Subject Area

Electrical, Electronics Engineering

Abstract

The increasing penetration of renewable energy sources (RESs) has brought considerable economic and environmental benefits to power system operations. However, it also introduces several critical challenges that should be addressed to ensure system reliability and efficiency. First, from the generation side, energy curtailment remains a persistent issue due to the random and intermittent nature of RESs. Second, from the transmission side, delivering power from remote RESs (e.g., offshore wind farms) requires substantial infrastructure investment and careful planning. Third, from the operation side, the inherent uncertainty and variability of RES generation create operational difficulties that necessitate advanced mathematical optimization tools for decision-making. Addressing these challenges is essential for the sustainable integration of RESs into modern power systems.

To solve these issues, this dissertation introduces three major techniques, namely hydrogen technologies, back-to-back DC transmission systems (BDTSs), and robust optimization. Chapter 1 first provides a brief introduction to hydrogen technologies, encompassing hydrogen production, storage, transportation, and utilization. Then, two hydrogen applications, 1) behind-the-meter renewable hydrogen systems and 2) multi-period reconfiguration for unbalanced distribution networks with hydrogen injection, are presented. Subsequently, DC transmission systems and uncertainty modeling in power systems are outlined. Finally, a distributionally robust chance-constrained application is proposed.

Chapter 2 introduces a day-ahead optimal scheduling model tailored for electricity-hydrogen systems under renewable uncertainty, with embedded technologies of hydrogen production, storage, and utilization. Three novel ambiguity sets enriched with the moment, Wasserstein distance, and unimodality information are adeptly devised. Building upon these elaborated ambiguity sets, efficient and scalable reformulations of the expected objective function and uncertain constraints are developed, leading to either a tractable mixed-integer second-order cone programming (SOCP) problem or a linear programming (LP) problem.

Chapter 3 formulates a novel BDTS embedded scheduling problem while considering the comprehensive hydrogen model, which incorporates the technologies of hydrogen production, storage, and utilization. In addition, a well-defined Wasserstein-distance-based ambiguity set that harnesses the Gaussian-based nominal distribution is leveraged to capture the renewable output uncertainty. By developing equivalent reformulations of the nonlinear constraints inherent in BDTSs and distributionally robust chance constraints, the concerned model is eventually recast as a mixed-integer SOCP problem.

Chapter 4 proposes an optimal scheduling model for networked microgrids, embedding three innovative medium-voltage direct current (MVDC) technologies with voltage source converters, namely two-terminal back-to-back MVDC systems (BMSs), three-terminal BMSs, and multi-terminal BMSs. We first formulate their explicit models based on real-world applications, incorporating constraints related to normal operation and power supply modes. Then, we present a day-ahead scheduling model for networked MGs equipped with these BMSs. Finally, we develop tractable reformulations for nonlinear constraints considered in the scheduling model, leading to a mixed-integer LP problem.

Degree Date

Summer 2025

Document Type

Dissertation

Degree Name

Ph.D.

Department

Electrical and Computer Engineering

Advisor

Jianhui Wang

Second Advisor

Mohammad Khodayar

Number of Pages

174

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Download

Share

COinS