Graduation Semester and Year
Summer 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Ali Davoudi
Second Advisor
Ramtin Madani
Third Advisor
Yan Wan
Fourth Advisor
Yijing Xie
Fifth Advisor
Yichen Zhang
Abstract
Localization of energy generation and consumption profiles has made microgrids attractive options, but their robust and optimal operation has remained a daunting task. The two research challenges of accommodating uncertain features and bridging the control and optimization paradigms are considered in this dissertation. In the context of DC microgrids populated with power buffers, centralized, decentralized, and distributed robust control architectures are designed. The power buffer control with stochastic load behavior is addressed through an optimal control policy by incorporating the multivariate probability collocation method and integral reinforcement learning. In a more general DC microgrid paradigm, robust controllers with affine quadratic stability are introduced. Lastly, robust optimal power flow solutions are introduced for DC networks to accommodate uncertain loads. In the context of inverter-dominant AC microgrids, a more computationally tractable, stable, and optimal solution is sought to accommodate more frequent load changes. Throughout, proposed strategies have been validated with numerical analysis, simulation studies, and/or controller/hardware-in-the-loop setups.
Keywords
DC Microgrid, Power Buffer, Robust Control, Uncertainty, Optimal Control, Reinforcement Learning, Semidefinite Programming, Inverters, Stability, Optimal Power Flow
Disciplines
Controls and Control Theory | Power and Energy
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Valsala Priyadarsini Premakumar, Abhiram, "SYNERGISTIC CONTROL AND OPTIMIZATION IN COMPLEX MICROGRIDS" (2025). Electrical Engineering Dissertations. 409.
https://mavmatrix.uta.edu/electricaleng_dissertations/409
Comments
This material is based upon work supported by the U.S. National Science Foundation under award No. 1839804, 1809454, and Department of Navy award N00014-22-1-2524 issued by the Office of Naval Research. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research. The technical materials presented in this dissertation are approved for public release under DCN# 2025-7-7-1112.