Committee Chair

Disfani, Vahid R.

Committee Member

Karrar, Abdelrahman A.; Ofoli, Abdul R.; Ahmed, Raga


Dept. of Engineering


College of Engineering and Computer Science


University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)


The 21st-century electric power grid is transitioning from a centralized structure designed for bulk-power transfer to a distributed paradigm that integrates the variable renewable energy (VRE) resources spatially distributed across the grid. This work proposes algorithmic solutions for distributed economic dispatch based on Subgradient method and Alternating Direction Method of Multipliers (ADMM), both designed to be agnostic with any initialization vector. The proposed distributed online solutions leverage a dynamic average consensus algorithm to track the time-variant linearly coupled constraint that allows an abrupt change in power demand of the network because of the high penetration of VRE resources. The problems are modeled as discrete dynamic systems to investigate the stability and convergence of the algorithm. The update procedures are designed such that the iterates converge to the optimal solution of the original optimization problem, steered by the gain parameter corresponding to the second largest eigenvalue of the system matrix.


I am grateful to my advisor Dr. Vahid Disfani for his extraordinary mentorship throughout my graduate study. Before I met you, I had a naïve understanding of research and was not sure what I really wanted to pursue for the rest of my career. The lectures on Power System Optimization during my first semester truly awakened my research interests. I would have never known what I wanted to study without those incredible lectures covering topics from the primal-dual algorithm to consensus theory. I am always amazed how quickly you understand my questions, be it coding related or conceptual, and come up with elegant solutions. I thank you for patiently listening to all my questions, sometimes for hours. I am beyond words to describe how great your mentorship has been and how much I cherish it. I will always be indebted to you for everything you have done for me. I am very thankful to all my committee members: Dr. Abdelrahman A. Karrar, Dr. Abdul R. Ofoli, and Dr. Raga Ahmed for being kind to spare time for my work. Dr. Karrar, you truly have amazed me with your teaching and inspired me with your passion for the power system, not to mention that your door has always been open to students. Dr. Ofoli, I was fortunate to be in your class on Fuzzy Logic and Intelligent Control that has instilled in me the enthusiasm for control theory. Dr. Ahmed, it cannot be stressed enough how much you care about each of your students. Thank you Dr. Ahmed H. Eltom for your great leadership and constant encouragement. I want to thank Pablo, my friend and collaborator, for enthusiastically helping me throughout my thesis. Thank you for the early morning coffee and for teaching me some invaluable life lessons. I want to thank Saroj, my friend since college, for his support throughout and want to especially acknowledge him for providing me the template of \LaTeX. It would have taken days if I had to make one from scratch. Thank you to all my friends from ConnectSmart Lab: Ahmed, Farog, James, and Shahab. Each one of you is special to me. Thank you to my friends in Cherryton Christopher and Jackson. Thank you Sambuddha, Bikash, Kamal, Sudip, Kanchan, Mahim, Rohan, Hafiz, and all my dear friends for always cheering me up. Last but not the least, I want to thank my family for being with me during the highs and the lows. I could have never dreamt big without the sacrifice of my parents. Thank you to Bishal and Shailaja for their kindness, Satish and Samiksha for growing up with me.


M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.




Distributed algorithms; Modal analysis; Smart power grids; Subgradient methods


ADMM; Distributed algorithms; Dynamic average consensus; Modal analysis; Subgradient; Time-variant load

Document Type

Masters theses




xiv, 100 leaves





Date Available