Graduation Semester and Year

Summer 2025

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering

Department

Electrical Engineering

First Advisor

Yan Wan

Abstract

Microgrid systems have attracted considerable attention in recent years due to their wide applications in integrating renewable energy sources and supporting a reliable power supply. However, modern microgrids are frequently affected by various disturbances and uncertainties, such as fluctuating renewable energy sources, varying load demands, and complex environmental conditions. Therefore, enhancing system resilience, improving communication efficiency, and enabling effective uncertainty evaluation and decision-making are essential. To address these challenges, this dissertation develops resilient control strategies, communication-efficient control methods, and efficient approaches for uncertainty evaluation and decision-making in microgrid systems. The first thrust of this dissertation investigates the resilience of DC microgrids under various disturbances. We systematically analyze the failure conditions of boost converters and develop a power buffer control strategy to prevent voltage collapse and maintain system stability. A novel resilience metric is proposed to provide a comprehensive evaluation of microgrid performance under power mismatches and disruptions. The effectiveness of the proposed control and resilience models is validated through hardware-in-the-loop experiments. The second thrust addresses the challenge of communication efficiency in distributed microgrid control. We develop a model-based dynamic event-triggered distributed control strategy for physically interconnected systems, which leverages the system dynamic model to reduce unnecessary information exchange while ensuring system stability. The proposed approach is validated through numerical studies and hardware-in-the-loop experiments on networked power buffer systems. The third thrust addresses the challenges of uncertainty evaluation and decision-making in microgrid systems. First, we develop an efficient online uncertainty evaluation method that integrates the Multivariate Probabilistic Collocation Method (MPCM) with copula-based conditional probability, enabling high-accuracy, real-time estimation of system output statistics under dynamic uncertainties. Second, we develop the MPCM-Taguchi method, which combines MPCM with the Taguchi experimental design to achieve accurate and efficient evaluation of output statistics in high-dimensional uncertainty systems. Third, we develop an MPCM-Taguchi-based decision-making framework that integrates with integral reinforcement learning (IRL) and apply the method to optimize control policies for the power buffer system with uncertain loads.

Keywords

Microgrids, Resilient control, Event-triggered control, Uncertainty evaluation and decision-making

Disciplines

Controls and Control Theory | Power and Energy

Comments

Degree granted by The University of Texas at Arlington

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Available for download on Wednesday, August 11, 2027

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