Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado

Los Sistemas de Energía Eléctrica (SEE) enfrentan interrupciones del servicio provocadas por eventos disruptivos deliberados, como ciberataques, que afectan significativamente la vulnerabilidad y la resiliencia del sistema. Estos ataques comprometen la capacidad del sistema para recuperarse y restab...

Full description

Autores:
Mosquera Palacios , Darin Jairo
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2024
Institución:
Universidad Distrital Francisco José de Caldas
Repositorio:
RIUD: repositorio U. Distrital
Idioma:
spa
OAI Identifier:
oai:repository.udistrital.edu.co:11349/94104
Acceso en línea:
http://hdl.handle.net/11349/94104
Palabra clave:
Vulnerabilidad
Evento Disruptivo
Resiliencia
Ciberataques
Algoritmo Genético
Vulnerability, Disruptive Event, Resilience, Cyberattacks, Genetic Algorithm.
Vulnerability
Disruptive event
Resilience
Cyberattacks
Genetic Algorithm
Rights
License
Abierto (Texto Completo)
id UDISTRITA2_c0d28a8e25c8c445cc20621542b89933
oai_identifier_str oai:repository.udistrital.edu.co:11349/94104
network_acronym_str UDISTRITA2
network_name_str RIUD: repositorio U. Distrital
repository_id_str
dc.title.none.fl_str_mv Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
dc.title.titleenglish.none.fl_str_mv Vulnerability and resilience analysis in electric power systems under the occurrence of a deliberate disruptive event
title Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
spellingShingle Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
Vulnerabilidad
Evento Disruptivo
Resiliencia
Ciberataques
Algoritmo Genético
Vulnerability, Disruptive Event, Resilience, Cyberattacks, Genetic Algorithm.
Vulnerability
Disruptive event
Resilience
Cyberattacks
Genetic Algorithm
title_short Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
title_full Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
title_fullStr Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
title_full_unstemmed Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
title_sort Análisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberado
dc.creator.fl_str_mv Mosquera Palacios , Darin Jairo
dc.contributor.advisor.none.fl_str_mv Rivas Trujillo, Edwin
dc.contributor.author.none.fl_str_mv Mosquera Palacios , Darin Jairo
dc.contributor.orcid.none.fl_str_mv Rivas Trujillo,Edwin [0000-0003-2372-8056]
dc.subject.none.fl_str_mv Vulnerabilidad
Evento Disruptivo
Resiliencia
Ciberataques
Algoritmo Genético
topic Vulnerabilidad
Evento Disruptivo
Resiliencia
Ciberataques
Algoritmo Genético
Vulnerability, Disruptive Event, Resilience, Cyberattacks, Genetic Algorithm.
Vulnerability
Disruptive event
Resilience
Cyberattacks
Genetic Algorithm
dc.subject.keyword.none.fl_str_mv Vulnerability, Disruptive Event, Resilience, Cyberattacks, Genetic Algorithm.
Vulnerability
Disruptive event
Resilience
Cyberattacks
Genetic Algorithm
description Los Sistemas de Energía Eléctrica (SEE) enfrentan interrupciones del servicio provocadas por eventos disruptivos deliberados, como ciberataques, que afectan significativamente la vulnerabilidad y la resiliencia del sistema. Estos ataques comprometen la capacidad del sistema para recuperarse y restablecer su funcionamiento normal, reduciendo su capacidad de respuesta y adaptación, lo que resulta en mayores pérdidas económicas y un impacto prolongado en las cargas críticas. En esta tesis Doctoral se propone un modelo de optimización, basado en el problema de interdicción, implementando un Algoritmo Genético (AG) que permitirá identificar puntos vulnerables del sistema (vectores de interdicción) y priorizar acciones de mitigación, minimizando la vulnerabilidad y maximizando la resiliencia. El análisis de vulnerabilidad, identificó los elementos más susceptibles a ser atacados, como lo son las líneas y generadores. El desarrollo del Algoritmo Genético (AG), extrajo la Matriz de Interdicción (MI) y el Vector de Interdicción (VI), que generan mayores costos al sistema ante un evento disruptivo. Se identificaron las estrategias más efectivas del agente disruptor al atacar líneas y generadores, así como la respuesta del Operador de Red (OR). Se establecieron cuatro escenarios, incluyendo el escenario base, en los cuales se aplicaron mecanismos de Respuesta a la Demanda (RD) y Generación Distribuida (GD)/Plantas de generación (PG), buscando mejorar la resiliencia del sistema. En el escenario del sistema de transmisión, al aplicar RD y PG, el sistema logró aumentar la carga atendida en un 82%, mientras que aplicando RD y PG por separado, el incremento fue del 77% y 66%, respectivamente. En el sistema de distribución, al aplicar RD y GD, el sistema logró aumentar la carga atendida en un 56%, mientras que aplicando RD y GD por separado, el incremento fue del 44% y 25% respectivamente. Además, se implementó una estrategia de reconfiguración topológica de la red eléctrica, sugiriendo configuraciones alternativas después de un evento disruptivo, con el objetivo de maximizar la resiliencia y minimizar los costos operativos y la vulnerabilidad. Finalmente, se establecieron métricas para cuantificar y cualificar la resiliencia de las acciones de mitigación del OR, reduciendo la pérdida de carga y sus costos, validadas a través de casos de estudio con redes de prueba IEEE. Entre las contribuciones, la más importante de esta tesis Doctoral es el desarrollo de una técnica de optimización basada en Algoritmo Genético (AG) que permite identificar los planes de ataque más dañinos y determinar las acciones óptimas de restauración de los elementos del sistema, como lo son las líneas y generadores.
publishDate 2024
dc.date.created.none.fl_str_mv 2024-11-22
dc.date.accessioned.none.fl_str_mv 2025-03-25T16:26:36Z
dc.date.available.none.fl_str_mv 2025-03-25T16:26:36Z
dc.type.none.fl_str_mv doctoralThesis
dc.type.degree.none.fl_str_mv Investigación-Innovación
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_db06
format http://purl.org/coar/resource_type/c_db06
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11349/94104
url http://hdl.handle.net/11349/94104
dc.language.iso.none.fl_str_mv spa
language spa
dc.relation.references.none.fl_str_mv Abbasi, S., Barati, M. y Lim, G. J. (2019). A Parallel Sectionalized Restoration Scheme for Resilient Smart Grid Systems. IEEE Transactions on Smart Grid, 10(2), 1660-1670. https://doi.org/10.1109/TSG.2017.2775523 Abedi, A., Gaudard, L. y Romerio, F. (2019). Review of major approaches to analyze vulnerability in power system. Reliability Engineering and System Safety, 183, 153-172. https://doi.org/10.1016/j.ress.2018.11.019 Abedi, A., Hesamzadeh, M. R. y Romerio, F. (2022). Adaptive robust vulnerability analysis of power systems under uncertainty: A multilevel OPF-based optimization approach. International Journal of Electrical Power & Energy Systems, 134, 1-13. https://doi.org/10.1016/j.ijepes.2021.107432 Abdelbary, A. M., Manglicmot, L., Kanwhen, O., & Mohamed, A. A. (2024). Community- centric distributed energy resources for energy justice and decarbonization in dense urban regions. Energy Reports, 11, 1742–1751. https://doi.org/10.1016/j.egyr.2024.01.023 Agrawal, P., Kanwar, N., Gupta, N., Niazi, K. R., & Swarnkar, A. (2021). Resiliency in active distribution systems via network reconfiguration. Sustainable Energy, Grids and Networks, 26. https://doi.org/10.1016/j.segan.2021.100434 Amirioun, M. H., Aminifar, F., Lesani, H. y Shahidehpour, M. (2019). Metrics and quantitative framework for assessing microgrid resilience against windstorms. International Journal of Electrical Power and Energy Systems, 104, 716-723. https://doi.org/10.1016/j.ijepes.2018.07.025 Angalaeswari, S., Sanjeevikumar, P., Jamuna, K., & Leonowicz, Z. (2020). Hybrid pipso- sqp algorithm for real power loss minimization in radial distribution systems with optimal placement of distributed generation. Sustainability (Switzerland), 12(14), 1– 21. https://doi.org/10.3390/su12145787 Bajwa, A. A., Mokhlis, H., Mekhilef, S. y Mubin, M. (2019). Enhancing power system resilience leveraging microgrids: A review. Journal of Renewable and Sustainable Energy, 11(3), 035503. https://doi.org/10.1063/1.5066264 Balaban, G., Dumbrava, V., Lazaroiu, A. C., & Kalogirou, S. (2024). Analysis of urban network operation in presence of renewable sources for decarbonization of energy system. Renewable Energy, 230. https://doi.org/10.1016/j.renene.2024.120870 Barbadilla, A. (2013). Evolución Biológica. Coloquios de Paleontología, 16, 11–11. Bawayan, H., & Younis, M. (2020). Mitigating failure propagation in microgrids through topology reconfiguration. Sustainable Energy, Grids and Networks, 23. https://doi.org/10.1016/j.segan.2020.100363 Biegler, L. T. (2024). Multi-level optimization strategies for large-scale nonlinear process systems. Computers and Chemical Engineering, 185. https://doi.org/10.1016/j.compchemeng.2024.108657 Bruckler, M., Wietschel, L., Messmann, L., Thorenz, A., & Tuma, A. (2024). Review of metrics to assess resilience capacities and actions for supply chain resilience. Computers and Industrial Engineering, 192. https://doi.org/10.1016/j.cie.2024.110176 Bhusal, N., Abdelmalak, M., Kamruzzaman, M. y Benidris, M. (2020). Power System Resilience: Current Practices, Challenges, and Future Directions. IEEE Access, 8, 18064-18086. https://doi.org/10.1109/ACCESS.2020.2968586 Cicirello, V. A. (2024). Evolutionary Computation: Theories, Techniques, and Applications. In Applied Sciences (Switzerland) (Vol. 14, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app14062542 Chalishazar, V., Poudel, S., Hanif, S. y Mana, P. T. (2021). Power System Resilience Metrics Augmentation for Critical Load Prioritization. U.S. Department of Energy. https://www.osti.gov/servlets/purl/1764623 Dakos, V. y Kéfi, S. (2022). Ecological resilience: What to measure and how. Environmental Research Letters, 17(4). https://doi.org/10.1088/1748-9326/ac5767 Fose, N., Singh, A. R., Krishnamurthy, S., Ratshitanga, M., & Moodley, P. (2024). Empowering distribution system operators: A review of distributed energy resource forecasting techniques. Heliyon, 10(15), e34800. https://doi.org/10.1016/j.heliyon.2024.e34800 Gilani, M. A., Kazemi, A. y Ghasemi, M. (2020). Distribution system resilience enhancement by microgrid formation considering distributed energy resources. Energy, 191, 116442. https://doi.org/10.1016/j.energy.2019.116442 González, P., Osorio, R. R., Pardo, X. C., Banga, J. R., & Doallo, R. (2022). An efficient ant colony optimization framework for HPC environments. Applied Soft Computing,114. https://doi.org/10.1016/j.asoc.2021.108058 Goyal, G., Kumar Srivastava, P., & Bisht, D. C. S. (2020). Genetic algorithm: a metaheuristic approach of optimization. In Computational Intelligence (pp. 27–44). De Gruyter. https://doi.org/10.1515/9783110671353-002 Guamán, A. y Valenzuela, A. (2021). Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms. Energies, 14(20), 6699. https://doi.org/10.3390/en14206699 Guimarães, I. G., Bernardon, D. P., Garcia, V. J., Schmitz, M., & Pfitscher, L. L. (2021). A decomposition heuristic algorithm for dynamic reconfiguration after contingency situations in distribution systems considering island operations. Electric Power Systems Research, 192. https://doi.org/10.1016/j.epsr.2020.106969 Haritha, P. C., & Anjaneyulu, M. V. L. R. (2024). Comparison of topological functionality- based resilience metrics using link criticality. Reliability Engineering and System Safety, 243. https://doi.org/10.1016/j.ress.2023.109881 Hasan, M. S., Chowdhury, M. M. U. T., & Kamalasadan, S. (2024). Sequential Quadratic Programming (SQP) Based Optimal Power Flow Methodologies for Electric Distribution System with High Penetration of DERs. IEEE Transactions on Industry Applications, 60(3), 4810–4820. https://doi.org/10.1109/TIA.2024.3371428 Hashemi, R., Kamranrad, R., Bagheri, F., & Emami, I. (2020). A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects). International Journal of Managing Projects in Business, 13(5), 1025–1050. https://doi.org/10.1108/IJMPB-04-2019-0089 Hernández-Valencia, J. P., Lopez-Lezama, J. M. y Restrepo-Cuestas, B. J. (2021). Assessing the Vulnerability of Power Systems Using Multilevel Programming: A Literature Review. Revista Ingenierías Universidad de Medellín, 20(38), 99-117. https://doi.org/10.22395/rium.v20n38a6 Hogan, K. F. E., Nash, K. L. y Bennett, E. (2021). Adaptive management of ecosystem services for multisystemic resilience: Iterative feedback between application and theory. Multisystemic Resilience: Adaptation and Transformation in Contexts of Change, 725-743. Oxford University Press. https://doi.org/10.1093/oso/9780190095888.003.0038 Ji, X., Yin, Z., Zhang, Y., Xu, B., & liu, Q. (2021). Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network. Electric Power Systems Research, 195. https://doi.org/10.1016/j.epsr.2021.107132 Jufri, F. H., Widiputra, V. y Jung, J. (2019). State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies. Applied Energy, 239, 1049-1065. https://doi.org/10.1016/j.apenergy.2019.02.017 Kahouli, O., Alsaif, H., Bouteraa, Y., Ben Ali, N. y Chaabene, M. (2021). Power System Reconfiguration in Distribution Network for Improving Reliability Using Genetic Algorithm and Particle Swarm Optimization. Applied Sciences, 11(7), 1-15. https://doi.org/10.3390/app11073092 Karabaş, T., & Meral, S. (2023). An exact solution method and a genetic algorithm-based approach for the unit commitment problem in conventional power generation systems. Computers and Industrial Engineering, 176. https://doi.org/10.1016/j.cie.2022.108876 Lai, K., Illindala, M. y Subramaniam, K. (2019). A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment. Applied Energy, 235, 204-218. https://doi.org/10.1016/j.apenergy.2018.10.077 Leterme, W., & Heylen, E. (2023). Computationally Efficient Electromagnetic Transient Power System Studies using Bayesian Optimization. http://arxiv.org/abs/2310.14945 Liu, Y., Peng, M., Gao, X. y Zhang, H. (2021). Probabilistic vulnerability assessment of transmission lines considering cascading failures. Processes, 9(11), 1-15. https://doi.org/10.3390/pr9111994 López-Lezama, J. P. H.-V. B. J. R.-C. J. M. (2020). A Bilevel Attacker-Defender Model for Enhancing Power Systems Resilience with Distributed Generation. Scientia et Technica, 25(4), 540–547. https://doi.org/10.22517/23447214.23721 Mitrovic, M. (2024). Data-Driven Stochastic AC-OPF using Gaussian Processes. http://arxiv.org/abs/2402.11365 López-Lezama, J. M., Galeano, J. C. C., & Trujillo, E. R. (2020). Assessment of the electric grid interdiction problem considering different network models. Journal of Applied Science and Engineering, 23(2), 175–183. https://doi.org/10.6180/jase.202006_23(2).0001 Ma, S., Li, S., Wang, Z. y Qiu, F. (2019). Resilience-Oriented Design of Distribution Systems. IEEE Transactions on Power Systems, 34(4), 2880-2891. https://doi.org/10.1109/TPWRS.2019.2894103 Mora, J. P., Samper, J., & Rodriguez, C. F. (2023). Bayesian optimization study for energy consuniptiou reduction of a parallel robot during pick and place tasks. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, 20(1), 1–12. https://doi.org/10.4995/riai.2022.16724 Mishra, D. K., Ghadi, M. J., Azizivahed, A., Li, L. y Zhang, J. (2021). A review on resilience studies in active distribution systems. Renewable and Sustainable Energy Reviews, 135(3), 110201. http://dx.doi.org/10.1016/j.rser.2020.110201 Nacar Cikan, N., & Cikan, M. (2024). Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2024.109796 Naderi, E., Pazouki, S. y Asrari, A. (2022). A Coordinated Cyberattack Targeting Load Centers and Renewable Distributed Energy Resources for Undervoltage/Overvoltage in the Most Vulnerable Regions of a Modern Distribution System. Sustainable Cities and Society, 88, 104276. https://doi.org/10.1016/j.scs.2022.104276 Naval, N., Sánchez, R. y Yusta, J. M. (2020). A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation. Renewable Energy, 151, 57-69. https://doi.org/10.1016/j.renene.2019.10.144 Nikolaou, N., Papadakis, A., Psychogyios, K. y Zahariadis, T. (2023). Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector. Electronics (Switzerland), 12(14), 1-18. https://doi.org/10.3390/electronics12143185 Oloulade, A., Imano, A. M., Fifatin, X., Vianou, A., Tamadaho, H., & Badarou, R. (2019). Multi-Objective Optimization of the Safe Operation of the Electrical Distribution System by Placing D-FACTS and Network Reconfiguration. Journal of Power and Energy Engineering, 07(09), 94–113. https://doi.org/10.4236/jpee.2019.79007 Oner, A. y Abur, A. (2019). Improving Resiliency of Power Grids during Extreme Events. 2018 North American Power Symposium, NAPS 2018, 1-5. https://doi.org/10.1109/NAPS.2018.8600632 Palacios, D. J. M., Trujillo, E. R., & López-Lezama, J. M. (2021). Vulnerability analysis to maximize the resilience of power systems considering demand response and distributed generation. Electronics (Switzerland), 10(12). https://doi.org/10.3390/electronics10121498 Pilatásig-Lasluisa, J. y Carrión, D. (2020). Resiliencia de sistemas eléctricos de potencia mediante la conmutación de líneas de transmission. Estado del arte. Revista de I+D Tecnológico, 16(2), 67-77. https://doi.org/10.33412/idt.v16.2.2834 Poulin, C., & Kane, M. B. (2021). Infrastructure resilience curves: Performance measures and summary metrics. In Reliability Engineering and System Safety (Vol. 216). Elsevier Ltd. https://doi.org/10.1016/j.ress.2021.107926 Pu, C., Wu, P. y Xia, Y. (2020). Vulnerability Assessment of Power Grids Against Link-Based Attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(10), 2209-2213. https://doi.org/10.1109/TCSII.2019.2958313 Rajwar, K., Deep, K., & Das, S. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56(11), 13187–13257. https://doi.org/10.1007/s10462- 023-10470-y Ramadan, H. S., & Helmi, A. M. (2021). Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions. Computers and Electrical Engineering, 93. https://doi.org/10.1016/j.compeleceng.2021.107310 Rocchetta, R. (2022). Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics. Renewable and Sustainable Energy Reviews, 159. https://doi.org/10.1016/j.rser.2022.112185 Ruan, G., Qiu, D., Sivaranjani, S., Awad, A. S. A., & Strbac, G. (2024). Data-driven energy management of virtual power plants: A review. In Advances in Applied Energy (Vol.14). Elsevier Ltd. https://doi.org/10.1016/j.adapen.2024.100170 Salau, A. O., Gebru, Y. W., & Bitew, D. (2020). Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04233 Salmeron, J., Wood, K., & Baldick, R. (2004). Analysis of electric grid security under terrorist threat. IEEE Transactions on Power Systems, 19(2), 905–912. https://doi.org/10.1109/TPWRS.2004.825888 Sermarini, A. C. de P., Azevedo, J. H. P., Albuquerque, V. C. de, Calili, R. F., Gonçalves, F., & Jannuzzi, G. (2024). Distributed energy resources in low-income communities: A public policy proposal for Brazil. Energy Policy, 187. https://doi.org/10.1016/j.enpol.2024.114030 Silos-Sánchez, A., Villafafila-Robles, R. y Lloret-Gallego, P. (2020). Novel fault location algorithm for meshed distribution networks with DERs. Electric Power Systems Research, 181, 106182. https://doi.org/10.1016/j.epsr.2019.106182 Shashank, S., Gurrala, G., Sastry, P. S., & Katewa, V. (2024). Cyber-Physical Modeling and Vulnerability Assessment of Substations for Transmission System Operator. Electric Power Systems Research, 235. https://doi.org/10.1016/j.epsr.2024.110769 Shezan, S. A., Kamwa, I., Ishraque, M. F., Muyeen, S. M., Hasan, K. N., Saidur, R., Rizvi, S. M., Shafiullah, M., & Al-Sulaiman, F. A. (2023). Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review. Energies, 16(4). https://doi.org/10.3390/en16041792 Shi, Q., Li, F., Olama, M., Dong, J., Xue, Y., Starke, M., Winstead, C., & Kuruganti, T. (2021). Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience. International Journal of Electrical Power and Energy Systems, 124. https://doi.org/10.1016/j.ijepes.2020.106355 Shiozaki, Y., Nagamatsu, S., Sato, K., & Bhattacharya, Y. (2024). A Systematic Literature Review of Empirical Validation of Disaster Resilience Indicators. International Journal of Disaster Risk Reduction, 104681. https://doi.org/10.1016/j.ijdrr.2024.104681 Su, Q., Sun, J., & Li, J. (2024). Vulnerability analysis of cyber-physical power systems based on failure propagation probability. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2024.109877 Sun, R., Liu, Y., Zhu, H., Azizipanah-Abarghooee, R., & Terzija, V. (2019). A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization. Applied Soft Computing Journal, 83. https://doi.org/10.1016/j.asoc.2019.105656 Tang, D., Fang, Y. P., & Zio, E. (2023). Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber-attacks and online detection methods. Reliability Engineering and System Safety, 235. https://doi.org/10.1016/j.ress.2023.109212 Tang, L., Han, Y., Zalhaf, A. S., Zhou, S., Yang, P., Wang, C., & Huang, T. (2024). Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies. In Renewable and Sustainable Energy Reviews (Vol. 189). Elsevier Ltd. https://doi.org/10.1016/j.rser.2023.113898 Tareq M. Shami, A. A. E. M. A. Q. A. (2022). Particle Swarm Optimization- A Comprehensive Survey. IEEE XPLORE, 10, 10031–10061. Tran The, T., Vo Ngoc, D., & Tran Anh, N. (2020). Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm. Complexity, 2020. https://doi.org/10.1155/2020/2353901 Tu, H., Xia, Y., Tse, C. K. y Chen, X. (2020). A Hybrid Cyber Attack Model for Cyber-Physical Power Systems. IEEE Access, 8, 114876-114883. https://doi.org/10.1109/ACCESS.2020.3003323. Uzum, B., Yoldas, Y., Bahceci, S., & Onen, A. (2024). Comprehensive review of transmission system operators–Distribution system operators collaboration for flexible grid operations. In Electric Power Systems Research (Vol. 227). Elsevier Ltd. https://doi.org/10.1016/j.epsr.2023.109976 Verdejo, H., Pino, V., Kliemann, W., Becker, C., & Delpiano, J. (2020). Implementation of particle swarm optimization (PSO) algorithm for tuning of power system stabilizers in multimachine electric power systems. Energies, 13(8). https://doi.org/10.3390/en13082093 Verma, R., Gawre, S. K., Patidar, N. P., & Nandanwar, S. (2024). A state of art review on the opportunities in automatic generation control of hybrid power system. In Electric Power Systems Research (Vol. 226). Elsevier Ltd. https://doi.org/10.1016/j.epsr.2023.109945 Wang, L., Zhu, Z., Jiang, C., & Li, Z. (2021). Bi-Level Robust Optimization for Distribution System with Multiple Microgrids Considering Uncertainty Distribution Locational Marginal Price. IEEE Transactions on Smart Grid, 12(2), 1104–1117. https://doi.org/10.1109/TSG.2020.3037556 Wen, J., Wan, C., Ye, Q., Yan, J. y Li, W. (2023). Disaster Risk Reduction, climate change adaptation and Their Linkages with Sustainable Development the Past 30 years: A Review. Int J Disaster Risk Sci, 14, 1-13. https://doi.org/10.1007/s13753-023-00472-3 Wu, X., Wu, S., Wu, J., Feng, L., & Tan, K. C. (2024). Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap. http://arxiv.org/abs/2401.10034 Yan, M., Ai, X., Shahidehpour, M., Li, Z., Wen, J., Bahramira, S. y Paaso, A. (2019). Enhancing the Transmission Grid Resilience in Ice Storms by Optimal Coordination of Power System Schedule with Pre-Positioning and Routing of Mobile DC De-Icing Devices. IEEE Transactions on Power Systems, 34(4), 2663-2674. https://doi.org/10.1109/TPWRS.2019.2899496 Xu, L. (2021). Research on computer interactive optimization design of power system based on genetic algorithm. Energy Reports, 7, 1–13. https://doi.org/10.1016/j.egyr.2021.10.085 Zhang, B., Dehghanian, P. y Kezunovic, M. (2019). Optimal Allocation of PV Generation and Battery Storage for Enhanced Resilience. IEEE Transactions on Smart Grid, 10(1), 535-545. DOI 10.1109/TSG.2017.2747136 Zimmerman, R. D., & Murillo-s, C. E. (2020). Matpower manual de usuario Version 7.1.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.none.fl_str_mv Abierto (Texto Completo)
rights_invalid_str_mv Abierto (Texto Completo)
http://purl.org/coar/access_right/c_abf2
dc.format.mimetype.none.fl_str_mv pdf
dc.publisher.none.fl_str_mv Universidad Distrital Francisco José de Caldas
publisher.none.fl_str_mv Universidad Distrital Francisco José de Caldas
institution Universidad Distrital Francisco José de Caldas
bitstream.url.fl_str_mv https://repository.udistrital.edu.co/bitstreams/d260bff8-da2a-465d-b9b9-96afbb6d9a3d/download
https://repository.udistrital.edu.co/bitstreams/6dd466d8-d868-452e-a29b-8f93ebada6a1/download
https://repository.udistrital.edu.co/bitstreams/f399f49b-2c92-4ba3-b6f6-6387ec701b88/download
https://repository.udistrital.edu.co/bitstreams/9794bbcc-83f3-4fca-9350-686c92cbc05a/download
https://repository.udistrital.edu.co/bitstreams/208166e9-ec33-4efd-8d69-51713a9cc1bc/download
bitstream.checksum.fl_str_mv 997daf6c648c962d566d7b082dac908d
4a38ffd7d6f3f1c7a4c11d19c8a7adf8
effa9a83bfab5bea4cc15c899b7b27a4
6f8ee9db9bd8dc494030561c9066c09c
6f0b171cfdd6920d19595f48807cd91d
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv Repositorio Universidad Distrital
repository.mail.fl_str_mv repositorio@udistrital.edu.co
_version_ 1837007054404845568
spelling Rivas Trujillo, EdwinMosquera Palacios , Darin JairoRivas Trujillo,Edwin [0000-0003-2372-8056]2025-03-25T16:26:36Z2025-03-25T16:26:36Z2024-11-22http://hdl.handle.net/11349/94104Los Sistemas de Energía Eléctrica (SEE) enfrentan interrupciones del servicio provocadas por eventos disruptivos deliberados, como ciberataques, que afectan significativamente la vulnerabilidad y la resiliencia del sistema. Estos ataques comprometen la capacidad del sistema para recuperarse y restablecer su funcionamiento normal, reduciendo su capacidad de respuesta y adaptación, lo que resulta en mayores pérdidas económicas y un impacto prolongado en las cargas críticas. En esta tesis Doctoral se propone un modelo de optimización, basado en el problema de interdicción, implementando un Algoritmo Genético (AG) que permitirá identificar puntos vulnerables del sistema (vectores de interdicción) y priorizar acciones de mitigación, minimizando la vulnerabilidad y maximizando la resiliencia. El análisis de vulnerabilidad, identificó los elementos más susceptibles a ser atacados, como lo son las líneas y generadores. El desarrollo del Algoritmo Genético (AG), extrajo la Matriz de Interdicción (MI) y el Vector de Interdicción (VI), que generan mayores costos al sistema ante un evento disruptivo. Se identificaron las estrategias más efectivas del agente disruptor al atacar líneas y generadores, así como la respuesta del Operador de Red (OR). Se establecieron cuatro escenarios, incluyendo el escenario base, en los cuales se aplicaron mecanismos de Respuesta a la Demanda (RD) y Generación Distribuida (GD)/Plantas de generación (PG), buscando mejorar la resiliencia del sistema. En el escenario del sistema de transmisión, al aplicar RD y PG, el sistema logró aumentar la carga atendida en un 82%, mientras que aplicando RD y PG por separado, el incremento fue del 77% y 66%, respectivamente. En el sistema de distribución, al aplicar RD y GD, el sistema logró aumentar la carga atendida en un 56%, mientras que aplicando RD y GD por separado, el incremento fue del 44% y 25% respectivamente. Además, se implementó una estrategia de reconfiguración topológica de la red eléctrica, sugiriendo configuraciones alternativas después de un evento disruptivo, con el objetivo de maximizar la resiliencia y minimizar los costos operativos y la vulnerabilidad. Finalmente, se establecieron métricas para cuantificar y cualificar la resiliencia de las acciones de mitigación del OR, reduciendo la pérdida de carga y sus costos, validadas a través de casos de estudio con redes de prueba IEEE. Entre las contribuciones, la más importante de esta tesis Doctoral es el desarrollo de una técnica de optimización basada en Algoritmo Genético (AG) que permite identificar los planes de ataque más dañinos y determinar las acciones óptimas de restauración de los elementos del sistema, como lo son las líneas y generadores.Electrical Power Systems (EPS) face service interruptions caused by deliberate disruptive events, such as cyberattacks, which significantly impact the system's vulnerability and resilience. These attacks compromise the system's ability to recover and restore normal operation, reducing its responsiveness and adaptability, leading to greater economic losses and prolonged impact on critical loads. This Doctoral thesis proposes an optimization model based on the interdiction problem, implementing a Genetic Algorithm (GA) that will identify the system's vulnerable points (interdiction vectors) and prioritize mitigation actions, minimizing vulnerability and maximizing resilience. The vulnerability analysis identified the most susceptible elements to attacks, such as lines and generators. The development of the Genetic Algorithm (GA) extracted the Interdiction Matrix (IM) and the Interdiction Vector (IV), which generate higher costs for the system in the event of a disruptive incident. The most effective strategies of the attacker were identified, targeting lines and generators, as well as the response of the Network Operator (NO). Four scenarios were established, including the base scenario, in which Demand Response (DR) mechanisms and Distributed Generation (DG)/Generation Plants (GP) were applied, aiming to improve the system's resilience. In the transmission system scenario, by applying DR and GP, the system managed to increase the load served by 69%, while applying DR and GP separately resulted in increases of 64% and 47%, respectively. In the distribution system, by applying DR and DG, the system managed to increase the load served by 56%, while applying DR and DG separately resulted in increases of 46% and 44%, respectively. Additionally, a topological reconfiguration strategy of the electrical network was implemented, suggesting alternative configurations after a disruptive event to maximize resilience and minimize operational costs and vulnerability. Finally, metrics were established to quantify and qualify the resilience of the NO's mitigation actions, reducing load loss and its costs, validated through case studies with IEEE test networks. Among the contributions, the most important contribution of this Doctoral thesis is the development of an optimization technique based on Genetic Algorithms (GA) that allows identifying the most damaging attack plans and determining the optimal restoration actions for system elements, such as lines and generators.Universidad Distrital Francisco José de CaldaspdfspaUniversidad Distrital Francisco José de CaldasVulnerabilidadEvento DisruptivoResilienciaCiberataquesAlgoritmo GenéticoVulnerability, Disruptive Event, Resilience, Cyberattacks, Genetic Algorithm.VulnerabilityDisruptive eventResilienceCyberattacksGenetic AlgorithmAnálisis de vulnerabilidad y resiliencia en sistemas de energía eléctrica ante la ocurrencia de un evento disruptivo deliberadoVulnerability and resilience analysis in electric power systems under the occurrence of a deliberate disruptive eventdoctoralThesisInvestigación-Innovacióninfo:eu-repo/semantics/doctoralThesishttp://purl.org/coar/resource_type/c_db06Abierto (Texto Completo)http://purl.org/coar/access_right/c_abf2Abbasi, S., Barati, M. y Lim, G. J. (2019). A Parallel Sectionalized Restoration Scheme for Resilient Smart Grid Systems. IEEE Transactions on Smart Grid, 10(2), 1660-1670. https://doi.org/10.1109/TSG.2017.2775523 Abedi, A., Gaudard, L. y Romerio, F. (2019). Review of major approaches to analyze vulnerability in power system. Reliability Engineering and System Safety, 183, 153-172. https://doi.org/10.1016/j.ress.2018.11.019 Abedi, A., Hesamzadeh, M. R. y Romerio, F. (2022). Adaptive robust vulnerability analysis of power systems under uncertainty: A multilevel OPF-based optimization approach. International Journal of Electrical Power & Energy Systems, 134, 1-13. https://doi.org/10.1016/j.ijepes.2021.107432 Abdelbary, A. M., Manglicmot, L., Kanwhen, O., & Mohamed, A. A. (2024). Community- centric distributed energy resources for energy justice and decarbonization in dense urban regions. Energy Reports, 11, 1742–1751. https://doi.org/10.1016/j.egyr.2024.01.023 Agrawal, P., Kanwar, N., Gupta, N., Niazi, K. R., & Swarnkar, A. (2021). Resiliency in active distribution systems via network reconfiguration. Sustainable Energy, Grids and Networks, 26. https://doi.org/10.1016/j.segan.2021.100434 Amirioun, M. H., Aminifar, F., Lesani, H. y Shahidehpour, M. (2019). Metrics and quantitative framework for assessing microgrid resilience against windstorms. International Journal of Electrical Power and Energy Systems, 104, 716-723. https://doi.org/10.1016/j.ijepes.2018.07.025 Angalaeswari, S., Sanjeevikumar, P., Jamuna, K., & Leonowicz, Z. (2020). Hybrid pipso- sqp algorithm for real power loss minimization in radial distribution systems with optimal placement of distributed generation. Sustainability (Switzerland), 12(14), 1– 21. https://doi.org/10.3390/su12145787 Bajwa, A. A., Mokhlis, H., Mekhilef, S. y Mubin, M. (2019). Enhancing power system resilience leveraging microgrids: A review. Journal of Renewable and Sustainable Energy, 11(3), 035503. https://doi.org/10.1063/1.5066264 Balaban, G., Dumbrava, V., Lazaroiu, A. C., & Kalogirou, S. (2024). Analysis of urban network operation in presence of renewable sources for decarbonization of energy system. Renewable Energy, 230. https://doi.org/10.1016/j.renene.2024.120870 Barbadilla, A. (2013). Evolución Biológica. Coloquios de Paleontología, 16, 11–11. Bawayan, H., & Younis, M. (2020). Mitigating failure propagation in microgrids through topology reconfiguration. Sustainable Energy, Grids and Networks, 23. https://doi.org/10.1016/j.segan.2020.100363 Biegler, L. T. (2024). Multi-level optimization strategies for large-scale nonlinear process systems. Computers and Chemical Engineering, 185. https://doi.org/10.1016/j.compchemeng.2024.108657 Bruckler, M., Wietschel, L., Messmann, L., Thorenz, A., & Tuma, A. (2024). Review of metrics to assess resilience capacities and actions for supply chain resilience. Computers and Industrial Engineering, 192. https://doi.org/10.1016/j.cie.2024.110176 Bhusal, N., Abdelmalak, M., Kamruzzaman, M. y Benidris, M. (2020). Power System Resilience: Current Practices, Challenges, and Future Directions. IEEE Access, 8, 18064-18086. https://doi.org/10.1109/ACCESS.2020.2968586 Cicirello, V. A. (2024). Evolutionary Computation: Theories, Techniques, and Applications. In Applied Sciences (Switzerland) (Vol. 14, Issue 6). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app14062542 Chalishazar, V., Poudel, S., Hanif, S. y Mana, P. T. (2021). Power System Resilience Metrics Augmentation for Critical Load Prioritization. U.S. Department of Energy. https://www.osti.gov/servlets/purl/1764623 Dakos, V. y Kéfi, S. (2022). Ecological resilience: What to measure and how. Environmental Research Letters, 17(4). https://doi.org/10.1088/1748-9326/ac5767 Fose, N., Singh, A. R., Krishnamurthy, S., Ratshitanga, M., & Moodley, P. (2024). Empowering distribution system operators: A review of distributed energy resource forecasting techniques. Heliyon, 10(15), e34800. https://doi.org/10.1016/j.heliyon.2024.e34800 Gilani, M. A., Kazemi, A. y Ghasemi, M. (2020). Distribution system resilience enhancement by microgrid formation considering distributed energy resources. Energy, 191, 116442. https://doi.org/10.1016/j.energy.2019.116442 González, P., Osorio, R. R., Pardo, X. C., Banga, J. R., & Doallo, R. (2022). An efficient ant colony optimization framework for HPC environments. Applied Soft Computing,114. https://doi.org/10.1016/j.asoc.2021.108058 Goyal, G., Kumar Srivastava, P., & Bisht, D. C. S. (2020). Genetic algorithm: a metaheuristic approach of optimization. In Computational Intelligence (pp. 27–44). De Gruyter. https://doi.org/10.1515/9783110671353-002 Guamán, A. y Valenzuela, A. (2021). Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms. Energies, 14(20), 6699. https://doi.org/10.3390/en14206699 Guimarães, I. G., Bernardon, D. P., Garcia, V. J., Schmitz, M., & Pfitscher, L. L. (2021). A decomposition heuristic algorithm for dynamic reconfiguration after contingency situations in distribution systems considering island operations. Electric Power Systems Research, 192. https://doi.org/10.1016/j.epsr.2020.106969 Haritha, P. C., & Anjaneyulu, M. V. L. R. (2024). Comparison of topological functionality- based resilience metrics using link criticality. Reliability Engineering and System Safety, 243. https://doi.org/10.1016/j.ress.2023.109881 Hasan, M. S., Chowdhury, M. M. U. T., & Kamalasadan, S. (2024). Sequential Quadratic Programming (SQP) Based Optimal Power Flow Methodologies for Electric Distribution System with High Penetration of DERs. IEEE Transactions on Industry Applications, 60(3), 4810–4820. https://doi.org/10.1109/TIA.2024.3371428 Hashemi, R., Kamranrad, R., Bagheri, F., & Emami, I. (2020). A fuzzy DEMATEL – Fuzzy Binary Logistic Regression approach to evaluate and prioritize risks and simulated annealing optimization algorithm (an empirical study in energy projects). International Journal of Managing Projects in Business, 13(5), 1025–1050. https://doi.org/10.1108/IJMPB-04-2019-0089 Hernández-Valencia, J. P., Lopez-Lezama, J. M. y Restrepo-Cuestas, B. J. (2021). Assessing the Vulnerability of Power Systems Using Multilevel Programming: A Literature Review. Revista Ingenierías Universidad de Medellín, 20(38), 99-117. https://doi.org/10.22395/rium.v20n38a6 Hogan, K. F. E., Nash, K. L. y Bennett, E. (2021). Adaptive management of ecosystem services for multisystemic resilience: Iterative feedback between application and theory. Multisystemic Resilience: Adaptation and Transformation in Contexts of Change, 725-743. Oxford University Press. https://doi.org/10.1093/oso/9780190095888.003.0038 Ji, X., Yin, Z., Zhang, Y., Xu, B., & liu, Q. (2021). Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network. Electric Power Systems Research, 195. https://doi.org/10.1016/j.epsr.2021.107132 Jufri, F. H., Widiputra, V. y Jung, J. (2019). State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies. Applied Energy, 239, 1049-1065. https://doi.org/10.1016/j.apenergy.2019.02.017 Kahouli, O., Alsaif, H., Bouteraa, Y., Ben Ali, N. y Chaabene, M. (2021). Power System Reconfiguration in Distribution Network for Improving Reliability Using Genetic Algorithm and Particle Swarm Optimization. Applied Sciences, 11(7), 1-15. https://doi.org/10.3390/app11073092 Karabaş, T., & Meral, S. (2023). An exact solution method and a genetic algorithm-based approach for the unit commitment problem in conventional power generation systems. Computers and Industrial Engineering, 176. https://doi.org/10.1016/j.cie.2022.108876 Lai, K., Illindala, M. y Subramaniam, K. (2019). A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment. Applied Energy, 235, 204-218. https://doi.org/10.1016/j.apenergy.2018.10.077 Leterme, W., & Heylen, E. (2023). Computationally Efficient Electromagnetic Transient Power System Studies using Bayesian Optimization. http://arxiv.org/abs/2310.14945 Liu, Y., Peng, M., Gao, X. y Zhang, H. (2021). Probabilistic vulnerability assessment of transmission lines considering cascading failures. Processes, 9(11), 1-15. https://doi.org/10.3390/pr9111994 López-Lezama, J. P. H.-V. B. J. R.-C. J. M. (2020). A Bilevel Attacker-Defender Model for Enhancing Power Systems Resilience with Distributed Generation. Scientia et Technica, 25(4), 540–547. https://doi.org/10.22517/23447214.23721 Mitrovic, M. (2024). Data-Driven Stochastic AC-OPF using Gaussian Processes. http://arxiv.org/abs/2402.11365 López-Lezama, J. M., Galeano, J. C. C., & Trujillo, E. R. (2020). Assessment of the electric grid interdiction problem considering different network models. Journal of Applied Science and Engineering, 23(2), 175–183. https://doi.org/10.6180/jase.202006_23(2).0001 Ma, S., Li, S., Wang, Z. y Qiu, F. (2019). Resilience-Oriented Design of Distribution Systems. IEEE Transactions on Power Systems, 34(4), 2880-2891. https://doi.org/10.1109/TPWRS.2019.2894103 Mora, J. P., Samper, J., & Rodriguez, C. F. (2023). Bayesian optimization study for energy consuniptiou reduction of a parallel robot during pick and place tasks. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, 20(1), 1–12. https://doi.org/10.4995/riai.2022.16724 Mishra, D. K., Ghadi, M. J., Azizivahed, A., Li, L. y Zhang, J. (2021). A review on resilience studies in active distribution systems. Renewable and Sustainable Energy Reviews, 135(3), 110201. http://dx.doi.org/10.1016/j.rser.2020.110201 Nacar Cikan, N., & Cikan, M. (2024). Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2024.109796 Naderi, E., Pazouki, S. y Asrari, A. (2022). A Coordinated Cyberattack Targeting Load Centers and Renewable Distributed Energy Resources for Undervoltage/Overvoltage in the Most Vulnerable Regions of a Modern Distribution System. Sustainable Cities and Society, 88, 104276. https://doi.org/10.1016/j.scs.2022.104276 Naval, N., Sánchez, R. y Yusta, J. M. (2020). A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation. Renewable Energy, 151, 57-69. https://doi.org/10.1016/j.renene.2019.10.144 Nikolaou, N., Papadakis, A., Psychogyios, K. y Zahariadis, T. (2023). Vulnerability Identification and Assessment for Critical Infrastructures in the Energy Sector. Electronics (Switzerland), 12(14), 1-18. https://doi.org/10.3390/electronics12143185 Oloulade, A., Imano, A. M., Fifatin, X., Vianou, A., Tamadaho, H., & Badarou, R. (2019). Multi-Objective Optimization of the Safe Operation of the Electrical Distribution System by Placing D-FACTS and Network Reconfiguration. Journal of Power and Energy Engineering, 07(09), 94–113. https://doi.org/10.4236/jpee.2019.79007 Oner, A. y Abur, A. (2019). Improving Resiliency of Power Grids during Extreme Events. 2018 North American Power Symposium, NAPS 2018, 1-5. https://doi.org/10.1109/NAPS.2018.8600632 Palacios, D. J. M., Trujillo, E. R., & López-Lezama, J. M. (2021). Vulnerability analysis to maximize the resilience of power systems considering demand response and distributed generation. Electronics (Switzerland), 10(12). https://doi.org/10.3390/electronics10121498 Pilatásig-Lasluisa, J. y Carrión, D. (2020). Resiliencia de sistemas eléctricos de potencia mediante la conmutación de líneas de transmission. Estado del arte. Revista de I+D Tecnológico, 16(2), 67-77. https://doi.org/10.33412/idt.v16.2.2834 Poulin, C., & Kane, M. B. (2021). Infrastructure resilience curves: Performance measures and summary metrics. In Reliability Engineering and System Safety (Vol. 216). Elsevier Ltd. https://doi.org/10.1016/j.ress.2021.107926 Pu, C., Wu, P. y Xia, Y. (2020). Vulnerability Assessment of Power Grids Against Link-Based Attacks. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(10), 2209-2213. https://doi.org/10.1109/TCSII.2019.2958313 Rajwar, K., Deep, K., & Das, S. (2023). An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artificial Intelligence Review, 56(11), 13187–13257. https://doi.org/10.1007/s10462- 023-10470-y Ramadan, H. S., & Helmi, A. M. (2021). Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions. Computers and Electrical Engineering, 93. https://doi.org/10.1016/j.compeleceng.2021.107310 Rocchetta, R. (2022). Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics. Renewable and Sustainable Energy Reviews, 159. https://doi.org/10.1016/j.rser.2022.112185 Ruan, G., Qiu, D., Sivaranjani, S., Awad, A. S. A., & Strbac, G. (2024). Data-driven energy management of virtual power plants: A review. In Advances in Applied Energy (Vol.14). Elsevier Ltd. https://doi.org/10.1016/j.adapen.2024.100170 Salau, A. O., Gebru, Y. W., & Bitew, D. (2020). Optimal network reconfiguration for power loss minimization and voltage profile enhancement in distribution systems. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04233 Salmeron, J., Wood, K., & Baldick, R. (2004). Analysis of electric grid security under terrorist threat. IEEE Transactions on Power Systems, 19(2), 905–912. https://doi.org/10.1109/TPWRS.2004.825888 Sermarini, A. C. de P., Azevedo, J. H. P., Albuquerque, V. C. de, Calili, R. F., Gonçalves, F., & Jannuzzi, G. (2024). Distributed energy resources in low-income communities: A public policy proposal for Brazil. Energy Policy, 187. https://doi.org/10.1016/j.enpol.2024.114030 Silos-Sánchez, A., Villafafila-Robles, R. y Lloret-Gallego, P. (2020). Novel fault location algorithm for meshed distribution networks with DERs. Electric Power Systems Research, 181, 106182. https://doi.org/10.1016/j.epsr.2019.106182 Shashank, S., Gurrala, G., Sastry, P. S., & Katewa, V. (2024). Cyber-Physical Modeling and Vulnerability Assessment of Substations for Transmission System Operator. Electric Power Systems Research, 235. https://doi.org/10.1016/j.epsr.2024.110769 Shezan, S. A., Kamwa, I., Ishraque, M. F., Muyeen, S. M., Hasan, K. N., Saidur, R., Rizvi, S. M., Shafiullah, M., & Al-Sulaiman, F. A. (2023). Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review. Energies, 16(4). https://doi.org/10.3390/en16041792 Shi, Q., Li, F., Olama, M., Dong, J., Xue, Y., Starke, M., Winstead, C., & Kuruganti, T. (2021). Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience. International Journal of Electrical Power and Energy Systems, 124. https://doi.org/10.1016/j.ijepes.2020.106355 Shiozaki, Y., Nagamatsu, S., Sato, K., & Bhattacharya, Y. (2024). A Systematic Literature Review of Empirical Validation of Disaster Resilience Indicators. International Journal of Disaster Risk Reduction, 104681. https://doi.org/10.1016/j.ijdrr.2024.104681 Su, Q., Sun, J., & Li, J. (2024). Vulnerability analysis of cyber-physical power systems based on failure propagation probability. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2024.109877 Sun, R., Liu, Y., Zhu, H., Azizipanah-Abarghooee, R., & Terzija, V. (2019). A network reconfiguration approach for power system restoration based on preference-based multiobjective optimization. Applied Soft Computing Journal, 83. https://doi.org/10.1016/j.asoc.2019.105656 Tang, D., Fang, Y. P., & Zio, E. (2023). Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber-attacks and online detection methods. Reliability Engineering and System Safety, 235. https://doi.org/10.1016/j.ress.2023.109212 Tang, L., Han, Y., Zalhaf, A. S., Zhou, S., Yang, P., Wang, C., & Huang, T. (2024). Resilience enhancement of active distribution networks under extreme disaster scenarios: A comprehensive overview of fault location strategies. In Renewable and Sustainable Energy Reviews (Vol. 189). Elsevier Ltd. https://doi.org/10.1016/j.rser.2023.113898 Tareq M. Shami, A. A. E. M. A. Q. A. (2022). Particle Swarm Optimization- A Comprehensive Survey. IEEE XPLORE, 10, 10031–10061. Tran The, T., Vo Ngoc, D., & Tran Anh, N. (2020). Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm. Complexity, 2020. https://doi.org/10.1155/2020/2353901 Tu, H., Xia, Y., Tse, C. K. y Chen, X. (2020). A Hybrid Cyber Attack Model for Cyber-Physical Power Systems. IEEE Access, 8, 114876-114883. https://doi.org/10.1109/ACCESS.2020.3003323. Uzum, B., Yoldas, Y., Bahceci, S., & Onen, A. (2024). Comprehensive review of transmission system operators–Distribution system operators collaboration for flexible grid operations. In Electric Power Systems Research (Vol. 227). Elsevier Ltd. https://doi.org/10.1016/j.epsr.2023.109976 Verdejo, H., Pino, V., Kliemann, W., Becker, C., & Delpiano, J. (2020). Implementation of particle swarm optimization (PSO) algorithm for tuning of power system stabilizers in multimachine electric power systems. Energies, 13(8). https://doi.org/10.3390/en13082093 Verma, R., Gawre, S. K., Patidar, N. P., & Nandanwar, S. (2024). A state of art review on the opportunities in automatic generation control of hybrid power system. In Electric Power Systems Research (Vol. 226). Elsevier Ltd. https://doi.org/10.1016/j.epsr.2023.109945 Wang, L., Zhu, Z., Jiang, C., & Li, Z. (2021). Bi-Level Robust Optimization for Distribution System with Multiple Microgrids Considering Uncertainty Distribution Locational Marginal Price. IEEE Transactions on Smart Grid, 12(2), 1104–1117. https://doi.org/10.1109/TSG.2020.3037556 Wen, J., Wan, C., Ye, Q., Yan, J. y Li, W. (2023). Disaster Risk Reduction, climate change adaptation and Their Linkages with Sustainable Development the Past 30 years: A Review. Int J Disaster Risk Sci, 14, 1-13. https://doi.org/10.1007/s13753-023-00472-3 Wu, X., Wu, S., Wu, J., Feng, L., & Tan, K. C. (2024). Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap. http://arxiv.org/abs/2401.10034 Yan, M., Ai, X., Shahidehpour, M., Li, Z., Wen, J., Bahramira, S. y Paaso, A. (2019). Enhancing the Transmission Grid Resilience in Ice Storms by Optimal Coordination of Power System Schedule with Pre-Positioning and Routing of Mobile DC De-Icing Devices. IEEE Transactions on Power Systems, 34(4), 2663-2674. https://doi.org/10.1109/TPWRS.2019.2899496 Xu, L. (2021). Research on computer interactive optimization design of power system based on genetic algorithm. Energy Reports, 7, 1–13. https://doi.org/10.1016/j.egyr.2021.10.085 Zhang, B., Dehghanian, P. y Kezunovic, M. (2019). Optimal Allocation of PV Generation and Battery Storage for Enhanced Resilience. IEEE Transactions on Smart Grid, 10(1), 535-545. DOI 10.1109/TSG.2017.2747136 Zimmerman, R. D., & Murillo-s, C. E. (2020). Matpower manual de usuario Version 7.1.LICENSElicense.txtlicense.txttext/plain; charset=utf-87167https://repository.udistrital.edu.co/bitstreams/d260bff8-da2a-465d-b9b9-96afbb6d9a3d/download997daf6c648c962d566d7b082dac908dMD53ORIGINALLicencia de uso y publicacion.pdfLicencia de uso y publicacion.pdfapplication/pdf230724https://repository.udistrital.edu.co/bitstreams/6dd466d8-d868-452e-a29b-8f93ebada6a1/download4a38ffd7d6f3f1c7a4c11d19c8a7adf8MD52MosqueraPalaciosDarinJairo2024.pdfMosqueraPalaciosDarinJairo2024.pdfapplication/pdf5007111https://repository.udistrital.edu.co/bitstreams/f399f49b-2c92-4ba3-b6f6-6387ec701b88/downloadeffa9a83bfab5bea4cc15c899b7b27a4MD54THUMBNAILLicencia de uso y publicacion.pdf.jpgLicencia de uso y publicacion.pdf.jpgIM Thumbnailimage/jpeg9488https://repository.udistrital.edu.co/bitstreams/9794bbcc-83f3-4fca-9350-686c92cbc05a/download6f8ee9db9bd8dc494030561c9066c09cMD55MosqueraPalaciosDarinJairo2024.pdf.jpgMosqueraPalaciosDarinJairo2024.pdf.jpgIM Thumbnailimage/jpeg2602https://repository.udistrital.edu.co/bitstreams/208166e9-ec33-4efd-8d69-51713a9cc1bc/download6f0b171cfdd6920d19595f48807cd91dMD5611349/94104oai:repository.udistrital.edu.co:11349/941042025-03-26 01:16:01.785restrictedhttps://repository.udistrital.edu.coRepositorio Universidad Distritalrepositorio@udistrital.edu.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

Publicaciones similares