{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117797"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117797","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Analysis for Resilience of Complex Energy Systems: Operations and Designs","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Wu, Jiaxin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Wang, Pingfeng","Kim, Harrison Hyung Min","Mohaghegh, Zahra","Shao, Chenhui","Chen, Xin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Energy Systems","Disruption Management","Design Automation","Optimization","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2022 Jiaxin Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117797","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Pingfeng","Kim, Harrison Hyung Min","Mohaghegh, Zahra","Shao, Chenhui","Chen, Xin"]},{"key":"dc:creator","label":"Author","values":["Wu, Jiaxin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-30"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Energy Systems","Disruption Management","Design Automation","Optimization","Machine Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Jiaxin Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117797"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Jiaxin Wu, accepted the attached license on 2022-11-29 at 12:04.","The student, Jiaxin Wu, submitted this Dissertation for approval on 2022-11-29 at 12:20.","This Dissertation was approved for publication on 2022-11-30 at 17:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18668 on 2023-04-12 at 07:35:37","With the growth of complexity and extent, large-scale interconnected network systems, such as infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events of an engineered system and therefore improving the system’s resilience is an essential yet challenging task. This thesis proposes mechanisms across different phases: design, operation, and failure recovery to ensure system resilience after the occurrence of failure events. We first formulate a mixed-integer linear programming (MILP) based failure recovery framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Furthermore, because of the temporal sparsity of the decision-making, we introduce an additional CVaR risk measure to the framework. The resulting restoration framework involves a large-scale MILP problem. Thus an adequate decomposition technique, i.e., modified Lagrangian dual decomposition, is employed to achieve tractable computational complexity. Besides, during the operation stage, intentional islanding is commonly applied in practical applications and attracts great interest in the literature. Thus, we propose a novel hierarchical spectral clustering-based intentional islanding strategy for interconnected systems. Various system online measurements are used as embedded information in the clustering algorithm to enrich the modeling capability of the proposed framework. As for enhancing the designs, challenges have arisen due to the increasing scale of modern systems and the complicated underlying physical constraints. Therefore, we develop a novel generative design method utilizing graph learning algorithms to tackle these challenges. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. At the same time, the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. We consider case studies for complex engineering systems, such as synthetic supply chain networks and power systems from the IEEE dataset, to illustrate the applicability of the proposed methods for improving system resilience."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analysis for Resilience of Complex Energy Systems: Operations and Designs"]}]}],"canonical_facts":{"dc:contributor":["Wang, Pingfeng","Kim, Harrison Hyung Min","Mohaghegh, Zahra","Shao, Chenhui","Chen, Xin"],"dc:creator":["Wu, Jiaxin"],"dc:date":["2022-12","2022-11-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Jiaxin Wu, accepted the attached license on 2022-11-29 at 12:04.","The student, Jiaxin Wu, submitted this Dissertation for approval on 2022-11-29 at 12:20.","This Dissertation was approved for publication on 2022-11-30 at 17:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18668 on 2023-04-12 at 07:35:37","With the growth of complexity and extent, large-scale interconnected network systems, such as infrastructure networks, become more vulnerable to external disturbances. Hence, managing potential disruptive events of an engineered system and therefore improving the system’s resilience is an essential yet challenging task. This thesis proposes mechanisms across different phases: design, operation, and failure recovery to ensure system resilience after the occurrence of failure events. We first formulate a mixed-integer linear programming (MILP) based failure recovery framework using heterogeneous dispatchable agents. The scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Furthermore, because of the temporal sparsity of the decision-making, we introduce an additional CVaR risk measure to the framework. The resulting restoration framework involves a large-scale MILP problem. Thus an adequate decomposition technique, i.e., modified Lagrangian dual decomposition, is employed to achieve tractable computational complexity. Besides, during the operation stage, intentional islanding is commonly applied in practical applications and attracts great interest in the literature. Thus, we propose a novel hierarchical spectral clustering-based intentional islanding strategy for interconnected systems. Various system online measurements are used as embedded information in the clustering algorithm to enrich the modeling capability of the proposed framework. As for enhancing the designs, challenges have arisen due to the increasing scale of modern systems and the complicated underlying physical constraints. Therefore, we develop a novel generative design method utilizing graph learning algorithms to tackle these challenges. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. At the same time, the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. We consider case studies for complex engineering systems, such as synthetic supply chain networks and power systems from the IEEE dataset, to illustrate the applicability of the proposed methods for improving system resilience."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117797"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jiaxin Wu"],"dc:subject":["Energy Systems","Disruption Management","Design Automation","Optimization","Machine Learning"],"dc:title":["Analysis for Resilience of Complex Energy Systems: Operations and Designs"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}