Data-Driven Modeling and Optimization of Flexible and Efficient Power and Water Systems Management

The accelerating transition towards low-carbon energy systems and increasing pressure on water resources are transforming how societies plan, operate, and coordinate infrastructures. Power systems face rising challenges from growing demand driven by electrification and uncertainty from renewable generation, placing new requirements on flexibility, reliability, and market design. Water systems simultaneously confront growing demand, reduced freshwater availability, and the need to manage energy-intensive treatment and distribution under volatile energy prices, making their operation more tightly coupled to energy system dynamics. At the same time, the physical and economic interdependence between energy and water creates opportunities for flexibility, efficiency, and resilience, provided that decision-making frameworks jointly represent coupled physics, market interactions, and uncertainties, thereby facilitating the water-energy nexus transition. This thesis advances such an integrated view by developing data-driven mathematical optimization frameworks supporting flexible, technology-aware planning, market participation, and operation of coupled energy and water systems under uncertainty, enabling system-level investment and operational decisions that account for infrastructure physics, interdependence, and techno-economic characteristics.