A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Engineering thin films of magnetic alloys and semiconductor oxides at the nanoscale

The thesis aims to exploit properties of thin films for applications such as spintronics, UV detection and gas sensing. Nanoscale thin films devices have myriad advantages and compatibility with Si-based integrated circuits processes. Two distinct classes of material systems are investigated, namely ferromagnetic thin films and semiconductor oxides. To aid the designing of devices, the surface properties of the thin films were investigated by using electron and photon characterization techniques including Auger electron spectroscopy (AES), X-ray photoelectron spectroscopy (XPS), grazing incidence X-ray diffraction (GIXRD), and energy-dispersive X-ray spectroscopy (EDS). These are complemented by nanometer resolved local proximal probes such as atomic force microscopy (AFM), magnetic force microscopy (MFM), electric force microscopy (EFM), and scanning tunneling microscopy to elucidate the interplay between stoichiometry, morphology, chemical states, crystallization, magnetism, optical transparency, and electronic properties. Specifically, I studied the effect of annealing on the surface stoichiometry of the CoFeB/Cu system by in-situ AES and discovered that magnetic nanoparticles with controllable areal density can be produced. This is a good alternative for producing nanoparticles using a maskless process. Additionally, I studied the behavior of magnetic domain walls of the low coercivity alloy CoFeB patterned nanowires. MFM measurement with the in-plane magnetic field showed that, compared to their permalloy counterparts, CoFeB nanowires require a much smaller magnetization switching field , making them promising for low-power-consumption domain wall motion based devices. With oxides, I studied CuO nanoparticles on SnO2 based UV photodetectors (PDs), and discovered that they promote the responsivity by facilitating charge transfer with the formed nanoheterojunctions. I also demonstrated UV PDs with spectrally tunable photoresponse with the bandgap engineered ZnMgO. The bandgap of the alloyed ZnMgO thin films was tailored by varying the Mg contents and AES was demonstrated as a surface scientific approach to assess the alloying of ZnMgO. With gas sensors, I discovered the rf-sputtered anatase-TiO2 thin films for a selective and sensitive NO2 detection at room temperature, under UV illumination. The implementation of UV enhances the responsivity, response and recovery rate of the TiO2 sensor towards NO2 significantly. Evident from the high resolution XPS and AFM studies, the surface contamination and morphology of the thin films degrade the gas sensing response. I also demonstrated that surface additive metal nanoparticles on thin films can improve the response and the selectivity of oxide based sensors. I employed nanometer-scale scanning probe microscopy to study a novel gas senor scheme consisting of gallium nitride (GaN) nanowires with functionalizing oxides layer. The results suggested that AFM together with EFM is capable of discriminating low-conductive materials at the nanoscale, providing a nondestructive method to quantitatively relate sensing response to the surface morphology.

Energy Management of a Battery-Ultracapacitor Hybrid Energy Storage System in Electric Vehicles

Electric vehicle (EV) batteries tend to have accelerated degradation due to high peak power and harsh charging/discharging cycles during acceleration and deceleration periods, particularly in urban driving conditions. An oversized energy storage system (ESS) can meet the high power demands; however, it suffers from increased size, volume and cost. In order to reduce the overall ESS size and extend battery cycle life, a battery-ultracapacitor (UC) hybrid energy storage system (HESS) has been considered as an alternative solution. In this work, we investigate the optimized configuration, design, and energy management of a battery-UC HESS. One of the major challenges in a HESS is to design an energy management controller for real-time implementation that can yield good power split performance. We present the methodologies and solutions to this problem in a battery-UC HESS with a DC-DC converter interfacing with the UC and the battery. In particular, a multi-objective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. This optimization problem is numerically solved for standard drive cycle datasets using Dynamic Programming (DP). Trained using the DP optimal results, an effective real-time implementation of the optimal power split is realized based on Neural Network (NN). This proposed online energy management controller is applied to a midsize EV model with a 360V/34kWh battery pack and a 270V/203Wh UC pack. The proposed online energy management controller effectively splits the load demand with high power efficiency and also effectively reduces the battery peak current. More importantly, a 38V-385Wh battery and a 16V-2.06Wh UC HESS hardware prototype and a real-time experiment platform has been developed. The real-time experiment results have successfully validated the real-time implementation feasibility and effectiveness of the real-time controller design for the battery-UC HESS. A battery State-of-Health (SoH) estimation model is developed as a performance metric to evaluate the battery cycle life extension effect. It is estimated that the proposed online energy management controller can extend the battery cycle life by over 60%.

Energy Cooperation in Energy Harvesting Communication Systems

In energy harvesting communications, users transmit messages using energy harvested from nature. In such systems, transmission policies of the users need to be carefully designed according to the energy arrival profiles. When the energy management policies are optimized, the resulting performance of the system depends only on the energy arrival profiles. In this dissertation, we introduce and analyze the notion of energy cooperation in energy harvesting communications where users can share a portion of their harvested energy with the other users via wireless energy transfer. This energy cooperation enables us to control and optimize the energy arrivals at users to the extent possible. In the classical setting of cooperation, users help each other in the transmission of their data by exploiting the broadcast nature of wireless communications and the resulting overheard information. In contrast to the usual notion of cooperation, which is at the signal level, energy cooperation we introduce here is at the battery energy level. In a multi-user setting, energy may be abundant in one user in which case the loss incurred by transferring it to another user may be less than the gain it yields for the other user. It is this cooperation that we explore in this dissertation for several multi-user scenarios, where energy can be transferred from one user to another through a separate wireless energy transfer unit. We first consider the offline optimal energy management problem for several basic multi-user network structures with energy harvesting transmitters and one-way wireless energy transfer. In energy harvesting transmitters, energy arrivals in time impose energy causality constraints on the transmission policies of the users. In the presence of wireless energy transfer, energy causality constraints take a new form: energy can flow in time from the past to the future for each user, and from one user to the other at each time. This requires a careful joint management of energy flow in two separate dimensions, and different management policies are required depending on how users share the common wireless medium and interact over it. In this context, we analyze several basic multi-user energy harvesting network structures with wireless energy transfer. To capture the main trade-offs and insights that arise due to wireless energy transfer, we focus our attention on simple two- and three-user communication systems, such as the relay channel, multiple access channel and the two-way channel. Next, we focus on the delay minimization problem for networks. We consider a general network topology of energy harvesting and energy cooperating nodes. Each node harvests energy from nature and all nodes may share a portion of their harvested energies with neighboring nodes through energy cooperation. We consider the joint data routing and capacity assignment problem for this setting under fixed data and energy routing topologies. We determine the joint routing of energy and data in a general multi-user scenario with data and energy transfer. Next, we consider the cooperative energy harvesting diamond channel, where the source and two relays harvest energy from nature and the physical layer is modeled as a concatenation of a broadcast and a multiple access channel. Since the broadcast channel is degraded, one of the relays has the message of the other relay. Therefore, the multiple access channel is an extended multiple access channel with common data. We determine the optimum power and rate allocation policies of the users in order to maximize the end-to-end throughput of this system. Finally, we consider the two-user cooperative multiple access channel with energy harvesting users. The users cooperate at the physical layer (data cooperation) by establishing common messages through overheard signals and then cooperatively sending them. For this channel model, we investigate the effect of intermittent data arrivals to the users. We find the optimal offline transmit power and rate allocation policy that maximize the departure region. When the users can further cooperate at the battery level (energy cooperation), we find the jointly optimal offline transmit power and rate allocation policy together with the energy transfer policy that maximize the departure region.

EXPERIMENTAL DEMONSTRATION OF LIGHT TRAPPING AND INTERNAL LIGHT SCATTERING IN SOLAR CELLS

Renewable energy technologies have long-term economic and environmental advantages over fossil fuels, and solar power is the most abundant renewable resource, supplying 120 PW over earth’s surface. In recent years the cost of photovoltaic modules has reached grid parity in many areas of the world, including much of the USA. A combination of economic and environmental factors has encouraged the adoption of solar technology and led to an annual growth rate in photovoltaic capacity of 76% in the US between 2010 and 2014. Despite the enormous growth of the solar energy industry, commercial unit efficiencies are still far below their theoretical limits. A push for thinner cells may reduce device cost and could potentially increase device performance. Fabricating thinner cells reduces bulk recombination, but at the cost of absorbing less light. This tradeoff generally benefits thinner devices due to reduced recombination. The effect continues up to a maximum efficiency where the benefit of reduced recombination is overwhelmed by the suppressed absorption. Light trapping allows the solar cell to circumvent this limitation and realize further performance gains (as well as continue cost reduction) from decreasing the device thickness. This thesis presents several advances in experimental characterization, theoretical modeling, and device applications for light trapping in thin-film solar cells. We begin by introducing light trapping strategies and discuss theoretical limits of light trapping in solar cells. This is followed by an overview of the equipment developed for light trapping characterization. Next we discuss our recent work measuring internal light scattering and a new model of scattering to predict the effects of dielectric nanoparticle back scatterers on thin-film device absorption. The new model is extended and generalized to arbitrary stacks of stratified media containing scattering structures. Finally, we investigate an application of these techniques using polymer dispersed liquid crystals to produce switchable solar windows. We show that these devices have the potential for self-powering.

AN ENERGY BASED MINIMUM-TIME OPTIMAL CONTROL OF DC-DC CONVERTERS

Time-optimal response is an important and sometimes necessary characteristic of dynamic systems for specific applications. Power converters are widely used in different electrical systems and their dynamic response will affect the whole system. In many electrical systems like microgrids or voltage regulators which supplies sensitive loads fast dynamic response is a must. Minimum time is the fastest converter to compensate the step output reference or load change. Boost converters as one of the wildly used power converters in the electrical systems are aimed to be controlled in optimal time in this study. Linear controllers are not able to provide the optimal response for a boost converter however they are still useful and functional for other applications like reference tracking or stabilization. To obtain the fastest possible response from boost converters, a nonlinear control approach based on the total energy of the system is studied in this research. Total energy of the system considers as the basis for developing the presented method, since it is easy and accurate to measure besides that the total energy of the system represents the actual operating condition of the boost converter. The detailed model of a boost converter is simulated in MATLAB/Simulink to achieve the time optimal response of the boost converter by applying the developed method. The simulation results confirmed the ability of the presented method to secure the time optimal response of the boost converter under four different scenarios.

Energy and exergy analysis of ethanol production process from banana fruit

An alternative for ethanol production, is the use of vegetable waste, such as excess of banana production, that are evaluated in 2,400,000 t/year, which includes: residual banana fruit and lignocellulosic material. This paper analyzes the energetic and exergetic behavior to carry the process developed at laboratory scale to a plant processing of banana for the ethanol production, involving: growing and transport of the vegetable material, hydrolysis of banana fruit, sugar fermentation, ethanol distillation and utility plant. Finally, energy and exergy indicators are obtained. The results show a positive energy balance when banana fruit is used for ethanol production, but some process modification must be done looking for improving the exergetic efficiency in ethanol production.

A methodology for building energy modelling and calibration in warm climates

In the last 7 years, a method has been developed to analyse building energy performance using computer simulation, in Brazil. The method combines analysis of building design plans and documentation, walk-through visits, electric and thermal measurements and the use of an energy simulation tool (DOE-2.1E code), The method was used to model more than 15 office buildings (more than 200 000 m(2)), located between 12.5degrees and 27.5degrees South latitude. The paper describes the basic methodology, with data for one building and presents additional results for other six cases. (C) 2002 Elsevier Science Ltd. All rights reserved.

Review of time-domain polarization measurements for assessing insulation condition in aged transformers

This paper presents a review of the time-domain polarization measurement techniques for the condition assessment of aged transformer insulation. The polarization process is first described with appropriate dielectric response theories and then commonly used polarization methods are described with special emphasis on the most widely used return voltage(rv) measurement. Most recent emphasis has been directed to techniques of determining moisture content of insulation indirectly by measuring rv parameters. The major difficulty still lies with the accurate interpretation of return voltage results. This paper investigates different thoughts regarding the interpretation of rv results for different moisture and ageing conditions. Other time domain polarization measurement techniques and their results are also presented in this paper.

An advanced method for eliminating impacts from frequency deviations in power system signal processing

Frequency deviation is a common problem for power system signal processing. Many power system measurements are carried out in a fixed sampling rate assuming the system operates in its nominal frequency (50 or 60 Hz). However, the actual frequency may deviate from the normal value from time to time due to various reasons such as disturbances and subsequent system transients. Measurement of signals based on a fixed sampling rate may introduce errors under such situations. In order to achieve high precision signal measurement appropriate algorithms need to be employed to reduce the impact from frequency deviation in the power system data acquisition process. This paper proposes an advanced algorithm to enhance Fourier transform for power system signal processing. The algorithm is able to effectively correct frequency deviation under fixed sampling rate. Accurate measurement of power system signals is essential for the secure and reliable operation of power systems. The algorithm is readily applicable to such occasions where signal processing is affected by frequency deviation. Both mathematical proof and numerical simulation are given in this paper to illustrate robustness and effectiveness of the proposed algorithm. Crown Copyright (C) 2003 Published by Elsevier Science B.V. All rights reserved.

Risk Aversion in a Mixed-Integer Nonlinear Approach to Support Decision-Making for a Hydro Power Producer

In this paper, a mixed-integer nonlinear approach is proposed to support decision-making for a hydro power producer, considering a head-dependent hydro chain. The aim is to maximize the profit of the hydro power producer from selling energy into the electric market. As a new contribution to earlier studies, a risk aversion criterion is taken into account, as well as head-dependency. The volatility of the expected profit is limited through the conditional value-at-risk (CVaR). The proposed approach has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems.

Modeling, control and simulation of full-power converter wind turbines equipped with permanent magnet synchronous generator

In this paper, two wind turbines equipped with a permanent magnet synchronous generator (PMSG) and respectively with a two-level or a multilevel converter are simulated in order to access the malfunction transient performance. Three different drive train mass models, respectively, one, two and three mass models, are considered in order to model the bending flexibility of the blades. Moreover, a fractional-order control strategy is studied comparatively to a classical integer-order control strategy. Computer simulations are carried out, and conclusions about the total harmonic distortion (THD) of the electric current injected into the electric grid are in favor of the fractional-order control strategy.

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.