INFRASTRUCTURE PLANNING AND OPERATIONAL SCHEDULING FOR POWER GENERATING SYSTEMS: AN ENERGY-WATER NEXUS APPROACH

Due to population growth and economic prosperity, the demand for energy and potable water is rapidly increasing around the world. As the demand for energy and water increases, the need of decision-making strategies for power generating systems that exploit the Energy-Water Nexus (EW-N) is becoming more apparent. These decision-making strategies are complex and comprise of decisions related to: (i) the construction of new power plants and energy storage devices; (ii) the conversion of cooling technologies for existing power plants; and (iii) environmental impacts. Since the type of generating and cooling technology of a power plant directly affects its water usage, the decision-making strategies are intrinsically multi-objective. Therefore, a decision-making framework based upon the aforementioned concerns is essential for developing a power generating system that is able to meet the energy demands and sustainably utilize water. In this work, we present a novel EW-N superstructure-based representation and multi-objective optimization framework for infrastructure planning and operational scheduling of power generating systems with renewable generators and large-scale energy storage devices. The EW-N problem is posed as a two-stage stochastic mixed-integer linear program that minimizes the capital expenditures, operational cost, and water usage of the system. The model includes planning decisions such as the ability to construct additional power plants, storage units, and convert the cooling technologies of existing power plants. The model also includes scheduling decisions which determine how much power each plant generates, how energy is allocated within the system, and when energy is stored and released from storage devices. The model is implemented into a case study within the Edwards Aquifer region of Texas for a centralized power generating utility and determines the optimal conversion, expansion and operational decisions for the utility given the current infrastructure of the system.