Analysis for Resilience of Complex Energy Systems: Operations and Designs

Wu, Jiaxin

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https://hdl.handle.net/2142/117797 Copy

Description

Title

Analysis for Resilience of Complex Energy Systems: Operations and Designs

Author(s)

Wu, Jiaxin

Issue Date

2022-11-30

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Pingfeng

Doctoral Committee Chair(s)

Wang, Pingfeng

Committee Member(s)

• Kim, Harrison Hyung Min
• Mohaghegh, Zahra
• Shao, Chenhui
• Chen, Xin

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Systems & Entrepreneurial Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Energy Systems
• Disruption Management
• Design Automation
• Optimization
• Machine Learning

Abstract

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.

Graduation Semester

2022-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/117797

Copyright and License Information

Copyright 2022 Jiaxin Wu

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