big data to big insights”, Renewable and Sustainable Energy Reviews, vol. 56, pp. 215-225, 2016.
  77. European Smart Grids Task Force, “Interoperability, Standards and Functionalities applied in the large scale roll out of smart metering”. 2015.
  78. L. P. Ciminelli, “Electricity Without Borders”, Vital Speeches Day, vol. 70, n.o 11, p. 344, 2004.
  79. S. Paul, A. Poudyal, S. Poudel, A. Dubey, Z. Wang, “Resilience assessment and planning in power distribution systems: Past and future considerations”, Renewable and Sustainable Energy Reviews, vol. 189, p. 113991, 2024, doi: https://doi.org/10.1016/j.rser.2023.113991
  80. R. A. Moise y A. Fratu, “Artificial Intelligence in Power Distribution Systems”, en Smart Mobile Communication & Artificial Intelligence, M. E. Auer y T. Tsiatsos, Eds., Cham: Springer Nature Switzerland, 2024, pp. 151-159.
  81. M. Chui et al., “Notes from the AI frontier: Applications and value of deep learning”, McKinsey global institute discussion paper, April, 2018.
  82. E. Parliament, “Directive (EU) 2016/1148 of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union”, Official Journal of the European Union, Office for Official Publications of the European Union Luxembourg, 2016.
  83. OEA, “Un abordaje integral de la Ciberseguridad”, 2019.
  84. David Ramírez Morán, “Ciberseguridad en China”, 2017. [En línea]. Disponible en: https://www.rsaconference.com/writable/presentations/f ile_upload/law-w04
  85. Victor Meza Jimenez y Ernesto Perez, “The Role of Artificial Intelligence in Latin America’s Energy Transition”, 2404 IEEE Latin America Transactions,vol. 20, 2022.
  86. International Renewable Energy Agency, “Artificial Intelligence and Big Data: Innovation Landscape Brief”, Irena, p. 24, 2019.
  87. M. A. Ahmed, “Design and Development of Audio Processing and Speech Recognition Algorithm”, en 2021 Seventh International Conference on Aerospace Science and Engineering (ICASE), 2021, pp. 1-7, doi: https://doi.org/10.1109/ICASE54940.2021.9904277
  88. A. Yarali, “Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home”, en Intelligent Connectivity: AI, IoT, and 5G IEEE, 2022, pp. 239 - 249, doi: https://doi.org/10.1002/9781119685265.ch13
  89. N. Amurova, S. Abdullaeva, Y. Borisova, I. Narzullayev, I. Siddikov, “Development of Current Converters in the Power Supply Control and Management System Using Renewable Energy Sources Through Artificial Intelligence in the Sphere of Telecommunications”, 2021 International Conference on Information Science and Communications Technologies (ICISCT), 2021, pp. 1-5, doi: https://doi.org/10.1109/ICISCT52966.2021.9670128
  90. Z. Li, M. Shahidehpour, F. Aminifar, “Cybersecurity in Distributed Power Systems”, Proceedings of the IEEE, vol. 105, n.o 7, 2017, doi: https://doi.org/10.1109/JPROC.2017.2687865
  91. Y. Wang, “Towards Energy-Efficient Systems for Artificial Intelligence in the Future”, 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 2020, doi: https://doi.org/10.1109/ICCICC50026.2020.9450224
  92. S. Kokin, N. Djagarov, U. Bumtsend, J. Ahyoev, S. Dmitriev, M. Safaraliev, “Optimization of electric power system modes by methods of artificial intelligence”, 2020 21st International Scientific Conference on Electric Power Engineering (EPE), 2020, doi: https://doi.org/10.1109/EPE51172.2020.9269195
  93. Y. Shu-jun, S. Xiao-yan, W. Yan, Y. Yu-xin, Y. Zhi, “Research on dynamic characteristics of Unified Power Flow Controller (UPFC)”, 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011, doi: https://doi.org/10.1109/DRPT.2011.5993940
  94. F. Heymann, H. Quest, T. Lopez Garcia, C. Ballif, M. Galus, “Reviewing 40 years of artificial intelligence applied to power systems – A taxonomic perspective”, Energy and AI, vol. 15, p. 100322, 2024, doi: https://doi.org/10.1016/j.egyai.2023.100322
  95. Banco Interamericano de Desarrollo, “Tecnologías de Inteligencia Artificial (AI) en el Mantenimiento de Activos del Sector Eléctrico”, 2023.
  96. “Anexo 1-Guía práctica para incorporar la digitalización en los proyectos del sector de la energía eléctrica”.
  97. P. M. Attia et al., “Closed-loop optimization of fast-charging protocols for batteries with machine learning”, Nature, vol. 578, n.o 7795, pp. 397-402, 2020, doi: https://doi.org/10.1038/s41586-020-1994-5
  98. U. S. DOE, “DOE global energy storage database”, [En línea]. Disponible en: https://gesdb.sandia.gov/
  99. J. Li, B. Pradhan, S. Gaur, J. Thomas, “Predictions and Strategies Learned from Machine Learning to Develop High-Performing Perovskite Solar Cells,” Advanced Energy Materials, 2019, doi: https://doi.org/10.1002/aenm.201901891
  100. E.E Gaona, T. Morales Vega, C.L Trujillo, F. Santamaría, “Esquemas de trasmisión de datos en una Microrred a través de una Infraestructura de medición avanzada”, Revista UIS Ingenierías, vol. 15, no. 2, pp. 8592, ene. 2017, doi: https://doi.org/10.18273/revuin.v15n2-2016007
  101. F. Gutiérrez, F. Almenares, L. Calderón, E. Romero, “Prospectiva de seguridad de las redes de sensores inalámbricos”, Revista UIS Ingenierías, vol. 20, n.o 3, 2021, doi: https://doi.org/10.18273/revuin.v20n3-2021014
  102. J. M. Grothoff, “Battery storage for renewables: market status and technology outlook”, International Renewable Energy Agency (IRENA), Abu Dhabi, 2015.
  103. M. Andoni et al., “Blockchain technology in the energy sector: A systematic review of challenges and opportunities”, Renewable and Sustainable Energy Reviews, vol. 100, pp. 143-174, 2019, doi: https://doi.org/10.1016/j.rser.2018.10.014
  104. K. D. Thoben, S. A. Wiesner, T. Wuest, ““Industrie 4.0” and smart manufacturing-a review of research issues and application examples”, International Journal of Automation Technology, vol. 11, no. 1, pp. 416, 2017, doi: https://doi.org/10.20965/ijat.2017.p0004
  105. S. Phuyal, D. Bista, y R. Bista, “Challenges, Opportunities and Future Directions of Smart Manufacturing: A State of Art Review”, Sustainable Futures, vol. 2, p. 100023, 2020, doi: https://doi.org/10.1016/j.sftr.2020.100023
  106. Y. Guo et al., “Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving”, Appl Energy, vol. 225, pp. 732-745, 2018, doi: https://doi.org/10.1016/j.apenergy.2018.05.075
  107. Y. Guo et al., “An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems”, Appl Therm Eng, vol. 149, pp. 1223-1235, 2019, doi: https://doi.org/10.1016/j.applthermaleng.2018.12.132
  108. A. C. Şerban, M. D. Lytras, “Artificial Intelligence for Smart Renewable Energy Sector in Europe—Smart Energy Infrastructures for Next Generation Smart Cities”, IEEE Access, vol. 8, pp. 77364 - 77377, 2020, doi: https://doi.org/10.1109/ACCESS.2020.2990123
  109. A. Ashfaq, M. Kamran, F. Rehman, N. Sarfaraz, H. U. Ilyas, H. H. Riaz, “Role of Artificial Intelligence in Renewable Energy and its Scope in Future”, 2022 5th International Conference on Energy Conservation and Efficiency (ICECE), 2022, pp. 1-6, doi: https://doi.org/10.1109/ICECE54634.2022.9758957
  110. A. de Paola, G. L. Re, M. Morana, M. Ortolani, “SmartBuildings: an AmI system for energy efficiency”, 2015 Sustainable Internet and ICT for Sustainability (SustainIT), 2015, pp. 1-7, doi: https://doi.org/10.1109/SustainIT.2015.7101372
  111. B. A. Wokoma, E. N. Osegi, “A Resonant Fault Current Limiting Prediction Technique based onAuditory Machine Intelligence”, en 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf), 2019, pp. 1-5. doi: https://doi.org/10.1109/NigeriaComputConf45974.2019.8949653
  112. M. J. B. Reddy, D. v Rajesh, P. Gopakumar, D. K. Mohanta, “Smart Fault Location for Smart Grid Operation Using RTUs and Computational Intelligence Techniques”, IEEE Syst J, vol. 8, n.o 4, 2014, doi: https://doi.org/10.1109/JSYST.2014.2303833
  113. L. Fernando Rueda-Vásquez, J. Guillermo Barrero-Pérez, C. Duarte, “El conmutador inteligente de potencia y la sub-medición por circuito como herramientas para la gestión energética residencial”, Revista UIS Ingenierías, vol. 16, núm. 1, pp. 35-46, 2017.
  114. J. M. Sánchez, M. P. González, M. P. Sánchez, “La Sociedad de la Información: Génesis, Iniciativas, Concepto y su Relación con Las TIC”, Revista UIS Ingenierías, vol. 11, 2012.
  115. D. I. Cruz, “Guía específica de trabajo sobre: "Por qué la transición energética necesita a la inteligencia artificial?” Fundación San Patricio, 2023. [En línea]. Disponible en: https://www.programainvestiga.org/pdf/guias2023-2024/GuiaintroductoriaaltemaPorquelatransicionenergeticanecesitaalainteligenciaartificial.pdf