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Lu, Lian. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 91-95). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

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The Internet of Things (IoT) is now permeating our daily lives, providing critical data for every decision. IoT architecture consists of multiple layers with unique functions and independent components. Each layer of IoT architecture requires different power sources and power delivery schemes. Therefore, different types of power management architectures are required for individual IoT components. Fortunately, advances in metal oxide semiconductor (MOS) technology have made it possible to implement a variety of high-performance power management architectures. These power management architectures should not only create the power rails required for IoT components but also serve additional functions depending on the application. The power management architecture of IoT devices needs to support sub-mW- or mW-scale power consumption. In addition, the power management architecture should be either fully integrated on a chip or miniaturized with few passive components to minimize the size of IoT devices. Building-scale data centers, on the other hand, need various power conversion stages. In this scenario, power conversion from an intermediate DC bus to many point of loads (PoL) requires a high conversion ratio DC-DC converter. Because each PoL draws enormous amounts of power, the power management architecture should withstand high currents and include protection circuitry to prevent damage. This thesis presents research on the design of power management architectures required by IoT devices and data centers. Chapter 2 presents the design and circuit techniques of power management architectures for IoT devices. Chapter 2 outlines a new methodology for co-designing an integrated switched-capacitor (SC) DC-DC converter and a load circuit in ultra-low-power IoT devices. This methodology enables the implementation of an area-efficient fully integrated IoT system-on-chip (SoC) while maintaining high power conversion efficiency (PCE). Chapter 3 presents a 10-output ultra-low-power single-inductor-multiple-output (SIMO) DC-DC buck converter with integrated output capacitors for sub-mW IoT SoCs. Featuring a continuous comparator-based output switch controller and a digital pulse-width modulation (PWM) controller for ultra-low feedback latency, this SIMO converter produces ten independent output voltages with high PCE. Lastly, in Chapter 4, an integrated programmable gate timing control and gate driver chip for an active-clamp forward converter (ACFC) Power Block for data center applications is developed. While the ACFC Power Block converts 12-48 V intermediate DC bus voltage to a digital PoL voltage, the gate timing control and driver chip can optimize PCE and reduce the system form factor.

An entirely digital system is presented which has several benefits as compared to the systems that are deployed currently. Utilizing digital capabilities to a much greater extent than is currently used within the power system allows for various improvements upon the current system. One such improvement is the ease of configuring and using the system. Each device can easily alter its functionality through a user interface, and the addition of devices is as easy as plugging it in. Additionally, the burden on the transformer due to the increase in the number of devices is nullified. The information remains accurate and unchanged, even when new devices are added to the system. The entire system conforms to the IEC 61850 standard, such that it adheres to the requirements of the actual power system. / Master of Science

A method of calculating the yearly owning and operating cost of a distribution transformer is developed taking into consideration the loss of life due to overload. Using the developed methods, the potential benefits of managing an overloaded distribution transformer was calculated for a transformer on the Virginia Electric and Power Company (Vepco) System. By loading the transformer according to a saturation type load growth curve considered typical for Vepco System, its life was approximated. The fixed carrying charges were then applied at a rate sufficient to recover all invested capital during the life of the transformer. The potential savings were calculated when cutting the secondary and adding a transformer of equal one size smaller and two sizes smaller than the original. The study indicated no savings would be obtained when cutting the secondary. The only savings indicated were obtained by taking down an overloaded transformer and replacing it with the next larger size. The potential savings of managing these transformers presently installed, as well as those to be installed in the next years, as well as those to be installed in the next ten years, was calculated using a critical rate of return of 6, 7, 8, 9, and 10 percent. The calculated savings were $3,251,500 at 6 Percent, $2,674,400 at 7 Percent, $2,075,400 at 8 Percent, $1,602,200 at 9 Percent, $1,257,300 at 10 Percent. / Master of Science

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.