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...
- 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)
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oai:repository.udistrital.edu.co:11349/94104 |
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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 |
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(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. |
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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. 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