An optimal energy scheduling procedure is essential in an isolated environment such as naval submarines. Conventional naval submarines include diesel-electric propulsion systems, which utilize diesel generators along with batteries and fuel cells. Submarines can charge the batteries by running diesel-electric generators only at the surface or at snorkeling depth. This is the most dangerous time for submarines to be detectable by acoustic and non-acoustic sensors of enemy assets. Optimizing the energy resources while reducing the need for snorkeling is the main factor to enhance underwater endurance. This thesis introduces an energy management system (EMS) as a supervisory tool for the officers onboard to plan energy schedules in order to complete various missions. The EMS for a 4,000-ton class conventional submarine is developed to minimize snorkeling and satisfy various conditions of practically designed missions by optimizing the energy resources, such as Lithium-ion batteries, Proton-exchange membrane fuel cells, and diesel-electric generators. Eventually, the optimized energy schedules with the minimum snorkeling hours are produced for five mission scenarios. More importantly, this EMS performs deterministic and stochastic operational scheduling processes to provide secured optimal schedules which contains outages in the power generation and storage systems.

Climate change is affecting power generation globally. Increase in the ambient air

temperature due to the emission of greenhouse gases, caused mainly by burning of fossil fuels, is the most prominent reason for this effect. This increase in the temperature along with the changing precipitation levels has led to the melting of the snow packs and increase in the evaporation levels, thus affecting hydropower. The hydropower in the United States might increase by 8%-60% due to Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios respectively by 2050. Wind power generation is mainly affected by the change in the wind speed and solar power generation is mainly affected by the increase in the ambient air temperature, changes in precipitation and solar radiation. Solar power output reduces by approximately a total of 2.5 billion kilowatt- hour (kWh) by 2050 for an increase in ambient air temperature of 1 degree Celsius. Increase in the ambient air and water temperature mainly affect the thermal power generation. An increase in the temperature as per the RCP 4.5 and RCP 8.5 climate change scenarios could decrease the total thermal power generation in the United States by an average of 26 billion kWh and a possible income loss of around 1.5 billion dollars. This thesis discusses the various effects of climate change on each of these four power plant types.

The objectives of this research project were to develop a model of real power demand from a dc fast charging station both with and without an integrated battery energy storage system (BESS). An optimal deterministic control strategy was devel-oped to perform load-shaping under various scenarios with various load-shaping goals in mind to establish the value for BESS’s with various power and energy capacities.

To achieve these objectives, first a statistical model of electric vehicle drivers’ charging behaviors (home charging and dc fast charging) was constructed and simu-lated according to empirical charging data and several key findings about people’s charging habits in the literature.

Data of private vehicles’ driving records was extracted from the National Household Travel Survey (NHTS), derived 42 statistical distributions that mathe-matically modeled people’s driving behaviors. From this start, two algorithms were developed to simulate driver behavior: one using a database sampling method (DSM) and another using probability distribution sampling method (PDSM) to simulate the electric vehicles’ driving cycles. Both methods used data and statistical distributions derived from NHTS. Next, a model of the EV drivers’ charging behavior was incor-porated into the simulation of the electric vehicles’ driving cycles, and then the ve-hicles’ charging behaviors were simulated. From these simulations, one can forecast the real-power demand of a typical dc fast charging station with six dc 50 kW fast chargers serving a population of 700 EVs. (The ratio of six dc fast chargers to 700 EVs was selected based on the current value of this ratio in the US.) Next, a BESS was integrated into the dc fast charging station demand model and the size and charging behavior was optimized to account for different criteria which were based on the goals of the different potential owners: SRP or a third-party owner. It was established when a BESS would become economically feasible using a simplified economic model.

It was observed that the real-power demand shape is a function of the size of the BESS and the owner’s objective, i.e., flattening the demand curve or minimizing the cost of electricity.

In recent years, with the increasing penetration of solar generation, the uncertainty and variability of the power system generation also have increased. Power systems always require a balance between generation and load. The generation of the conventional generators must be scheduled to meet the total net load of the system with the variability and uncertainty of the solar resources integrated. The ability to match generation to load requires certain flexibility of the conventional generation units as well as a flexible transmission network to deliver the power. In this work, given the generation flexibility primarily reflected in the ramping rates, as well as the minimum and maximum output of the generation units, the transmission network flexibility is assessed using the metric developed in this work.

The main topic of this thesis is the examination of the transmission system flexibility using time series power flows (TSPFs). First, a TSPFs program is developed considering the economic dispatch of all the generating stations, as well as the available ramping rate of each generating unit. The time series power flow spans a period of 24 hours with 5-minute time interval and hence includes 288 power flow snapshots. Every power flow snapshot is created based on the power system topology and the previous system state. These power flow snapshots are referred to as the base case power flow below.

Sensitivity analysis is then conducted by using the TSPFs program as a primary tool, by fixing all but one of the system changes which include: solar penetration, wires to wires interconnection, expected retirements of coal units and expected participation in the energy

imbalance market. The impact of each individual change can be evaluated by the metric developed in the following chapters.

Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs).

This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.

High Voltage Direct Current (HVDC) Technology has several features that make it particularly attractive for specific transmission applications. Recent years have witnessed an unprecedented growth in the number of the HVDC projects, which demonstrates a heightened interest in the HVDC technology. In parallel, the use of renewable energy sources has dramatically increased. For instance, Kuwait has recently announced a renewable project to be completed in 2035; this project aims to produce 15% of the countrys energy consumption from renewable sources. However, facilities that use renewable sources, such as solar and wind, to provide clean energy, are mostly placed in remote areas, as their installation requires a massive space of free land. Consequently, considerable challenges arise in terms of transmitting power generated from renewable sources of energy in remote areas to urban areas for further consumption.

The present thesis investigates different transmission line systems for transmitting bulk energy from renewable sources. Specifically, two systems will be focused on: the high-voltage alternating current (HVAC) system and the high-voltage direct current (HVDC) system. In order to determine the most efficient way of transmitting bulk energy from renewable sources, different aspects of the aforementioned two types of systems are analyzed. Limitations inherent in both HVAC and HVDC systems have been discussed.

At present, artificial intelligence plays an important role in power system control and monitoring. Consequently, in this thesis, the fault issue has been analyzed in transmission systems, with a specific consideration of machine learning tools that can help monitor transmission systems by detecting fault locations. These tools, called models, are used to analyze the collected data. In the present thesis, a focus on such models as linear regression (LR), K-nearest neighbors (KNN), linear support vector machine (LSVM) , and adaptive boost (AdaBoost). Finally, the accuracy of each model is evaluated and discussed. The machine learning concept introduced in the present thesis lays down the foundation for future research in this area so that to enable further research on the efficient ways to improve the performance of transmission line components and power systems.

With the increasing penetration of converter interfaced renewable generation into power systems, the structure and behavior of the power system is changing, catalyzing alterations and enhancements in modeling and simulation methods.

This work puts forth a Hybrid Electromagnetic Transient-Transient Stability simulation method implemented using MATLAB and Simulink, to study power electronic based power systems. Hybrid Simulation enables detailed, accurate modeling, along with fast, efficient simulation, on account of the Electromagnetic Transient (EMT) and Transient Stability (TS) simulations respectively. A critical component of hybrid simulation is the interaction between the EMT and TS simulators, established through a well-defined interface technique, which has been explored in detail.

This research focuses on the boundary conditions and interaction between the two simulation models for optimum accuracy and computational efficiency.

A case study has been carried out employing the proposed hybrid simulation method. The test case used is the IEEE 9-bus system, modified to integrate it with a solar PV plant. The validation of the hybrid model with the benchmark full EMT model, along with the analysis of the accuracy and efficiency, has been performed. The steady-state and transient analysis results demonstrate that the performance of the hybrid simulation method is competent. The hybrid simulation technique suitably captures accuracy of EMT simulation and efficiency of TS simulation, therefore adequately representing the behavior of power systems with high penetration of converter interfaced generation.