On the Integration and Coordination of Interconnected Water and Electricity Networks: Use Cases, Algorithms, and Solutions

• Main Author: Alhazmi, Mohannad
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2022
• Subjects: Electrical engineering; Energy; Engineering
• ISBN: 9798426807303

With the widespread deployment of advanced heterogeneous technologies and the sharply-growing complexity in our modern society, there is an increasing demand for risk-aware management and joint operation of interconnected infrastructures and lifeline networks. The coordination between water and electricity networks particularly stands out and is urgently demanding as (i) water system is one of the most energy-intensive critical infrastructures, and (ii) water unavailability, if experienced, swiftly translates into health, safety, and national security concerns. This dissertation studies the day-ahead operation of power and water systems under normal and emergency operating conditions. Under normal operating conditions, a comprehensive day-ahead optimization framework for joint operation of the interdependent power and water systems is proposed. Beyond the state-of-the-art solutions and practices where power and water systems are individually operated in their respective domains, we present an integrated framework for power and water systems that conjoins the Optimal Power Flow (OPF) mechanisms in power grids with innovative operation models of water networks. Further, this dissertation develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interlinked power and water systems. Joint probabilistic constraint programming is employed to capture the uncertainties in renewable energy sources and water demand forecasts. Moreover, we develop an emergency response optimization model for the operation of power and water systems when faced with failures and threats originated from the high-impact low-probability events. The proposed formulation considers the hydraulic constraints of the water networks, which results in a naturally nonlinear model. The proposed nonlinear model is approximated using a piece-wise linearization approach to convert the optimization model into a mixed-integer linear programming formulation.