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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Electrical and Computer Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

David Irwin

Second Advisor

Prashant Shenoy

Third Advisor

Jay Taneja

Fourth Advisor

Fatima M. Anwar

Subject Categories

Computer and Systems Architecture | Data Science | Hardware Systems | Other Computer Engineering | Power and Energy | Systems and Communications | Systems Architecture


Global warming and climate change have underscored the need for designing sustainable energy systems. Sustainable energy systems, e.g., smart grids, green data centers, differ from the traditional systems in significant ways and present unique challenges to system designers and operators. First, intermittent renewable energy resources power these systems, which break the notion of infinite, reliable, and controllable power supply. Second, these systems come in varying sizes, spanning over large geographical regions. The control of these dispersed and diverse systems raises scalability challenges. Third, the performance modeling and fault detection in sustainable energy systems is still an active research area. Finally, because of their reliance on renewable energy resources, the energy efficiency of the sustainable energy systems depends on external and uncontrollable factors, i.e., weather, geographical location. In this thesis, I argue that enabling the programmability of energy systems improves their reliability, scalability, explainability, and efficiency. In evaluating my thesis statement, I make contributions to the design, analytics, and operation of energy systems. I develop a programmable solar module that enables precise and accurate control over the power output of photovoltaic panels. I also examine how the design of zero-carbon data centers and edge-computing systems can help them adapt to their environments and maximize the energy efficiency. I develop tools and techniques that automate the modeling and forecasting of energy systems’ performance. Finally, I show how to leverage the programmability and performance analytics to operate sustainable energy systems in a reliable, scalable, and efficient manner.


Creative Commons License

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

Available for download on Saturday, May 13, 2023