Guidance and Control Division

Flight computers and sequencers, spacecraft power and control systems, and guidance and control analysis and integratio

Analysis of Satellite Timing and Navigation Receiver Pseudorange Biases due to Spreading Code Puncturing and Phase Optimized Constant Envelope Transmission

There is a desire for future GPS satellites to be software-defined to enable greater operational flexibility and adapt to a variety of current and future threats. This includes implementing new modulation techniques such as phase optimized constant envelope transmission (POCET) and asymmetric signal authentication methods such as chips message robust authentication (Chimera). Any new GPS signal transmitted must be backwards compatible with the millions of receivers already in use. This thesis shows a variety of tests performed to demonstrate the effects of Chimera and POCET-enabled signals. It is shown through actual radio frequency signal generation, testing the response of current-generation high accuracy commercial off-the-shelf GPS receivers to these signals, that both Chimera and POCET, as implemented in a GPS signal constellation, are backwards compatible

Flexitranstore

This open access book comprises 10 high-level papers on research and innovation within the Flexitranstore Project that were presented at the FLEXITRANSTORE special session organized as part of the 21st International Symposium on High Voltage Engineering. FLEXITRANSTORE (An Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources) aims to contribute to the development of a pan-European transmission network with high flexibility and high interconnection levels. This will facilitate the transformation of the current energy production mix by hosting an increasing share of renewable energy sources. Novel smart grid technologies, control and storage methods, and new market approaches will be developed, installed, demonstrated, and tested introducing flexibility to the European power system. FLEXITRANSTORE is developing a next-generation Flexible Energy Grid (FEG) that will be integrated into the European Internal Energy Market (IEM) through the valorization of flexibility services. This FEG addresses the capabilities of a power system to maintain continuous service in the face of rapid and large swings in supply or demand. As such, a wholesale market infrastructure and new business models within this integrated FEG must be upgraded for network players, and offer incentives for new ones to join, while at the same time demonstrating new business perspectives for cross-border resource management and energy trading

Compiler Front-end for the IEC 61131-3 v3 Languages

Análises Léxica e Sintática concluídas. Abstract Syntax Treequase completa. Falta validar o trabalho. Falta concluir o documento

Incorporating operational flexibility into electric generation planning : impacts and methods for system design and policy analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 253-272).This dissertation demonstrates how flexibility in hourly electricity operations can impact long-term planning and analysis for future power systems, particularly those with substantial variable renewables (e.g., wind) or strict carbon policies. Operational flexibility describes a power system's ability to respond to predictable and unexpected changes in generation or demand. Planning and policy models have traditionally not directly captured the technical operating constraints that determine operational flexibility. However, as demonstrated in this dissertation, this capability becomes increasingly important with the greater flexibility required by significant renewables (>=20%) and the decreased flexibility inherent in some low-carbon generation technologies. Incorporating flexibility can significantly change optimal generation and energy mixes, lower system costs, improve policy impact estimates, and enable system designs capable of meeting strict regulatory targets. Methodologically, this work presents a new clustered formulation that tractably combines a range of normally distinct power system models, from hourly unit-commitment operations to long-term generation planning. This formulation groups similar generators into clusters to reduce problem size, while still retaining the individual unit constraints required to accurately capture operating reserves and other flexibility drivers. In comparisons against traditional unit commitment formulations, errors were generally less than 1% while run times decreased by several orders of magnitude (e.g., 5000x). Extensive numeric simulations, using a realistic Texas-based power system show that ignoring flexibility can underestimate carbon emissions by 50% or result in significant load and wind shedding to meet environmental regulations. Contributions of this dissertation include: 1. Demonstrating that operational flexibility can have an important impact on power system planning, and describing when and how these impacts occur; 2. Demonstrating that a failure to account for operational flexibility can result in undesirable outcomes for both utility planners and policy analysts; and 3. Extending the state of the art for electric power system models by introducing a tractable method for incorporating unit commitment based operational flexibility at full 8760 hourly resolution directly into planning optimization. Together these results encourage and offer a new flexibility-aware approach for capacity planning and accompanying policy design that can enable cleaner, less expensive electric power systems for the future.by Bryan S. Palmintier.Ph.D

Emergent descriptions at large charge: A foray into the structure of conformal field theories and beyond

Conformal Field Theories (CFT)s play a central role in the study of Quantum Field Theory (QFT). They represent the fixed point of the Wilsonian Renormalization Group (RG) flow and any QFT is in principle describable as a relevant deformation of the associated nearby Conformal Field Theory (CFT). This thesis aims to explore the structure of CFTs with global internal symmetries and beyond via the Large-Charge Expansion (LCE), a semi-classical expansion applicable for states with large global quantum numbers. In the first part of this thesis we study CFT and Spontaneous Symmetry Breaking (SSB). We discuss the symmetry-constraints imposed by conformal invariance on the quantum theory, introduce the concept of CFT data and the Operator–Product Expansion (OPE). Concerning SSB, we discuss the existence of Nambu–Goldstone (NG) modes, the general counting rule for the number of NG modes under the spontaneous breaking of global internal symmetries and a generalization of the Goldstone theorem at finite density. In the second part of this thesis we discuss the current state-of-the-art understanding of the LCE and systematically study CFTs with a global O(2) symmetry in the context of the LCE. We present the LCE in the broader context of the different methods available for accessing CFT data. Particularly, we discuss its relation to large-spin expansions in CFTs and the description of operators with both large spin and large charge. We discuss the emergence of effective condensed-matter descriptions, in particular superfluids, in correlators involving states with large global quantum numbers. Finally, we use the superfluid Effective Field Theory (EFT) description to systematically study two-, three- and four-point functions for CFTs with a global O(2) symmetry. Using the EFT approach we derive universal results for the spectrum of scaling dimensions and three-point coefficients at large charge. In the last part of this thesis we study CFTs in the double-scaling limit of large charge and large N. We discuss the D = 3Wilson–Fisher (WF) fixed point at large N and derive the leading order asymptotics at large charge Q in the double scaling limit Q/N fixed, where scaling dimensions can be studied analytically in the limit Q/2N ≫1, where we recover the superfluid EFT structure, and Q/2N ≪1, where we recover the free mean-field limit. These limits can be connected by resurgent analysis. We also study the spectrum of fluctuations to confirm EFT predictions. Next, we use a fixed-charge approach to gain access to the leading order effective potential for the ϕ4 theory, which we then study for spacetime dimensions 2 <D < 6. In D = 3, we reproduce and extend old results originally found by re-summing Feynman diagrams. In D = 5, under the assumption of unitarity the ϕ4-model does not appear to be Ultra–Violet (UV) complete. Finally, we discuss the interacting fixed points of three-dimensional fermionic CFTs in the double-scaling limit of large charge and large N. While the Gross–Neveu (GN)model exhibits a Fermi-sphere description at large charge, whose fate at finite N is yet to be determined, for the Nambu–Jona–Lasinio (NJL)-type models we find a Bose–Einstein Condensate (BEC). The large-charge sector of these models is therefore captured by the superfluid EFT approach

R Package OBsMD for Follow-Up Designs in an Objective Bayesian Framework

Fractional factorial experiments often produce ambiguous results due to confounding among the factors; as a consequence more than one model is consistent with the data. Thus, the practical problem is how to choose additional runs in order to discriminate among the rival models and to identify the active factors. The R package OBsMD solves this problem by implementing the objective Bayesian methodology proposed by Consonni and Deldossi (2016). The main feature of this approach is that the follow-up designs are obtained through the use of just two functions, OBsProb() and OMD() without requiring any prior specifications, being fully automatic. Thus OBsMD provides a simple tool for conducting a design of experiments to solve real world problems

Pervasive Data Analytics for Sustainable Energy Systems

With an ever growing population, global energy demand is predicted to keep increasing. Furthermore, the integration of renewable energy sources into the electricity grid (to reduce carbon emission and humanity's dependency on fossil fuels), complicates efforts to balance supply and demand, since their generation is intermittent and unpredictable. Traditionally, it has always been the supply side that has adapted to follow energy demand, however, in order to have a sustainable energy system for the future, the demand side will have to be better managed to match the available energy supply. In the first part of this thesis, we focus on understanding customers' energy consumption behavior (demand analytics). While previously, information about customer's energy consumption could be obtained only with coarse granularity (e.g., monthly or bimonthly), nowadays, using advanced metering infrastructure (or smart meters), utility companies are able to retrieve it in near real-time. By leveraging smart meter data, we then develop a versatile customer segmentation framework, track cluster changes over time, and identify key characteristics that define a cluster. Additionally, although household-level consumption is hard to predict, it can be used to improve aggregate-level forecasting by first segmenting the households into several clusters, forecasting the energy consumption of each cluster, and then aggregating those forecasts. The improvements provided by this strategy depend not only on the number of clusters, but also on the size of the customer base. Furthermore, we develop an approach to model the uncertainty of future demand. In contrast to previous work that used computationally expensive methods, such as simulation, bootstrapping, or ensemble, we construct prediction intervals directly using the time-varying conditional mean and variance of future demand. While analytics on customer energy data are indeed essential to understanding customer behavior, they could also lead to breaches of privacy, with all the attendant risks. The first part of this thesis closes by exploring symbolic representations of smart meter data which still allow learning algorithms to be performed on top of them, thus providing a trade-off between accurate analytics and the protection of customer privacy. In the second part of this thesis, we focus on mechanisms for incentivizing changes in customers' energy usage in order to maintain (electricity) grid stability, i.e., Demand Response (DR). We complement previous work in this area (which typically targeted large, industrial customers) by studying the application of DR to residential customers. We first study the influence of DR baselines, i.e., estimates of what customers would have consumed in the absence of a DR event. While the literature to date has focused on baseline accuracy and bias, we go beyond these concepts by explaining how a baseline affects customer participation in a DR event, and how it affects both the customer and company profit. We then discuss a strategy for matching the demand side with the supply side by using a multiunit auction performed by intelligent agents on behalf of customers. The thesis closes by eliciting behavioral incentives from the crowd of customers for promoting and maintaining customer engagement in DR programs