The shipboard power system supplies energy to sophisticated systems for weapons, communications, navigation, and operation. After a fault is encountered, reconfiguration of a shipboard power system becomes a critical activity that is required to either restore service to a lost load or to meet some operational requirements of the ship. Reconfiguration refers to changing the topology of the power system in order to isolate system damage and/or optimize certain characteristics of the system related to power efficiency. When finding the optimal state, it is important to have a method that finds the desired state within a short amount of time, in order to allow fast response for the system. Since the reconfiguration problem is highly nonlinear over a domain of discrete variables, the genetic algorithm method is a suitable candidate. In this thesis, a reconfiguration methodology, using a genetic algorithm, is presented that will reconfigure a network, satisfying the operational requirements and priorities of loads. Graph theory is utilized to represent the shipboard power system topology in matrices. The reconfiguration process and the genetic algorithm are implemented in MATLAB and tested on an 8-bus power system model and on larger power system with distributed generators by considering different fault scenarios. Each test system was reconfigured in three different ways: by considering load priority, without considering load priority, and by combining priority factor and magnitude factor. The test results accuracy was verified through hand checking.

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A modern power system is composed of many individual entities collaborating with each other to operate the entire system in a secure and economic manner. These entities may have different owners and operators with their own operating rules and policies, and it complicates the decision-making process in the system. In this work, a system of systems (SoS) engineering framework is presented for optimally operating the modern power systems. The proposed SoS framework defines each entity as an independent system with its own regulations, and the communication and process of information exchange between the systems are discussed. Since the independent systems are working in an interconnected system, the operating condition of one may impact the operating condition of others. According to the independent systems’ characteristics and connection between them, an optimization problem is formulated for each independent system. In order to solve the optimization problem of each system and to optimally operate the entire SoS-based power system, a decentralized decision-making algorithm is developed. Using this algorithm, only a limited amount of information is exchanged among different systems, and the operators of independent systems do not need to exchange all the information, which may be commercially sensitive, with each other. In addition, applying chance-constrained stochastic programming, the impact of uncertain variables, such as renewable generation and load demands, is modeled in the proposed SoS-based decision-making algorithm. The proposed SoS-based decision-making algorithm is applied to find the optimal and secure operating point of an active distribution grid (ADG). This SoS framework models the distribution company (DISCO) and microgrids (MGs) as independent systems having the right to work based on their own operating rules and policies, and it coordinates the DISCO and MGs operating condition. The proposed decision-making algorithm is also performed to solve the security-constrained unit commitment incorporating distributed generations (DGs) located in ADGs. The independent system operator (ISO) and DISCO are modeled as self-governing systems, and competition and collaboration between them are explained according to the SoS framework.

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The United States Air Force is in the process of developing magnetic bearings as well as an aircraft Integrated Power Unit and an Internal Starter/Generator for main propulsion engines. These developments are the driving force behind a new emphasis on high temperature, high strength magnetic materials for power applications. Analytical work, utilizing elasticity theory, in conjunction with design requirements, indicates a need for magnetic materials to have strengths in excess of 80 ksi up to about 1000 degrees F. It is this combination of desired material characteristics that is the motivation for this effort to measure, model, and predict the creep behavior of such advanced magnetic materials. Hiperco® Alloy 50HS, manufactured by Carpenter Technology Corporation, is one of the leading candidates for application and is studied in this effort by subjecting mechanical test specimens to a battery of tensile and creep tests. The tensile tests provide stress versus strain behaviors that clearly indicate: a yield point, a heterogeneous deformation described as LuÌ ders elongation, the Portevin-LeChatelier effect at elevated temperatures, and, most often, a section of homogeneous deformation that concluded with necking and fracture. Creep testing indicated two distinct types of behavior. The first was a traditional response with primary, secondary and tertiary stages, while the second type could be characterized by an abrupt increase in strain rate that acted as a transition from one steady state behavior to another. This second linear region was then followed by the tertiary stage. The relationship between the tensile response and the creep responses is discussed. Analyses of the mechanical behavior includes double linear regression of empirically modeled data, scanning electron microscopy for microstructural investigations, isochronous stress-strain relations, and constant strain rate testing to relate the tensile and creep test parameters. Also, elastic and creep deformation analyses are done, which incorporate material property data and material constants determined along with stress and displacement profiles for a specific Air Force design configuration. / Ph. D.

Power systems optimal load flow studies are performed for standard test cases using several methods of minimization. Comparisons of the solutions are made based on digital computer results. A general discussion is made of optimization and the necessary conditions for solution. Cost functions are written for the minimum costs and losses problem and then augmented to satisfy variable constraints. Through the use of common routines and format, sufficient basis for comparison of the several methods was established. Among the methods, steepest descent was chosen as most attractive and a further study was performed. Results of this are presented and conclusions drawn to improve the economical operation of our power system utilities. / Master of Science

A dissertation submitted to the Facaulty of Engineering and the Built Environment, University of the Witwatersrand in ful lment of the requirements of the degree of Master of science in Engineering September 2016 / Standalone microgrid studies are being done because an expansion of the existing utility grids to supply power to remote communities is not feasible. Standalone microgrids can be considered as one of the solutions for remote communities because power can be generated close to these communities and it minimizes cost related to power transmission. Renewable energy sources with large uctuations are frequently the source of power for these standalone microgrids. The uctuating nature of these renewable sources can often lead to frequent blackouts. This research is aimed at minimizing power uctuations using controllable energy storage systems. This MSc focuses on the analysis of the ramp rate and delay time requirements for controllable energy storage system used in standalone PV microgrids. Measured insolation data and recorded load demand data for typical domestic appliances are used in this study to analyze ramp rates present. The ramp rates are then used to determine the range of energy storage ramp rate and delay time required to maintain the microgrid voltage within the standardized range of 1pu 5%. From the recorded data it has been observed that PV power can be sampled from at least 1-second intervals without losing important information. The 1 second averaged ramp rates obtained from the insolation data measurements have been found to have the highest value of 0.12pu/sec. However, this ramp rate increases to 0.3pu/sec when the allowable microgrid voltage band is narrow (1pu 5%). These insolation ramp rates are very low compared to the ramp rates of typical loads that can be connected to a microgrid. This means that, if the energy storage system is speci ed to meet the load ramp rate requirements, it will be able to respond to the uctuating PV power. The results obtained from the simulations con rm that energy storage system ramp rate plays an important role in the stability of a standalone microgrid. The minimum allowable energy storage ramp rate was found to be 8.15pu/sec for load transients with a ramp time of 20ms. This value is 28 times the energy storage ramp rate required to cancel out insolation uctuations. This further con rms that energy storage system ramp rates must be speci ed using the load demand data. The maximum allowable delay time was also found to be 0.53s to maintain the microgrid voltage within the standardized range of 1pu 5%. This delay time is applicable when canceling out only the insolation uctuations. To cancel out load transient power uctuations, there should be no delay time. / MT2017

This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations research, including non-convex problems. Finally, an optimization framework is applied to solve a problem in electric power systems involving a smart solar inverter and battery storage system providing energy and ancillary services to the grid.

An approach to parallelize the simulation of AC-Radial Shipboard Power Systems (SPSs) using multicore computers is presented. Time domain simulations of SPSs are notoriously slow, due principally to the number of components, and the time-variance of the component models. A common approach to reduce the simulation run-time of power systems is to formulate the electrical network equations using modified nodal analysis, use Bergeron's travelling-wave transmission line model to create subsystems, and to parallelize the simulation using a distributed computer. In this work, an SPS was formulated using loop analysis, defining the subsystems using a diakoptics-based approach, and the simulation parallelized using a multicore computer. A program was developed in C# to conduct multithreaded parallel-sequential simulations of an SPS. The program first represents an SPS as a graph, and then partitions the graph. Each graph partition represents a SPS subsystem and is computationally balanced using iterative refinement heuristics. Once balanced subsystems are obtained, each SPS subsystem's electrical network equations are formulated using loop analysis. Each SPS subsystem is solved using a unique thread, and each thread is manually assigned to a core of a multicore computer. To validate the partitioning approach, performance metrics were created to assess the speed gain and accuracy of the partitioned SPS simulations. The simulation parameters swept for the performance metrics were the number of partitions, the number of cores used, and the time step increment. The results of the performance metrics showed adequate speed gains with negligible error. An increasing simulation speed gain was observed when the number of partitions and cores were augmented, obtaining maximum speed gains of <30x when using a quadcore computer. Results show that the speed gain is more sensitive to the number partitions than is to the number of cores. While multicore computers are suitable for parallel-sequential SPS simulations, increasing the number of cores does not contribute to the gain in speed as much as does partitioning. The simulation error increased with the simulation time step but did not influence the partitioned simulation results. The number of operations caused by protective devices was used to determine whether the simulation error introduced by partitioning SPS simulations produced a inconsistent system behavior. It is shown, for the time step sizes uses, that protective devices did not operate inadvertently, which indicates that the errors did not alter RMS measurement and, hence, were non-influential.