Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is employed to identify critical operating thresholds and stability boundaries. ENA, originating from systems ecology, is utilized to analyze the network structure and flow characteristics, potentially revealing topological vulnerabilities and strengths related to resilience. The primary research goal is to investigate how the combination of BA and ENA can inform strategic resilience enhancements in systems. Resilience is evaluated based on the system's absorptive, adaptive, and recovery capabilities. The proposed research consists of three main parts: applying a BA framework to map bifurcation phenomena to specific resilience properties, investigating the utility of ENA metrics for providing insights into the dynamic response of systems (using power networks under stress as the case study), and developing an open-source methodology and computational tool that leverages combined BA-ENA insights for actionable strategies to enhance systems (e.g. placement of assets like batteries in power systems). The methodology is designed for general application in systems across different fields, but it will be validated via detailed power systems case studies. In specific, this project aims to demonstrate the effectiveness of the method by providing alternative approaches to supportive generation placement strategies with both network and dynamic-aware analyses, contributing to a more robust and reliable energy infrastructure.
","abstract_html":"<p>Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is employed to identify critical operating thresholds and stability boundaries. ENA, originating from systems ecology, is utilized to analyze the network structure and flow characteristics, potentially revealing topological vulnerabilities and strengths related to resilience. The primary research goal is to investigate how the combination of BA and ENA can inform strategic resilience enhancements in systems. Resilience is evaluated based on the system's absorptive, adaptive, and recovery capabilities. The proposed research consists of three main parts: applying a BA framework to map bifurcation phenomena to specific resilience properties, investigating the utility of ENA metrics for providing insights into the dynamic response of systems (using power networks under stress as the case study), and developing an open-source methodology and computational tool that leverages combined BA-ENA insights for actionable strategies to enhance systems (e.g. placement of assets like batteries in power systems). The methodology is designed for general application in systems across different fields, but it will be validated via detailed power systems case studies. In specific, this project aims to demonstrate the effectiveness of the method by providing alternative approaches to supportive generation placement strategies with both network and dynamic-aware analyses, contributing to a more robust and reliable energy infrastructure.</p>","abstract_has_math":false,"creators":["Gracia Otalvaro, Rogelio"],"institution":null,"degree_name":"Doctor of Philosophy in Electrical Engineering & Computer Science","degree_level":"Dissertation - Open Access","degree_discipline":"Electrical, Computer, Software, and Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10-01T07:00:00Z","date_published":"2025-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Complex Systems","Resilience","Bifurcation Analysis","Critical Infrastructure","Open-Source","Ecological Network Analysis","Power Systems","Controls and Control Theory","Industrial Engineering","Other Operations Research, Systems Engineering and Industrial Engineering","Power and Energy","Risk Analysis","Systems Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/947","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Gracia Otalvaro, Rogelio"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical, Computer, Software, and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Electrical Engineering & Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Complex Systems","Resilience","Bifurcation Analysis","Critical Infrastructure","Open-Source","Ecological Network Analysis","Power Systems","Controls and Control Theory","Industrial Engineering","Other Operations Research, Systems Engineering and Industrial Engineering","Power and Energy","Risk Analysis","Systems Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/947"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is employed to identify critical operating thresholds and stability boundaries. ENA, originating from systems ecology, is utilized to analyze the network structure and flow characteristics, potentially revealing topological vulnerabilities and strengths related to resilience. The primary research goal is to investigate how the combination of BA and ENA can inform strategic resilience enhancements in systems. Resilience is evaluated based on the system's absorptive, adaptive, and recovery capabilities. The proposed research consists of three main parts: applying a BA framework to map bifurcation phenomena to specific resilience properties, investigating the utility of ENA metrics for providing insights into the dynamic response of systems (using power networks under stress as the case study), and developing an open-source methodology and computational tool that leverages combined BA-ENA insights for actionable strategies to enhance systems (e.g. placement of assets like batteries in power systems). The methodology is designed for general application in systems across different fields, but it will be validated via detailed power systems case studies. In specific, this project aims to demonstrate the effectiveness of the method by providing alternative approaches to supportive generation placement strategies with both network and dynamic-aware analyses, contributing to a more robust and reliable energy infrastructure.
"]},{"key":"dc:title","label":"Title","values":["Resilience Engineering via Bifurcation and Ecological Network Analysis: Demonstrated in an Electric Power Case Study"]}]}],"canonical_facts":{"dc:creator":["Gracia Otalvaro, Rogelio"],"dc:description.abstract":["Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is employed to identify critical operating thresholds and stability boundaries. ENA, originating from systems ecology, is utilized to analyze the network structure and flow characteristics, potentially revealing topological vulnerabilities and strengths related to resilience. The primary research goal is to investigate how the combination of BA and ENA can inform strategic resilience enhancements in systems. Resilience is evaluated based on the system's absorptive, adaptive, and recovery capabilities. The proposed research consists of three main parts: applying a BA framework to map bifurcation phenomena to specific resilience properties, investigating the utility of ENA metrics for providing insights into the dynamic response of systems (using power networks under stress as the case study), and developing an open-source methodology and computational tool that leverages combined BA-ENA insights for actionable strategies to enhance systems (e.g. placement of assets like batteries in power systems). The methodology is designed for general application in systems across different fields, but it will be validated via detailed power systems case studies. In specific, this project aims to demonstrate the effectiveness of the method by providing alternative approaches to supportive generation placement strategies with both network and dynamic-aware analyses, contributing to a more robust and reliable energy infrastructure.
"],"dc:identifier":["https://commons.erau.edu/edt/947"],"dc:subject":["Complex Systems","Resilience","Bifurcation Analysis","Critical Infrastructure","Open-Source","Ecological Network Analysis","Power Systems","Controls and Control Theory","Industrial Engineering","Other Operations Research, Systems Engineering and Industrial Engineering","Power and Energy","Risk Analysis","Systems Engineering"],"dc:title":["Resilience Engineering via Bifurcation and Ecological Network Analysis: Demonstrated in an Electric Power Case Study"],"thesis:degree_discipline":["Electrical, Computer, Software, and Systems Engineering"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Electrical Engineering & Computer Science"]},"updated_at":"2026-07-27T19:26:22Z"}