Theses and Dissertations

ORCID

https://orcid.org/0009-0009-7160-9634

Advisor

Fu, Yong

Committee Member

Choi, Seungdeog

Committee Member

Karimi, Masoud

Committee Member

Fan, Xing

Date of Degree

5-16-2025

Original embargo terms

Embargo 1 year

Document Type

Dissertation - Open Access

Major

Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

The Alternating Current Optimal Power Flow (ACOPF) problem seeks optimal operational planning for power systems with the lowest cost within the operational and physical constraints, ensuring higher efficiency and reliability for the power system. Meanwhile, the power reserve scheduling, which ensures an adequate backup capacity to compensate for demand and supply fluctuations, contingencies, and various unforeseen disruptions, plays a crucial role in power system reliability. As power systems become more stochastic and dynamic, the co optimization of energy and reserve becomes a significant trend among independent system operators (ISOs) and regional transmission organizations (RTOs), as it yields substantial benefit improvement over independent optimizations. Furthermore, expanding ACOPF to a multi-period format captures temporal dependencies and operational dynamics, enabling more comprehensive modeling of realistic power system operation, which enhances strategic management of the power system. However, the large-scale nature of the power system, the inherent nonlinearity and nonconvexity of the ACOPF, the increased model complexity due to the additional reserve scheduling, and the expanded model scale with linked decision variables across time periods, collectively contribute to substantial computational complexity, making it a challenging optimization problem. To tackle this challenge and achieve a fast, optimal, and reliable power system operational planning, the proposed research develops advanced solution methods involving two representative endeavors: the development of mathematical optimization algorithms with enhanced computational efficiency, and the investigation of the decomposition techniques along with the implementation on advanced computational resources. Firstly, the single-period reserve constrained ACOPF (RCOPF), which co-optimizes energy and reserve through detailed modeling of various reserve types with considering AC network constraint, is addressed with an accelerated primal-dual interior point (A-PDIPM) optimization algorithm. Secondly, the multi period RCOPF (M-RCOPF) is addressed with a parallel optimization approach, which enables problem decomposability by reformulating the intertemporal coupling constraints with introduced auxiliary variables, and decomposes the problem into two major solution modules. Each module consists of multiple independent smaller subproblems that are solved simultaneously in a fully parallel manner on the advanced High-Performance Computing (HPC) platform. As a result, the proposed methods effectively solve the large-scale reserve constrained ACOPF problems, ensuring efficient and reliable power system operation planning.

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