This thesis entails the design and fabrication of a smartphone charger that is powered by a bicycle dynamo hub. In addition to the design and validation of the charger prototype, this thesis involves the testing and characterization of the dynamo hub power source, the design and construction of specialized test equipment, and the design and prototyping of a handlebar-mounted case for the smartphone and charging electronics. With the intention of making the device a commercial product, price, aesthetics, and marketability are of importance to the design. An appropriate description of the charger circuit is a microcontroller-based energy management system, tailored to meet strict power demands of current smartphones. The system incorporates a switched-mode power supply, lithium polymer battery, microcontroller, and specialized protection circuitry. Prototype testing confirms that the circuit meets the charging requirements of the smartphone at bicycle speeds ranging from 7 miles per hour to as high as 55 miles per hour.

<p>Solar energy is a clean and abundant renewable energy source which is currently used in many types of photovoltaic (PV) designs. In practical PV systems, solar panels are used to harvest solar energy and convert it into a usable form of electricity. Due to the intermittent nature of solar energy input however, battery storage, in combination with solar panels, must be used to provide an uninterrupted source of power.</p> <p>The process of assigning solar panel and battery configurations for a PV system is referred to as energy resource provisioning. Unfortunately, energy provisioning costs are still relatively high, and this is one of the main obstacles that inhibits the adoption of solar power for many applications. These costs however, can be substantially reduced through cost-efficient resource provisioning methods. The focus of this thesis is on the development of efficient algorithms and energy management methods that will reduce energy provisioning costs in solar powered systems.</p> <p>First, we consider resource provisioning in solar powered wireless mesh networks. In practical solar powered systems, there are usually restrictions in the way that the mesh nodes can be positioned, and this results in a time-varying and node-dependent attenuation of the available solar energy. Unfortunately, conventional resource provisioning methods cannot take this into account and therefore the deployed system may be unnecessarily expensive. In this part of the thesis, the resource provisioning problem is considered from this point of view. We first review conventional resource provisioning mechanisms and give an example which shows the value of introducing positional solar insolation awareness. A Position Aware Provisioning (PAP) algorithm is then introduced that takes known positional variations into consideration when performing the energy provisioning. Simulation results show that reductions in total network provisioning cost can be obtained using the proposed methodology compared to conventional algorithms.</p> <p>In the second part of the thesis, we consider communication infrastructure that is operated from the power grid with a solar powered addition. Resource provisioning and energy management algorithms are introduced to minimize the capital expenditure (CAPEX) and operating expenditure (OPEX) costs. We first derive lower bounds on the costs using a linear programming (LP) formulation where solar components are sized using solar insolation and projected loading data. A variety of different node configurations are considered. Three energy scheduling algorithms are then introduced to optimize online OPEX costs, namely, Grid Purchase Last (GPL), Solar Load Optimization (SLO) and Solar Load Simulation (SLS) algorithms. Simulation results show the extent to which a solar powered add-on can reduce total cost.</p> <p>Finally, we consider solar powered systems where part of their energy demands are deferrable, up to some maximum tolerable delay. The objective is to exploit the flexibility of deferrable energy demands in a way that decreases the total provisioning cost. A mixed integer linear optimization program is derived which gives a lower bound on the provisioning cost. A Delay Aware Provisioning (DAP) algorithm is then proposed to determine practical cost-efficient energy provisioning. The performance of DAP is compared to the provisioning bound and the conventional Stand-alone Node Provisioning (SNP) algorithm. Results are presented which show the significant provisioning cost savings that can be obtained.</p> / Doctor of Philosophy (PhD)

Power amplifiers (PAs) convert energy from DC to high frequencies in all radio and microwave transmitter systems be they wireless base stations, handsets, radars, heaters, and so on. PAs are the dominant consumers of energy in these systems and, therefore, the dominant sources of system cost and inefficiency. Research has focused on efficient solid-state PA circuit topologies and their optimization since the 1960s. The 2000s saw the current-mode class-D (CMCD) topology, potentially suitable for today's wireless communications systems, show promise in the UHF frequency band. This thesis describes the design and testing of a high-efficiency CMCD amplifier with an integrated driver stage. In addition, analysis of a merged PA-mixer circuit based on the CMCD is provided.

The thesis project outlined in this report aims to comprehensively assess and optimize methods for long-term power production estimation of hybrid PV-wind energy systems. Through practical case studies, this approach seeks to exemplify the challenges and opportunities inherent in such systems. The research is conducted within the Wind Technical Team of Akuo Energy, an independent French renewable energy producer, leveraging their extensive expertise in technologies, industry practices, and data processing. The primary objective is to evaluate the relevance of two key parameters used in Akuo’s internal estimation methods, focusing on their impact on long-term production and revenues within the context of hybrid PV-wind energy systems. These parameters include unavailability losses, modeled on an hourly basis using Markovian transition matrices, and interannual variability of resources, statistically modeled by randomly shuffling yearly production profiles of individual wind and solar plants. Python will be employed to generate hybrid production time series, incorporating the models for unavailability losses and interannual variability. This approach facilitates the creation of multiple scenarios for sensitivity analysis, allowing for the variation of parameters to compare the productivity and profitability of different scenarios. The study sheds light on the importance of employing realistic models to account for unavailability losses, revealing that simpler models tend to overestimate revenues from hybrid power plants systematically. Moreover, the research shows the impact of interannual variability of resources on both production and revenues, emphasizing the necessity of generating multiple scenarios to anticipate best and worst-case outcomes. Ultimately, the results of this study aim to assist the company on the necessity and relevance of using such models for calculating long-term production and revenues in future hybrid PV-wind projects, as opposed to current simpler methods. / Det avhandlingsprojekt som beskrivs i denna rapport syftar till att heltäckande utvärdera och optimera metoder för långsiktig kraftproduktionsestimering av hybrid PV-vindenergisystem. Genom praktiska fallstudier söker detta tillvägagångssätt att exemplifiera de utmaningar och möjligheter som är inneboende i sådana system. Forskningen utförs inom vindtekniska teamet på Akuo Energy, en oberoende fransk producent av förnybar energi, där man dra nytta av deras omfattande expertis inom teknik, branschpraxis och datahantering. Det primära målet är att utvärdera relevansen av två viktiga parametrar som används i Akuos interna estimeringsmetoder och fokuserar på deras påverkan på långsiktig produktion och intäkter inom ramen för hybrid PV-vindenergisystem. Dessa parametrar inkluderar otillgänglighetsförluster, modellerade på timbasis med hjälp av Markovska övergångsmatriser, och årlig variabilitet av resurser, statistiskt modellerade genom att slumpmässigt ordna årliga produktionsprofiler för enskilda vind- och solanläggningar. Python kommer att användas för att generera hybridproduktionsserier och inkludera modeller för otillgänglighetsförluster och årlig variabilitet. Detta tillvägagångssätt möjliggör skapandet av flera scenarier för känslighetsanalys, vilket gör det möjligt att variera parametrar för att jämföra produktiviteten och lönsamheten för olika scenarier. Studien belyser vikten av att använda realistiska modeller för att ta hänsyn till otillgänglighetsförluster och visar att enklare modeller tenderar att systematiskt överskatta intäkter från hybridkraftverk. Dessutom visar forskningen påverkan av årlig variabilitet av resurser på både produktion och intäkter och betonar nödvändigheten av att generera flera scenarier för att förutse bästa och sämsta fall. Slutligen syftar resultaten av denna studie till att bistå företaget när det gäller nödvändigheten och relevansen av att använda sådana modeller för att beräkna långsiktig produktion och intäkter i framtida hybrid PV-vindprojekt, jämfört med nuvarande enklare metoder.

Recently, due to the increasing adoption of distributed energy resource (DER) technologies including battery energy storage (BES) and electric vehicle (EV) systems, bidirectional power converters are becoming more popular. These converters are broadly utilized as interface devices and provide a bidirectional power flow in applications where the primary power supply can both supply and receive energy. A dc capacitor, called the dc-link, is an important component of such bidirectional converters. For a wide range of applications, the converter is required to control the dc-link voltage. Commonly, a proportional-integrating (PI) controller is used by the dc capacitor voltage controller to generate a set-point for the inner current controller. This approach tightly regulates the dc-link voltage to a given value. The research presented in this dissertation shows that such an approach compromises the stability margins of the converter for reverse power flow and weak grid conditions. It is shown that by allowing a small variation of dc capacitor voltage in proportion to the amount of power flowing through the converter, the stability and robustness margins are improved. This approach also simplifies the design process and can be applied to both dc/dc and dc/ac (single-phase and three-phase) converters. Moreover, it grants an inherent power sharing capability when multiple converters share the same dc-link terminals; removing the need to a communication link between parallel converters. The proposed controller is equipped with a current limiting mechanism to protect the converter during low-voltage/over-current transients. Detailed analyses, simulations, comparisons, and experimental results are included to illustrate the effectiveness of the proposed control approach. To mathematically establish the properties of the proposed method in a single-phase dc/ac application, this dissertation also derives a new and systematic modeling approach for a grid-connected bidirectional single-phase inverter controlled in stationary frame. Implementing the control system in the stationary frame has advantages over rotating frame. However, the combination of dc and ac state variables and nonlinearities make its stability analysis challenging. In the proposed model, an imaginary subsystem is properly generated and augmented to allow a full transformation to a synchronous rotating frame. The proposed modeling strategy is modular and has a closed form which facilitates further extensions. It is successfully used to demonstrate enhanced stability margins of the proposed controller.

This study focuses on utilizing an energy harvesting system in which a dedicated Radio Frequency (RF) power source transmits RF power via rebar in a reinforced concrete column. The RF power is received and decoupled by a receiver, and is then rectified, boosted, and stored as electrical energy in a supercapacitor, later to be used to make measurements, process data, and communicate to the source via rebar. Two design attempts are presented in this study: (a) one uses single line conduction at 2.4 GHz for RF power transfer; (b) the other uses a more conventional two-line conduction at 8.0 kHz for RF power transfer. Both designs were unsuccessful: (a) the 2.4 GHz attempt demonstrated that no detectable RF power propagated through the concrete medium; (b) the 8.0 kHz attempt demonstrated that too much of the RF power was attenuated through the concrete medium for the energy harvesting circuitry work properly. A potential third design approach is posited in the conclusion of this study. In addition to investigating power transfer designs, a study on the energy harvesting circuitry was performed. A Two-Stage Dickson Multiplier was utilized in conjunction with a Texas Instruments BQ25504 Ultra-Low Power Energy Harvesting Circuit. For these two components to function best, it was shown that the BQ25504’s input filtering capacitor needed to be on the same order of magnitude as the charging capacitors of the Two-Stage Dickson Multiplier, otherwise, if the filtering capacitor was comparatively too large, it would short the output of the Two-Stage Dickson Multiplier. With that said, the lowest power input observed was at 7.83 dBm, but with lower input powers expected to be achievable. Nevertheless, since the second design attempt showed power losses were too significant, it was deemed that at present, unless the power transfer design were improved, then contemporary commercial off the shelf energy harvesting approaches are insufficient.

Artificial intelligence tools, which are fast, robust and adaptive can overcome the drawbacks of traditional solutions for several power systems problems. In this work, applications of AI techniques have been studied for solving two important problems in power systems. The first problem is static security evaluation (SSE). The objective of SSE is to identify the contingencies in planning and operations of power systems. Numerical conventional solutions are time-consuming, computationally expensive, and are not suitable for online applications. SSE may be considered as a binary-classification, multi-classification or regression problem. In this work, multi-support vector machine is combined with several evolutionary computation algorithms, including particle swarm optimization (PSO), differential evolution, Ant colony optimization for the continuous domain, and harmony search techniques to solve the SSE. Moreover, support vector regression is combined with modified PSO with a proposed modification on the inertia weight in order to solve the SSE. Also, the correct accuracy of classification, the speed of training, and the final cost of using power equipment heavily depend on the selected input features. In this dissertation, multi-object PSO has been used to solve this problem. Furthermore, a multi-classifier voting scheme is proposed to get the final test output. The classifiers participating in the voting scheme include multi-SVM with different types of kernels and random forests with an adaptive number of trees. In short, the development and performance of different machine learning tools combined with evolutionary computation techniques have been studied to solve the online SSE. The performance of the proposed techniques is tested on several benchmark systems, namely the IEEE 9-bus, 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus power systems. The second problem is the non-convex, nonlinear, and non-differentiable economic dispatch (ED) problem. The purpose of solving the ED is to improve the cost-effectiveness of power generation. To solve ED with multi-fuel options, prohibited operating zones, valve point effect, and transmission line losses, genetic algorithm (GA) variant-based methods, such as breeder GA, fast navigating GA, twin removal GA, kite GA, and United GA are used. The IEEE systems with 6-units, 10-units, and 15-units are used to study the efficiency of the algorithms.

Over the past decades, the shrinking transistor size enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges. Over the past decades, the shrinking transistor size, benefited from the advancement of IC technology, enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges. Different from many existing DTM heuristics that are based on simple intuitions, we seek to address the thermal problems through a rigorous analytical approach, to achieve the high predictability requirement in real-time system design. In this regard, we have made a number of important contributions. First, we develop a series of lemmas and theorems that are general enough to uncover the fundamental principles and characteristics with regard to the thermal model, peak temperature identification and peak temperature reduction, which are key to thermal-constrained real-time computer system design. Second, we develop a design-time frequency and voltage oscillating approach on multi-core platforms, which can greatly enhance the system throughput and its service capacity. Third, different from the traditional workload balancing approach, we develop a thermal-balancing approach that can substantially improve the energy efficiency and task partitioning feasibility, especially when the system utilization is high or with a tight temperature constraint. The significance of our research is that, not only can our proposed algorithms on throughput maximization and energy conservation outperform existing work significantly as demonstrated in our extensive experimental results, the theoretical results in our research are very general and can greatly benefit other thermal-related research.

The future power grid will effectively utilize renewable energy resources and distributed generation to respond to energy demand while incorporating information technology and communication infrastructure for their optimum operation. This dissertation contributes to the development of real-time techniques, for wide-area monitoring and secure real-time control and operation of hybrid power systems. To handle the increased level of real-time data exchange, this dissertation develops a supervisory control and data acquisition (SCADA) system that is equipped with a state estimation scheme from the real-time data. This system is verified on a specially developed laboratory-based test bed facility, as a hardware and software platform, to emulate the actual scenarios of a real hybrid power system with the highest level of similarities and capabilities to practical utility systems. It includes phasor measurements at hundreds of measurement points on the system. These measurements were obtained from especially developed laboratory based Phasor Measurement Unit (PMU) that is utilized in addition to existing commercially based PMU’s. The developed PMU was used in conjunction with the interconnected system along with the commercial PMU’s. The tested studies included a new technique for detecting the partially islanded micro grids in addition to several real-time techniques for synchronization and parameter identifications of hybrid systems. Moreover, due to numerous integration of renewable energy resources through DC microgrids, this dissertation performs several practical cases for improvement of interoperability of such systems. Moreover, increased number of small and dispersed generating stations and their need to connect fast and properly into the AC grids, urged this work to explore the challenges that arise in synchronization of generators to the grid and through introduction of a Dynamic Brake system to improve the process of connecting distributed generators to the power grid. Real time operation and control requires data communication security. A research effort in this dissertation was developed based on Trusted Sensing Base (TSB) process for data communication security. The innovative TSB approach improves the security aspect of the power grid as a cyber-physical system. It is based on available GPS synchronization technology and provides protection against confidentiality attacks in critical power system infrastructures.

The focus of this thesis is the development and implementation of a neural network model predictive controller to be used for controlling the integrated recycle heat exchanger (Intrex) in a 300MW circulating fluidized bed (CFB) boiler. Discussion of the development of the controller will include data collection and preprocessing, controller design and controller tuning. The controller will be programmed directly into the plant distributed control system (DCS) and does not require the continuous use of any third party software. The intrexes serve as the loop seal in the CFB as well as intermediate and finishing superheaters. Heat is transferred to the steam in the intrex superheaters from the circulating ash which can vary in consistency, quantity and quality. Fuel composition can have a large impact on the ash quality and in turn, on intrex performance. Variations in MW load and airflow settings will also impact intrex performance due to their impact on the quantity of ash circulating in the CFB. Insufficient intrex heat transfer will result in low main steam temperature while excessive heat transfer will result in high superheat attemperator sprays and/or loss of unit efficiency. This controller will automatically adjust to optimize intrex ash flow to compensate for changes in the other ash properties by controlling intrex air flows. The controller will allow the operator to enter a target intrex steam temperature increase which will cause all of the intrex air flows to adjust simultaneously to achieve the target temperature. The result will be stable main steam temperature and in turn stable and reliable operation of the CFB.