Gatsis, NikolaosAyyagari, Krishna Sandeep2024-04-092024-12-122024-04-092022https://hdl.handle.net/20.500.12588/6359This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.The full text of this item is not available at this time because the author has placed this item under an embargo until December 12, 2024.Modern power distribution networks (PDNs) are gradually moving forward with new technologies such as control, communication, and metering to promote the concept of Smart Grids. Driven by initiatives such as US DOE Smart Grids, future distribution systems will facilitate the increased adoption of variable renewable generation, such as photovoltaic (PV) installations and wind turbines, as well as the integration of other critical infrastructures like building and water utilities. In such a setting, integrated control and optimization of such infrastructures would enhance optimal and secure operation, and reap socio-economic and environmental benefits. To this end, the majority of this dissertation aims to provide an optimization framework to coordinate power distribution networks with building operations and pump scheduling in water distribution networks.204 pagesapplication/pdfenbuilding operationsdata-driven methodsmachine learningoptimizationpower distributionwater distributionElectrical engineeringThesis