Online optimization and learning for the optimal power flow problem with unknown objectives

Master thesis (2024)

Authors

A. Chai Mechanical Engineering

Contributors

S. Grammatico Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering (mentor)
E. Benenati Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering (mentor)
P. Mohajerin Esfahani Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2024 Anbang Chai
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Published Date

19-03-2024

Language

English

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Faculty

Mechanical Engineering

Abstract

The Optimal Power Flow (OPF) problem, a cornerstone of power system operations, has gained increased attention since its inception by Carpentier in 1962. OPF is fundamentally an optimization challenge aimed at enhancing electric power system operations within the bounds of physical and operational constraints. Over the decades, various methodologies have been explored to address the OPF problem, adapting to evolving grid complexities and the integration of distributed energy resources. These advancements have brought to the fore issues related to system randomness, fluctuation, and the need for rapid control mechanisms. This thesis introduces a comprehensive solution incorporating an online optimization algorithm tailored for real-time OPF applications. This approach, characterized by minimal computation times, integrates a feedback strategy that obviates the necessity for instantaneous power demand information and employs a Shape-constrained Gaussian Process for the estimation of unknown cost functions. The proposed control algorithm demonstrates robust tracking performance and satisfactory computation efficiency, marking a significant improvement towards optimizing future power networks fraught with increasing size and complexity. Moreover, this work delves into the investigation of various system design parameters, offering insights into potential avenues for enhancing system performance. Through a meticulous examination of these parameters, the thesis sheds light on strategies to refine the integrated system’s efficacy, paving the way for more resilient and efficient power networks.