De nos jours, les évolutions technologiques imposent aux ingénieurs de modéliser desphénomènes toujours plus multiphysiques et complexes tout au long du processus dedéveloppement d’un système : le cycle en V. Pour cela, il est primordial d’avoir à disposition desoutils adaptés et performants, afin de réduire les temps de mise sur le marché, tout en obtenantdes produits plus matures et plus économes en énergie. Les travaux présentés ici décrivent lamise en place d’une plate-forme de prototypage virtuel et l’intérêt d’intégrer des considérationsénergétiques dans toutes les étapes de la modélisation. Cette approche permet, par exemple, dequantifier l’efficacité d’un système et de ses composants, et donc d’optimiser au plus tôt le coûténergétique d’une solution technique. Nous avons, dans un second temps, souhaité répondre àla problématique du « modèle le plus adapté ». Après analyse des différentes méthodes deréduction de modèles, nous avons décidé de développer la méthode PEMRA permettant depallier les limitations de la méthode MORA, introduite par Louca et al. en 1997. Les variables depuissance et d’énergie introduites précédemment sont utilisées pour calculer deux nouveauxcritères dans le processus de réduction de modèles, permettant de converger vers un modèleréduit plus simple et plus précis qu’avec la méthode MORA. Nous montrons enfin qu’enchoisissant judicieusement le signal d’excitation et un critère dit de précision temporelle adapté, ilest possible, par une approche innovante à la fois énergétique et fréquentielle, de trouver unmodèle réduit mieux adapté aux exigences imposées par l’utilisateur. / Nowadays, technological evolutions are leading engineers to model increasingly multiphysic andcomplex phenomena throughout the systems design process: the V-cycle. Adapted and efficientsystems design tools are therefore necessary in order to reduce time-to-market, while stillensuring fully developed and energy-saving products. First, this work describes the set-up of avirtual prototyping platform and highlights the interest of integrating energetic aspects in allmodelling stages. For example, this approach enables to quantify the system and components’efficiency, and therefore to optimise earlier in the process the energy consumption of a technicalsolution. Secondly, the problematic of the “Proper Model” has been addressed. After the study ofthe model reduction methodologies, we decide to develop PEMRA in order to compensate forlimitations of the MORA methodology, introduced by Louca et al. in 1997. The previous powerand energy variables are then used to compute two new model reduction criteria, in order toobtain a simpler and more accurate reduced model than with MORA methodology. Finally, weshow that a well-defined excitation signal and a new adapted temporal validation criterion willlead, with this innovative energy- and frequency-based approach, to a better suited reducedmodel.

In the face of challenges of energy security, decarbonization, resilience, and the replacement of aging infrastructure systems, federal, state, and local actors are facilitating the development of smart electricity networks to transition towards a more sustainable electricity system. In the United States, development of "smart grids" is being pursued as a national policy mandate and goal, promising that the deployment of smart grid technologies -- referring in general to digital information and communication technologies that sense, monitor, control and manage the electric grid -- will make electricity systems more environmentally sustainable and reliable, and at the same time, provide opportunities for growth and innovation. This dissertation examines and analyzes three interconnected issues relating to these sociotechnical changes in electricity infrastructure: the material and discursive construction of the smart grid, urban smart grid experimentation, and the mobility of smart grid models and knowledge. A conceptual framework is proposed for investigating sociotechnical transitions that accounts for dimensions of power and politics that are commonly overlooked in conventional analysis, and highlights how governance regimes shape and are shaped by sociotechnical change. Utilizing Foucauldian discourse analysis and relational comparative case study methodology, this dissertation analyzes the development of the smart grid as a governmental program highlighting its rationalities, techniques, and imagined subjects. The findings of these analyses suggest that the transition to a smarter grid involves much more than top-down policy mandates; significant urban experimentation is involved, as well as inter-city learning that is shaped by local political economy and broader political rationalities. This dissertation also argues for a synthesis between policy mobilities and sociotechnical transitions theory, highlighting through case studies how urban smart grid experiments are influenced by experiences and knowledge generated from "vanguard" cities. The conclusion of this dissertation is that the creation of the smart grid is far from a purely technical infrastructural intervention, and instead, requires significant changes in the everyday social practices and conduct of energy consumers, while also reconfiguring the city, engaging in a material politics in order to govern energy transitions.

Microorganisms are capable of producing advanced biofuels that can be used as ‘drop-in’ alternatives to conventional liquid fuels. However, vital physiological processes and membrane properties are often disrupted by the presence of biofuel and limit the production yields. In order to make microbial biofuels a competitive fuel source, finding mechanisms for improving resistance to the toxic effects of biofuel production is vital. This investigation aims to identify resistance mechanisms from microorganisms that have evolved to withstand hydrocarbon-rich environments, such as those that thrive near natural oil seeps and in oil-polluted waters. First, using genomic DNA from Marinobacter aquaeolei, we constructed a transgenic library that we expressed in Escherichia coli. We exposed cells to inhibitory levels of pinene, a monoterpene that can serve as a jet fuel precursor with chemical properties similar to existing tactical fuels. Using a sequential strategy of a fosmid library followed by a plasmid library, we were able to isolate a region of DNA from the M. aquaeolei genome that conferred pinene tolerance when expressed in E. coli. We determined that a single gene, yceI, was responsible for the tolerance improvements. Overexpression of this gene placed no additional burden on the host. We also tested tolerance to other monoterpenes and showed that yceI selectively improves tolerance. Additionally, we used genomic DNA from Pseudomonas putida KT2440, which has innate solvent-tolerance properties, to create transgenic libraries in an E. coli host. We exposed cells containing the library to pinene, selecting for genes that improved tolerance. Importantly, we found that expressing the sigma factor RpoD from P. putida greatly expanded the diversity of tolerance genes recovered. With low expression of rpoDP. putida, we isolated a single pinene tolerance gene; with increased expression of the sigma factor our selection experiments returned multiple distinct tolerance mechanisms, including some that have been previously documented and also new mechanisms. Interestingly, high levels of rpoDP. putida induction resulted in decreased diversity. We found that the tolerance levels provided by some genes are highly sensitive to the level of induction of rpoDP. putida, while others provide tolerance across a wide range of rpoDP. putida levels. This method for unlocking diversity in tolerance screening using heterologous sigma factor expression was applicable to both plasmid and fosmid-based transgenic libraries. These results suggest that by controlling the expression of appropriate heterologous sigma factors, we can greatly increase the searchable genomic space within transgenic libraries. This dissertation describes a method of effectively screening genomic DNA from multiple organisms for genes to mitigate biofuel stress and shows how tolerance genes can improve bacterial growth in the presence of toxic biofuel compounds. These identified genes can be targeted in future studies as candidates for use in biofuel production strains to increase biofuel yields.

To maximize the effectiveness of the rapidly increasing capacity of installed wind energy resources, new models must be developed that are capable of more nuanced control of each wind turbine so that each device is more responsive to inflow events. Models used to plan wind turbine arrays and control behavior of devices within the farm currently make questionable estimates of the incoming atmospheric flow and update turbine configurations infrequently. As a result, wind turbines often operate at diminished capacities, especially in arrays where wind turbine wakes interact and inflow conditions are far from ideal. New turbine control and wake prediction models must be developed to tune individual devices and make accurate power predictions. To that end, wind tunnel experiments are conducted detailing the turbulent flow in the wake of a wind turbine in a model-scale array. The proper orthogonal decomposition (POD) is applied to characterize the spatial evolution of structures in the wake. Mode bases from distinct downstream locations are reconciled through a secondary decomposition, called double proper orthogonal decomposition (DPOD), indicating that modes of common rank in the wake share an ordered set of sub-modal projections whose organization delineates underlying wake structures and spatial evolution. The doubly truncated basis of sub-modal structures represents a reduction to 0.015% of the total degrees of freedom of the wind turbine wake. Low-order representations of the Reynolds stress tensor are made using only the most dominant DPOD modes, corrected to account for energy excluded from the truncated basis with a tensor of constant coefficients defined to rescale the low-order representation of the stresses to match the original statistics. Data from the wind turbine wake are contrasted against simulation data from a fully-developed channel flow, illuminating a range of anisotropic states of turbulence. Complexity of flow descriptions resulting from truncated POD bases is suppressed in severe basis truncations, exaggerating anisotropy of the modeled flow and, in extreme cases, can lead to the loss of three dimensionality. Constant corrections to the low-order descriptions of the Reynolds stress tensor reduce the root-mean-square error between low-order descriptions of the flow and the full statistics as much as 40% and, in some cases, reintroduce three-dimensionality to severe truncations of POD bases. Low-dimensional models are constructed by coupling the evolution of the dynamic mode coefficients through their respective time derivatives and successfully account for non-linear mode interaction. Deviation between time derivatives of mode coefficients and their least-squares fit is amplified in numerical integration of the system, leading to unstable long-time solutions. Periodic recalibration of the dynamical system is undertaken by limiting the integration time and using a virtual sensor upstream of the wind turbine actuator disk in to read the effective inflow velocity. A series of open-loop transfer functions are designed to inform the low-order dynamical system of the flow incident to the wind turbine rotor. Validation data shows that the model tuned to the inflow reproduces dynamic mode coefficients with little to no error given a sufficiently small interval between instances of recalibration. The reduced-order model makes accurate predictions of the wake when informed of turbulent inflow events. The modeling scheme represents a viable path for continuous time feedback and control that may be used to selectively tune a wind turbine in the effort to maximize power output of large wind farms.

Electricity is a perishable commodity; once it is generated it needs to be consumed or stored. Electric energy storage provides both power and energy capacity. Power capacity applications reduce the need for generation, while energy capacity allows for energy consumption to be decoupled from generation. Previous research was done to develop an algorithm for determining the power (MW) and energy (MWh) capacities of a battery energy storage system (BESS) to mitigate the adverse impacts of high levels of photovoltaic (PV) generation. The algorithm used a flat feeder profile, and its performance was demonstrated on the equinoxes and solstices. Managing feeder power leads to fewer voltage fluctuations along the length of the feeder, potentially mitigating load management issues caused by variability of renewable generation and load profile. These issues include lighting flicker, compressor seizing, equipment shut-off, loss of motor torque, frequent transformer tap changes and even voltage collapse due to loss of reactive power support. The research described in this thesis builds on this algorithm by incorporating a smoothed feeder profile and testing it over an entire year. Incorporating a smoothing function reduces the requisite BESS energy capacity necessary to provide firming and shaping to accommodate the stochastic nature of PV. Specifically, this method is used to conduct a year-long study on a per second basis, as well as a one-minute basis, for a distribution feeder. Statistical analytical methods were performed to develop recommendations for appropriately sizing the BESS. This method may be used to determine the amount of PV generation that could be installed on a distribution feeder with a minimal investment in the BESS power and energy capacities that would be required to manage the distribution feeder power. Results are presented for PV penetration levels of 10%-50% of the distribution feeder capacity and show that the use of a smooth feeder profile reduces the required energy capacity by a minimum factor of 10 when compared to a flat feeder profile. Results indicated that it is sufficient to have a one-minute sampling rate, as it provides the necessary granularity to model cloud-induced fluctuations. This method can be applied to any distribution feeder where a load profile and a PV profile are available.

Battery storage devices have been widely utilized for different applications. However, for high power applications, battery storage systems come with several challenges, such as the thermal issue, low power density, low life span and high cost. Compared with batteries, supercapacitors have a lower energy density but their power density is very high, and they offer higher cyclic life and efficiency even during fast charge and discharge processes. In this dissertation, new techniques for the control and energy management of the hybrid battery-supercapacitor storage system are developed to improve the performance of the system in terms of efficiency, power quality and reliability. To evaluate the findings of this dissertation, a laboratory-scale DC microgrid system is designed and implemented. The developed microgrid utilizes a hybrid lead-acid battery and supercapacitor energy storage system and is loaded under various grid conditions. The developed microgrid has also real-time monitoring, control and energy management capabilities. A new control scheme and real-time energy management algorithm for an actively controlled hybrid DC microgrid is developed to reduce the adverse impacts of pulsed power loads. The developed control scheme is an adaptive current-voltage controller that is based on the moving average measurement technique and an adaptive proportional compensator. Unlike conventional energy control methods, the developed controller has the advantages of controlling both current and voltage of the system. This development is experimentally tested and verified. The results show significant improvements achieved in terms of enhancing the system efficiency, reducing the AC grid voltage drop and mitigating frequency fluctuation. Moreover, a novel event-based protection scheme for a multi-terminal DC power system has been developed and evaluated. In this technique, fault identification and classifications are performed based on the current derivative method and employing an artificial inductive line impedance. The developed scheme does not require high speed communication and synchronization and it transfers much less data when compared with the traditional method such as the differential protection approach. Moreover, this scheme utilizes less measurement equipment since only the DC bus data is required.

Efficient and reliable techniques for power delivery and utilization are needed to account for the increased penetration of renewable energy sources in electric power systems. Such methods are also required for current and future demands of plug-in electric vehicles and high-power electronic loads. Distributed control and optimal power network architectures will lead to viable solutions to the energy management issue with high level of reliability and security. This dissertation is aimed at developing and verifying new techniques for distributed control by deploying DC microgrids, involving distributed renewable generation and energy storage, through the operating AC power system. To achieve the findings of this dissertation, an energy system architecture was developed involving AC and DC networks, both with distributed generations and demands. The various components of the DC microgrid were designed and built including DC-DC converters, voltage source inverters (VSI) and AC-DC rectifiers featuring novel designs developed by the candidate. New control techniques were developed and implemented to maximize the operating range of the power conditioning units used for integrating renewable energy into the DC bus. The control and operation of the DC microgrids in the hybrid AC/DC system involve intelligent energy management. Real-time energy management algorithms were developed and experimentally verified. These algorithms are based on intelligent decision-making elements along with an optimization process. This was aimed at enhancing the overall performance of the power system and mitigating the effect of heavy non-linear loads with variable intensity and duration. The developed algorithms were also used for managing the charging/discharging process of plug-in electric vehicle emulators. The protection of the proposed hybrid AC/DC power system was studied. Fault analysis and protection scheme and coordination, in addition to ideas on how to retrofit currently available protection concepts and devices for AC systems in a DC network, were presented. A study was also conducted on the effect of changing the distribution architecture and distributing the storage assets on the various zones of the network on the system’s dynamic security and stability. A practical shipboard power system was studied as an example of a hybrid AC/DC power system involving pulsed loads. Generally, the proposed hybrid AC/DC power system, besides most of the ideas, controls and algorithms presented in this dissertation, were experimentally verified at the Smart Grid Testbed, Energy Systems Research Laboratory. All the developments in this dissertation were experimentally verified at the Smart Grid Testbed.

Fault location within electric power systems is an important topic that helps reduce outage duration and increases reliability of the system. This paper explores the topic of fault location using traveling waves generated by fault conditions and the discrete wavelet transform used for time-frequency analysis. The single-ended and double-ended traveling wave methods are presented and evaluated on a single circuit and double circuit 500kV system modeled using MATLAB SIMULINK. Results are compared on the basis of wavelet used for analysis, sampling rate, and fault resistance.

Built with nanomagnets, a spintronic device called the all-spin logic (ASL) device carries information with only spin currents, resulting in a low power supply--10 mV. This voltage is 100 times smaller than the conventional CMOS devices (usually 0.8~1V). The potential for improved energy efficiency made possible by the low operating voltage of ASL makes it one of the most promising devices among its post-CMOS competitors. The basic working principles of ASL device are introduced in this thesis and two complementary approaches to studying energy efficiency of computation are applied to a common set of ASL circuits: (1) a circuit simulation approach that provides efficiency estimates for specific ASL circuit realizations, and (2) a physical-information-theoretic approach that reveals fundamental efficiency bounds for ASL circuits as limited by irreversible information loss. The results of this study support the expectation that the energy efficiency of computation in ASL can far exceed that of CMOS. However, it also reveals that ASL efficiencies--shown to exceed fundamental limits by many orders of magnitude in the ASL implementations studied here--are unlikely to approach fundamental limits because of the unavoidable energetic overhead cost of maintaining spin currents.

<div>As on 2017, US Energy Information Administration (US EIA) claims that 50 % of the total US energy consumption are contributed by Commercial and Industrial (C&I) end-users.</div><div>Most of the energy consumption by these users is in the form of the electric power. Electric utilities, who usually supply the electric power, tend to care about the power consumption profiles of these users mainly because of the scale of consumption and their significant contribution</div><div>towards the system peak. Predicting and managing the peaks of C&I users is crucial both for the users themselves and for utility companies.</div><div>In this research, we aim to understand and predict the daily peaks of individual C&I users. To empirically understand the statistical characteristics of the peaks, we perform an extensive exploratory data analysis using a real power consumption time series dataset. To accurately predict the peaks, we investigate indirect and direct learning approaches. In the indirect approach, daily peaks are identified after forecasting the entire time series for the day whereas in the direct approach, the daily peaks are directly predicted based on the historical data available for different users during different days of the week. The machine learning models used in this research are based on Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN).</div>