Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

The abundance of solar resources in Saudi Arabia motivates analyzing the possibility of supplying the Saudi electric power demand using solely renewable resources and storage. This is the main objective of this research work. First, a generation and transmission expansion planning model is developed and tailored to the power system of Saudi Arabia, targeting the year 2040. We consider utility-scale generation technologies including wind power plants, solar power plants, storage facilities, and also flexible combined cycle gas turbines. We represent long-term uncertainty in terms of demand growth via scenarios, and short-term uncertainty to characterize daily solar, wind, and demand patterns via typical days. We analyze a number of case studies with increasing renewable integration targets to characterize the Saudi Arabian power system in 2040. Health, environment, and security analyses are out of the scope of this research. We conclude that it is important to actively promote the integration of renewable power in the Saudi Arabia power sector if a high renewable integration is desired. Second, a stochastic all-solar operation model is developed. The aim of this model is to operate the Saudi electric power system considering only solar power units and storage facilities. We use the long-term planning model above to generate an all-solar power system and focus on the operation problem from the perspective of the operator, considering an operation horizon of one year. We use a number of year-long cases to characterize the operation of an all-solar power system in Saudi Arabia. We conclude that an only PV generation mix requires higher storage capacity and higher installed generation capacity than both an only CSP generation mix and a hybrid PV-CSP generation mix. Third, a model to coordinate the supply of electricity and the production and transport of freshwater is developed. The time span of the model is one year and is relevant to countries like Saudi Ara (open full item for complete abstract)

Committee: Antonio Conejo Prof. (Advisor); Mahesh Illindala Prof. (Committee Member); Theodore Allen Prof. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2018, Electrical and Computer Engineering

A well-planned electric transmission network is essential for attaining an effective electricity market and the reliable operation of the associated power system. In this dissertation, we address the transmission expansion planning (TEP) problem. The goal of the thesis work is to develop models and algorithms to help system planners to identify optimal investments in the transmission network. First, we propose a candidate-line selection algorithm based on a set of systematic rules to generate an appropriate candidate-line set for TEP studies. The expertise of system planners and the characteristics of a network are both considered for candidate-line selection. Second, we develop an adaptive robust stochastic optimization model for TEP problems that specifically differentiates long- and short-term uncertainties. The long-term uncertainty pertains to year-to-year changes including the peak demand and available generating capacity of the system during the planning target year. Then, within the target year, the short-term uncertainty pertains to the production of weather-dependent renewable capacity and the load. Next, we expand the adaptive robust stochastic optimization model to consider the coordinated investment in transmission and storage facilities. Such model provides an effective tool to identify the best trade-off between these two types of facilities. Finally, we conclude by providing conclusions, contributions and suggestions for future work.

Committee: Antonio Conejo (Advisor); Ramteen Sioshansi (Committee Member); Mahesh Illindala (Committee Member) Subjects: Electrical Engineering