Showing papers by "Illinois Institute of Technology published in 2018"
TL;DR: A system comprising a lithium carbonate-based protected anode, a molybdenum disulfide cathode and an ionic liquid/dimethyl sulfoxide electrolyte that operates as a lithium–air battery in a simulated air atmosphere with a long cycle life of up to 700 cycles is reported.
Abstract: Lithium-air batteries are considered to be a potential alternative to lithium-ion batteries for transportation applications, owing to their high theoretical specific energy. So far, however, such systems have been largely restricted to pure oxygen environments (lithium-oxygen batteries) and have a limited cycle life owing to side reactions involving the cathode, anode and electrolyte. In the presence of nitrogen, carbon dioxide and water vapour, these side reactions can become even more complex. Moreover, because of the need to store oxygen, the volumetric energy densities of lithium-oxygen systems may be too small for practical applications. Here we report a system comprising a lithium carbonate-based protected anode, a molybdenum disulfide cathode and an ionic liquid/dimethyl sulfoxide electrolyte that operates as a lithium-air battery in a simulated air atmosphere with a long cycle life of up to 700 cycles. We perform computational studies to provide insight into the operation of the system in this environment. This demonstration of a lithium-oxygen battery with a long cycle life in an air-like atmosphere is an important step towards the development of this field beyond lithium-ion technology, with a possibility to obtain much higher specific energy densities than for conventional lithium-ion batteries.
381 citations
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Eindhoven University of Technology1, Max Planck Society2, National Scientific and Technical Research Council3, University of Groningen4, Flinders University5, University of Oxford6, Illinois Institute of Technology7, Nottingham Trent University8, Bielefeld University9, University of Nevada, Las Vegas10, University of Wisconsin-Madison11, Missouri State University12, University of Arkansas13, Leiden University14, Katholieke Universiteit Leuven15, Linnaeus University16, Tzu Chi University17, University of British Columbia18, University of Cambridge19, University of Edinburgh20, Bangor University21, University of Glasgow22, Linköping University23, Florida State University24, Yale University25, University of Louisiana at Lafayette26, University of Texas at Austin27, St. Edward's University28, West Virginia University29, Rutgers University30, Indiana University31, RWTH Aachen University32, Keele University33, University of Tübingen34, Radboud University Nijmegen35, University of Chester36, New York University37, University of Nottingham38, Erasmus University Rotterdam39, University of Bristol40, Sahlgrenska University Hospital41, Adam Mickiewicz University in Poznań42, University of Connecticut43, Humboldt University of Berlin44, Charité45, University of Fribourg46, University of Kent47, Academy of Sciences of the Czech Republic48, RAND Corporation49, Baylor University50, Virginia Tech51, Northern Illinois University52, Open University53, King's College London54, Stanford University55, Karolinska Institutet56, Stockholm University57, Universidade Federal de Santa Catarina58, University of Tromsø59, DePaul University60, Boğaziçi University61, University of Cologne62, King Abdulaziz City for Science and Technology63, University of Leeds64, University of Virginia65, Center for Open Science66, National Institutes of Health67, University of Southern Indiana68, Autonomous University of Madrid69, Utrecht University70, Tilburg University71, Massey University72, Saint Louis University73, University of California, Davis74, Ghent University75
TL;DR: In response to recommendations to redefine statistical significance to P ≤ 0.005, it is proposed that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
Abstract: In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
296 citations
TL;DR: In this paper, a NbTaTiV refractory HEA with a single body-centered-cubic (BCC) structure using an integrated experimental and theoretical approach was developed.
276 citations
TL;DR: A measurement of electron antineutrino oscillation from the Daya Bay Reactor Neutrinos Experiment with nearly 4 million reactor ν[over ¯]_{e} inverse β decay candidates observed over 1958 days of data collection is reported.
Abstract: We report a measurement of electron antineutrino oscillation from the Daya Bay Reactor Neutrino Experiment with nearly 4 million reactor ν[over ¯]_{e} inverse β decay candidates observed over 1958 days of data collection. The installation of a flash analog-to-digital converter readout system and a special calibration campaign using different source enclosures reduce uncertainties in the absolute energy calibration to less than 0.5% for visible energies larger than 2 MeV. The uncertainty in the cosmogenic ^{9}Li and ^{8}He background is reduced from 45% to 30% in the near detectors. A detailed investigation of the spent nuclear fuel history improves its uncertainty from 100% to 30%. Analysis of the relative ν[over ¯]_{e} rates and energy spectra among detectors yields sin^{2}2θ_{13}=0.0856±0.0029 and Δm_{32}^{2}=(2.471_{-0.070}^{+0.068})×10^{-3} eV^{2} assuming the normal hierarchy, and Δm_{32}^{2}=-(2.575_{-0.070}^{+0.068})×10^{-3} eV^{2} assuming the inverted hierarchy.
239 citations
TL;DR: Light is shed on the biochemical and molecular nature of SRX and the mechanism of action of mavacamten, a cardiac inhibitor in phase 2 clinical trials, is demonstrated, which provides a biochemical and structural link between the genetics and physiology of cardiomyopathy with implications for therapeutic strategies.
Abstract: Mutations in β-cardiac myosin, the predominant motor protein for human heart contraction, can alter power output and cause cardiomyopathy. However, measurements of the intrinsic force, velocity, and ATPase activity of myosin have not provided a consistent mechanism to link mutations to muscle pathology. An alternative model posits that mutations in myosin affect the stability of a sequestered, super relaxed state (SRX) of the protein with very slow ATP hydrolysis and thereby change the number of myosin heads accessible to actin. Here we show that purified human β-cardiac myosin exists partly in an SRX and may in part correspond to a folded-back conformation of myosin heads observed in muscle fibers around the thick filament backbone. Mutations that cause hypertrophic cardiomyopathy destabilize this state, while the small molecule mavacamten promotes it. These findings provide a biochemical and structural link between the genetics and physiology of cardiomyopathy with implications for therapeutic strategies.
222 citations
TL;DR: This paper proposes an end‐to‐end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction, which improves the prediction performance over the previous state‐of‐the‐art methods.
Abstract: Motivation Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. Results We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms. Availability and implementation The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Contact xin.gao@kaust.edu.sa. Supplementary information Supplementary data are available at Bioinformatics online.
202 citations
TL;DR: In this paper, the stochastic optimal operation for the micro integrated electric power, natural gas, and heat delivery system (IPGHS) is investigated for the high-efficient utilization of multitype energy systems.
Abstract: Integrated energy system is important for the high-efficient utilization of multitype energy systems. In this paper, the stochastic optimal operation is investigated for the micro integrated electric power, natural gas, and heat delivery system (IPGHS). First, a low-carbon micro-IPGHS is proposed with the comprehensive consideration of renewable generation, carbon-capture-based power-to-gas technology, and the combined power and heat units. Second, a scenario-based optimal operation model for micro-IPGHS is proposed to handle uncertainties in energy demand and renewable generation. In the proposed model, energy transactions between micro-IPGHS and upstream energy systems as well as constraints for battery storage, natural gas storage, and heat storage systems are considered. Finally, a case study is used for the proposed low-carbon micro-IPGHS to validate the optimal stochastic operation approach. The proposed integrated system can effectively utilize the variable clean energy for optimizing the delivery of the green operation in micro-IPGHS.
201 citations
University of Wisconsin-Madison1, University of Vermont2, Lund University3, University of Cincinnati4, University of Kiel5, North Dakota State University6, Ghent University7, Agricultural Research Service8, Moscow State University9, Prince of Songkla University10, Wayne State University11, University of Valencia12, Baylor College of Medicine13, National Institute of Science Education and Research14, University of Padua15, Polish Academy of Sciences16, Institut national de la recherche agronomique17, Aix-Marseille University18, King Abdulaziz University19, University of Kentucky20, Texas A&M University21, German Cancer Research Center22, Nanjing Agricultural University23, Skolkovo Institute of Science and Technology24, Illinois Institute of Technology25, University of Wisconsin–Oshkosh26, Lawrence Berkeley National Laboratory27, University of Cologne28, University of Warwick29, Max Planck Society30, University of Illinois at Urbana–Champaign31, Michigan State University32, Indiana University33, Cincinnati Children's Hospital Medical Center34, Ohio State University35
TL;DR: Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles, and duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA.
Abstract: The Colorado potato beetle is one of the most challenging agricultural pests to manage. It has shown a spectacular ability to adapt to a variety of solanaceaeous plants and variable climates during its global invasion, and, notably, to rapidly evolve insecticide resistance. To examine evidence of rapid evolutionary change, and to understand the genetic basis of herbivory and insecticide resistance, we tested for structural and functional genomic changes relative to other arthropod species using genome sequencing, transcriptomics, and community annotation. Two factors that might facilitate rapid evolutionary change include transposable elements, which comprise at least 17% of the genome and are rapidly evolving compared to other Coleoptera, and high levels of nucleotide diversity in rapidly growing pest populations. Adaptations to plant feeding are evident in gene expansions and differential expression of digestive enzymes in gut tissues, as well as expansions of gustatory receptors for bitter tasting. Surprisingly, the suite of genes involved in insecticide resistance is similar to other beetles. Finally, duplications in the RNAi pathway might explain why Leptinotarsa decemlineata has high sensitivity to dsRNA. The L. decemlineata genome provides opportunities to investigate a broad range of phenotypes and to develop sustainable methods to control this widely successful pest.
197 citations
TL;DR: Simulations show that the proposed storage model is effective in the robust SCUC solution for IEGS considering possible N − k contingencies with limited natural gas adjustments, and distributed natural gas storage is included to smooth out power system demand curve.
Abstract: A robust security-constrained unit commitment (robust SCUC) model is proposed in this paper to enhance the operational reliability of integrated electricity-natural gas system (IEGS) against possible transmission line outages. In this work, adjustable capability of natural gas contract is considered to avoid dramatic changes in real-time gas demand. Distributed natural gas storage is also included to smooth out power system demand curve and further improve operational efficiency at normal state and during contingencies. Nonlinear and nonconvex natural gas flow functions are relaxed into second-order cone (SOC) constraints, then the SOC-based column and constraint generation method is adopted to solve the proposed two-stage robust convex optimization problem. In this iterative method, the security-check subproblem feeds back worst case constraints to the first-stage master problem, until no violated scenarios can be detected. Simulations tested on 6-bus-6-node and 118-bus-10-node IEGS with natural gas storage show that the proposed storage model is effective in the robust SCUC solution for IEGS considering possible N − k contingencies with limited natural gas adjustments.
179 citations
TL;DR: In this article, a productivity-based, context-dependent mechanism underlying the relationship between corporate social performance and financial performance is uncovered, and the authors argue that key stakeholders' social considerations are more valuable for firms with higher levels of discretionary cash and income stream uncertainty.
Abstract: This study treats firm productivity as an accumulation of productive intangibles and posits that stakeholder engagement associated with better corporate social performance helps develop such intangibles. We hypothesize that because shareholders factor improved productive efficiency into stock price, productivity mediates the relationship between corporate social and financial performance. Furthermore, we argue that key stakeholders’ social considerations are more valuable for firms with higher levels of discretionary cash and income stream uncertainty. Therefore, we hypothesize that those two contingencies moderate the mediated process of corporate social performance with financial performance. Our analysis, based on a comprehensive longitudinal dataset of the U.S. manufacturing firms from 1992 to 2009, lends strong support for these hypotheses. In short, this paper uncovers a productivity-based, context-dependent mechanism underlying the relationship between corporate social performance and financial performance.
178 citations
TL;DR: This paper presents a meta-analyses of the determinants of infectious disease in eight animal models and three of them are confirmed to be connected to EMMARM, a type of “spatially aggregating disease”.
Abstract: For full abstract please refer to Official URL link”, or if there is a document attached which contains the abstract, “For full abstract please refer to attached document
TL;DR: Case studies on both small and large gas-electric integrated systems show that the two ADMM-based distributed approaches have significant efficiencies over the traditional Lagrange relaxation (LR) and augmented LR-based methods.
Abstract: The increasing growth in the installation of natural-gas fired units has raised necessitates on synergy between the electricity and natural gas networks. This paper discusses how synergistic operation of electricity and natural gas networks can be achieved in a distributed fashion using alternating direction method of multipliers (ADMM). A standard ADMM approach and a consensus-based ADMM approach are developed, respectively, to solve the gas-electric integrated optimal power flow problem with and without a coordination operator. In both cases, the optimization formulation for each operator is modeled, and the solution procedure as well as data exchange among multiple decision-makers are explained. Case studies on both small and large gas-electric integrated systems show that the two ADMM-based distributed approaches have significant efficiencies over the traditional Lagrange relaxation (LR) and augmented LR-based methods.
TL;DR: A description of foodborne viruses and their characteristics, their responses to stress and technologies developed for viral detection and control, along with suggestions on how the food industry could implement effective control strategies for viruses in foods are provided.
TL;DR: The history of the field of microbiology of the built environment is outlined and recent insights that have been gained into microbial ecology, adaptation and evolution of this ecosystem are discussed.
Abstract: The built environment comprises all structures built by humans, including our homes, workplaces, schools and vehicles. As in any ecosystem on Earth, microorganisms have been found in every part of the built environment that has been studied. They exist in the air, on surfaces and on building materials, usually dispersed by humans, animals and outdoor sources. Those microbial communities and their metabolites have been implied to cause (or exacerbate) and prevent (or mitigate) human disease. In this Review, we outline the history of the field of microbiology of the built environment and discuss recent insights that have been gained into microbial ecology, adaptation and evolution of this ecosystem. Finally, we consider the implications of this research, specifically, how it is changing the types of materials we use in buildings and how our built environments affect human health.
TL;DR: In this article, the authors investigated the fault characteristics of IIDGs caused by both symmetrical and asymmetrical faults and proposed an algorithm to calculate fault current of droop-controlled IIDG.
Abstract: Diversification of control schemes adopted by inverter-interfaced distributed generators (IIDGs) leads to difficulties in fault current estimation in a microgrid, which might make preexisting protection systems invalid and threaten the safety of power electronic devices. It is therefore important to study fault characteristics of IIDGs. This paper investigates characteristics of fault current of IIDGs caused by both symmetrical and asymmetrical faults. Two kinds of widely used control modes, current control (constant current control and PQ control) and voltage control (V/F control and droop control), are under investigation to provide an intuitive comparison on fault current. In particular, a novel algorithm is proposed to calculate fault current of droop-controlled IIDGs. It is found that different limiters have great impacts on fault response of IIDGs and detailed research works are carried out to identify the effects in this paper. Simulation results based on PSCAD/EMTDC and calculation results based on MATLAB/Simulink verify the correctness of the proposed fault models.
TL;DR: A cell-free synthesis method that enables incorporating non-standard amino acids in the product, and the ability to introduce 40 identical p-acetyl-l-phenylalanine residues site specifically into an elastin-like polypeptide, is developed.
Abstract: Cell-free protein synthesis has emerged as a powerful approach for expanding the range of genetically encoded chemistry into proteins. Unfortunately, efforts to site-specifically incorporate multiple non-canonical amino acids into proteins using crude extract-based cell-free systems have been limited by release factor 1 competition. Here we address this limitation by establishing a bacterial cell-free protein synthesis platform based on genomically recoded Escherichia coli lacking release factor 1. This platform was developed by exploiting multiplex genome engineering to enhance extract performance by functionally inactivating negative effectors. Our most productive cell extracts enabled synthesis of 1,780 ± 30 mg/L superfolder green fluorescent protein. Using an optimized platform, we demonstrated the ability to introduce 40 identical p-acetyl-l-phenylalanine residues site specifically into an elastin-like polypeptide with high accuracy of incorporation ( ≥ 98%) and yield (96 ± 3 mg/L). We expect this cell-free platform to facilitate fundamental understanding and enable manufacturing paradigms for proteins with new and diverse chemistries. Cell-free protein synthesis allows for producing proteins without the need of a host organism, thus sparing the researcher experimental hassle. Here, the authors developed a cell-free synthesis method that enables incorporating non-standard amino acids in the product.
TL;DR: The simulation results show that the proposed DSO framework in a transactive market can efficiently reduce the supply cost of LDA and increase the GenCos’ payoff considering the optimal power exchange with the ISO.
Abstract: Distribution system operator (DSO) has traditionally been responsible for the reliable operation of power distribution systems. The advent of distributed energy resources (DERs) and microgrids in distribution networks has required a new platform for DSOs. The new DSO, which is for the most part an independent agent, is responsible for aggregating a widely dispersed DERs (which include small thermal generation units, energy storage, and demand response) and flexible loads in electricity markets. DSO coordinates and balances the transactive dispatch of supply and demand at the distribution level and links wholesale and retail electricity markets. In this paper, a framework for the day-ahead transactive market is proposed which includes end-to-end power system participants starting from the bulk power ISO and ending at DSO, which includes the management of retail customers with small loads. In this paper, DERs are considered in a local distribution area (LDA). The day-ahead transactive scheduling of LDA is modeled as an MILP and solved using the CPLEX solver. This paper offers numerical results considering a DSO that is responsible for the optimal and secure operation of LDA with microgrids. The simulation results show that the proposed DSO framework in a transactive market can efficiently reduce the supply cost of LDA and increase the GenCos’ payoff considering the optimal power exchange with the ISO.
TL;DR: A day-ahead EV charging scheduling based on an aggregative game model based on the pure strategy Nash equilibrium is proposed and an optimization method is developed to calculate the equilibrium of the game model through quadratic programming.
Abstract: The electric vehicle (EV) market has been growing rapidly around the world. With large scale deployment of EVs in power systems, both the grid and EV owners will benefit if the flexible demand of EV charging is properly managed through the electricity market. When EV charging demand is considerable in a grid, it will impact spot prices in the electricity market and consequently influence the charging scheduling itself. The interaction between the spot prices and the EV demand needs to be considered in the EV charging scheduling, otherwise it will lead to a higher charging cost. A day-ahead EV charging scheduling based on an aggregative game model is proposed in this paper. The impacts of the EV demand on the electricity prices are formulated with the game model in the scheduling considering possible actions of other EVs. The existence and uniqueness of the pure strategy Nash equilibrium are proved for the game. An optimization method is developed to calculate the equilibrium of the game model through quadratic programming. The optimal scheduling of the individual EV controller considering the actions of other EVs in the game is developed with the EV driving pattern distribution. Case studies with the proposed game model were carried out using real world driving data from the Danish National Travel Surveys. The impacts of the EV driving patterns and price forecasts on the EV demand with the proposed game model were also analysed.
TL;DR: The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses.
Abstract: A large-scale integrated energy system can represent several subsystems representing areas that are tied by electricity and natural gas networks. Accordingly, we propose a decentralized optimal energy flow (DOEF) calculation as compared with a centralized solution method. The proposed approach demonstrates the merits of the decentralized operation and control of a multi-area integrated electricity-natural gas system (IEGS), in terms of large-scale modeling requirements, faster computations, and data management for local sensitivity analyses. Using the proposed decentralized structure, the communication burden is relatively light as individual area operators in a multi-area IEGS will make optimal dispatch decisions independently and the corresponding information is shared with adjacent subsystems. The reformulation of the second-order cone (SOC) is proposed using advanced sequential cone programming (SCP) to handle the nonlinear steady-state natural gas flow, which provides a feasible solution with a high degree of computational efficiency. Furthermore, an iterative alternating direction method of multipliers (I-ADMM) is adopted to manage the nonconvexity of integer variables, which guarantees a satisfactory convergence performance. Case studies on three multi-area IEGS validate the effectiveness of the proposed model in a multi-area IEGS.
Wright Laboratory1, University of Wisconsin-Madison2, Oak Ridge National Laboratory3, Le Moyne College4, National Institute of Standards and Technology5, Temple University6, Brookhaven National Laboratory7, Lawrence Livermore National Laboratory8, Drexel University9, Georgia Institute of Technology10, University of Waterloo11, College of William & Mary12, Illinois Institute of Technology13
TL;DR: The first scientific results from the observation of antineutrinos emitted by fission products of U at the High Flux Isotope Reactor were reported in this paper.
Abstract: This Letter reports the first scientific results from the observation of antineutrinos emitted by fission products of ^{235}U at the High Flux Isotope Reactor. PROSPECT, the Precision Reactor Oscillation and Spectrum Experiment, consists of a segmented 4 ton ^{6}Li-doped liquid scintillator detector covering a baseline range of 7-9 m from the reactor and operating under less than 1 m water equivalent overburden. Data collected during 33 live days of reactor operation at a nominal power of 85 MW yield a detection of 25 461±283 (stat) inverse beta decays. Observation of reactor antineutrinos can be achieved in PROSPECT at 5σ statistical significance within 2 h of on-surface reactor-on data taking. A reactor model independent analysis of the inverse beta decay prompt energy spectrum as a function of baseline constrains significant portions of the previously allowed sterile neutrino oscillation parameter space at 95% confidence level and disfavors the best fit of the reactor antineutrino anomaly at 2.2σ confidence level.
TL;DR: The stability region characterized by the system parameters is analytically obtained, which guarantees that the improved synchronverter is always stable and converges to a unique equilibrium as long as the power exchanged at the terminal is kept within this area.
Abstract: Synchronverters are grid-friendly inverters that mimic conventional synchronous generators and play an important role in integrating different types of renewable energy sources, electric vehicles, energy storage systems, etc., to the smart grid. In this paper, an improved synchronverter is proposed to make sure that its frequency and voltage always stay within given ranges, while maintaining the function of the original synchronverter. Furthermore, the stability region characterized by the system parameters is analytically obtained, which guarantees that the improved synchronverter is always stable and converges to a unique equilibrium as long as the power exchanged at the terminal is kept within this area. Extensive OPAL-RT real-time simulation results are presented for the improved and the original self-synchronized synchronverters connected to a stiff grid and for the case when two improved synchronverters are connected to the same bus with one operating as a weak grid, to verify the theoretical development.
TL;DR: The news media are a central source of information about climate change for most people as discussed by the authors, and through frames, media transmit information that shape how people understand climate change as well as the ac...
Abstract: The news media are a central source of information about climate change for most people. Through frames, media transmit information that shape how people understand climate change as well as the ac...
TL;DR: In this article, the authors provide an overview of literature on embodied energy use in buildings in several directions, including key estimation methodologies and tools for embodied energy and presents the embodied energy values for different types of buildings, as studied by the literature.
TL;DR: The interface between the cathode and the solid electrolyte is an important con... as mentioned in this paper, which is the interface between all-solid-state batteries promise significant safety and energy density advantages over liquid-electrolyte batteries.
Abstract: All-solid-state batteries promise significant safety and energy density advantages over liquid-electrolyte batteries. The interface between the cathode and the solid electrolyte is an important con...
TL;DR: In this article, an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage-source-converter-based high-voltage direct-current (VSC-HVDC)-connected offshore wind farms (OWFs) was proposed.
Abstract: This paper proposes an enhanced voltage control strategy (EVCS) based on model predictive control (MPC) for voltage-source-converter-based high-voltage direct-current (VSC-HVDC)-connected offshore wind farms (OWFs). In the proposed MPC-based EVCS, all wind turbine generators (WTGs) and the wind-farm-side VSCs are optimally coordinated to keep voltages within the feasible range and reduce system power losses. Considering the high $R{\rm{/}}X$ ratio of the OWF collector system, the effects of active power outputs of WTGs on voltage control are also taken into consideration. The predictive model of the VSC with a typical cascaded control structure is derived in detail. The sensitivity coefficients are calculated by an analytical method to improve the computational efficiency. A VSC-HVDC-connected OWF with 64 WTGs was used to validate the proposed voltage control strategy.
TL;DR: This paper addresses the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks, and investigates the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally.
Abstract: The explosive growth of content requests from mobile users is stretching the capability of current mobile networking technologies to satisfy users’ demands with acceptable quality of service. An effective approach to address this challenge, which has not yet been thoroughly studied, is to offload network traffic by caching popular content at the edges (e.g., mobile devices and base stations) of mobile networks, thus reducing the massive duplication of content downloads. In this paper, we address the system modeling, large-scale optimization, and framework design of hierarchical edge caching in device-to-device aided mobile networks. In particular, taking into account the analysis of social behavior and preference of mobile users, heterogeneous cache sizes, and the derived system topology, we investigate the maximum capacity of the network infrastructure in terms of offloading network traffic, reducing system costs, and supporting content requests from mobile users locally. Our proposed framework has a low complexity and can be applied in practical engineering implementation. Trace-based simulation results demonstrate the effectiveness of the proposed framework.
TL;DR: In this article, the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC), is described.
Abstract: We describe the concept and procedure of drifted-charge extraction developed in the MicroBooNE experiment, a single-phase liquid argon time projection chamber (LArTPC). This technique converts the raw digitized TPC waveform to the number of ionization electrons passing through a wire plane at a given time. A robust recovery of the number of ionization electrons from both induction and collection anode wire planes will augment the 3D reconstruction, and is particularly important for tomographic reconstruction algorithms. A number of building blocks of the overall procedure are described. The performance of the signal processing is quantitatively evaluated by comparing extracted charge with the true charge through a detailed TPC detector simulation taking into account position-dependent induced current inside a single wire region and across multiple wires. Some areas for further improvement of the performance of the charge extraction procedure are also discussed.
TL;DR: A scenario-based stochastic decision-making model is proposed to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets.
Abstract: A scenario-based stochastic decision-making model is proposed in this paper to determine the optimal strategy for the operation of integrated natural gas generating unit (NGG) and power-to-gas conversion (P2G) facilities in energy and regulation markets. Using the proposed strategy, the coordination of NGG and P2G facilities will provide a higher market payoff than that of independent NGG and P2G participation. The market price uncertainty is simulated in multiple scenarios using the Latin hypercube sampling method and the conditional value-at-risk strategy is adopted for evaluating the financial risks introduced by price uncertainties. The optimal bidding strategy is developed for both P2G and NGG operations and the Shapley-value method is employed to allocate the market payoff among NGG and P2G facilities. A case study which is based on the Pennsylvania, New Jersey, and Maryland market data is employed to verify the effectiveness of the proposed model and examine the characteristics of the proposed bidding strategy for the optimal operation of integrated NGG and P2G facilities.
TL;DR: Experimental results show that the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines and the improvement in accuracy is 12% on average under different time steps.
Abstract: Recently, many countries have spent great efforts on wind power generation. Although there have been many methods in the field of wind power forecasting, the persistence statistics model based on historical data is still being challenged due to the randomness and uncontrollability in wind power. Hence, a more accurate and effective wind power forecasting method is still required. In this paper, a new forecasting method is proposed by combining stacked auto-encoders (SAE) and the back propagation (BP) algorithm. First, an SAE with three hidden layers is designed to extract the characteristics from the reference data sequence, and the subsequent loss function is used in the pre-training process to obtain the optimal initial connection weights of the deep network. Second, after adding one output layer to the stacked auto encoders, the BP algorithm is used to fine tune the weights of the whole network. To achieve the best network architecture, the particle swarm optimization is adopted to decide the number of neurons of the hidden layer and the learning rate of each auto encoder. Experimental results show that, for short-term wind power forecasting, the proposed method achieves more stable and effective performance than the existing BP neural network and support vector machines. The improvement in accuracy is 12% on average under different time steps.
TL;DR: The proposed algorithm is applied to parameter estimation for a PMSM drive system and results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.
Abstract: A global parameter estimation method for a permanent magnet synchronous machines (PMSM) drive system is proposed, where the electrical parameters, mechanical parameters, and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters, and VSI of PMSM drive by using dynamic self-learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multiscale based interactive learning operator is introduced for accelerating the convergence speed of the $Pbest$ particles; meanwhile a dynamic opposition-based learning strategy is designed to facilitate the $gBest$ particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously.