Study of distribution network demand response events in the Portuguese system

In competitive electricity markets with deep concerns for the efficiency level, demand response programs gain considerable significance. As demand response levels have decreased after the introduction of competition in the power industry, new approaches are required to take full advantage of demand response opportunities. Grid operators and utilities are taking new initiatives, recognizing the value of demand response for grid reliability and for the enhancement of organized spot markets’ efficiency. This paper proposes a methodology for the selection of the consumers that participate in an event, which is the responsibility of the Portuguese transmission network operator. The proposed method is intended to be applied in the interruptibility service implemented in Portugal, in convergence with Spain, in the context of the Iberian electricity market. This method is based on the calculation of locational marginal prices (LMP) which are used to support the decision concerning the consumers to be schedule for participation. The proposed method has been computationally implemented and its application is illustrated in this paper using a 937 bus distribution network with more than 20,000 consumers.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

Cellular distribution of Hsp70 expression in rat skeletal muscles. Effects of moderate exercise training and chronic hypoxia.

Rat hindlimb muscles constitutively express the inducible heat shock protein 72 (Hsp70), apparently in proportion to the slow myosin content. Since it remains controversial whether chronic Hsp70 expression reflects the overimposed stress, we investigated Hsp70 cellular distribution in fast muscles of the posterior rat hindlimb after (1) mild exercise training (up to 30 m/min treadmill run for 1 h/day), which induces a remodeling in fast fiber composition, or (2) prolonged exposure to normobaric hypoxia (10%O(2)), which does not affect fiber-type composition. Both conditions increased significantly protein Hsp70 levels in the skeletal muscle. Immunohistochemistry showed the labeling for Hsp70 in subsets of both slow/type 1 and fast/type 2A myofibers of control, sedentary, and normoxic rats. Endurance training increased about threefold the percentage of Hsp70-positive myofibers (P < 0.001), and changed the distribution of Hsp70 immunoreactivity, which involved a larger subset of both type 2A and intermediate type 2A/2X myofibers (P < 0.001) and vascular smooth muscle cells. Hypoxia induced Hsp70 immunoreactivity in smooth muscle cells of veins and did not increase the percentage of Hsp70-positive myofibers; however, sustained exposure to hypoxia affected the distribution of Hsp70 immunoreactivity, which appeared detectable in a very small subset of type 2A fibers, whereas it concentrated in type 1 myofibers (P < 0.05) together with the labeling for heme-oxygenase isoform 1, a marker of oxidative stress. Therefore, the chronic induction of Hsp70 expression in rat skeletal muscles is not obligatory related to the slow fiber phenotype but reveals the occurrence of a stress response.

The use of quantile trees in the prediction of the diameter distribution of a stand

Abstract

Bayesian estimation of diameter distribution during harvesting

Abstract

Distribution of immune response cells in the pelvic urethra and the prepuce of rams

The pathogens of the reproductive system in the male can penetrate and establish by ascending route, from to the prepuce to the urethra, accessory glands, epididymis and testicles. The aim of this paper is determine the distribution and number of cells involved in the immune response in prepuce and pelvic urethra of rams, without apparent clinical alterations in testicle, epididymis and prepuce. The distribution of some of the cells involved in the immune response at the level of the prepuce and the pelvic urethra was quantified in four one-year-old rams seronegative for B. ovis and A. seminis and without apparent lesions in the testicles, the epididymis, and the prepuce. At the moment of slaughter, samples were taken from the preputial fornix and the pelvic urethra and placed in 10% formalin and under freezing conditions. CD4, CD8, WC1, CD45RO, CD14 and CD1b cells were demonstrated by immunohistochemistry, and immunoglobulin-containing cells (ICC) of the IgA, IgG and IgM classes were demonstrated by immunofluorescence. The labeled cells present in the mucosa of both organs were counted with an image analyzer. The total number of cells was compared between both tissues and differentially between the epithelium and the connective tissue of the mucosa. Significant differences were found in the total number of CD4, CD45RO, and WC1 lymphocytes, in CD14 macrophages, and CD1b dendritic cells, with mean values being greater in the fornix than in the urethra (p<0.05) in all cases. Only dendritic cells were found in the prepuce. No differences were found in the number of CD8 lymphocytes between both organs. The ratio between each cell type in the connective and the intraepithelial tissues and between organs was 10/1 for CD4 in the fornix (p<0.05), against 7/1 in the urethra (p<0.05), while CD8 had a 1/1 distribution in both mucosae. The WC1 ratio was 5/1 in both mucosae (p<0.05). CD45RO labeling was 19/1 in the prepuce (p<0.05) and 1/1 in the urethra. IgA-containing cells did not show differences in the total number of cells in both tissues. In the urethra, no IgG-containing cells were observed and IgM-containing cells were scarce; in contrast, both cell types were present in the prepuce, in amounts greater than in the urethra (p<0.05). IgA-, IgG-, and IgM-containing cells were located in both organs in the mucosal connective tissue. The presence of antigen-presenting cells, macrophages, and dendritic cells, as well as of lymphocytes CD4, CD8 TCR γδ (WC1), IgA-, IgG and IgM positive cells, and CD45RO cells suggests that both mucosae may behave as inductive and effector sites for the mucosal immune response.

Topographic distribution of the tibial somatosensory evoked potential using coherence

The objective of the present study was to determine the adequate cortical regions based on the signal-to-noise ratio (SNR) for somatosensory evoked potential (SEP) recording. This investigation was carried out using magnitude-squared coherence (MSC), a frequency domain objective response detection technique. Electroencephalographic signals were collected (International 10-20 System) from 38 volunteers, without history of neurological pathology, during somatosensory stimulation. Stimuli were applied to the right posterior tibial nerve at the rate of 5 Hz and intensity slightly above the motor threshold. Response detection was based on rejecting the null hypothesis of response absence (significance level α= 0.05 and M = 500 epochs). The best detection rates (maximum percentage of volunteers for whom the response was detected for the frequencies between 4.8 and 72 Hz) were obtained for the parietal and central leads mid-sagittal and ipsilateral to the stimulated leg: C4 (87%), P4 (82%), Cz (89%), and Pz (89%). The P37-N45 time-components of the SEP can also be observed in these leads. The other leads, including the central and parietal contralateral and the frontal and fronto-polar leads, presented low detection capacity. If only contralateral leads were considered, the centro-parietal region (C3 and P3) was among the best regions for response detection, presenting a correspondent well-defined N37; however, this was not observed in some volunteers. The results of the present study showed that the central and parietal regions, especially sagittal and ipsilateral to the stimuli, presented the best SNR in the gamma range. Furthermore, these findings suggest that the MSC can be a useful tool for monitoring purposes.

Distribution, extracellular virulence factors and antibiogram of motile aeromonads in fresh water ornamental fishes and immune response of Cyprinus carpio against Aeromonas hydrophila infection

The thesis deals with the prevalence and distribution of motile aeromonads in selected ornamental fishes. The presence of motile aeromonads in ornamental fishes and associated carriage water is well documented. Though aeromonads are a part of autochthonous flora of natural waters, disease outbreak occurs as a result of environmental stress on the cultured species and virulence of the pathogens. While ornamental aquaculture in many parts of the world is highly organized and practiced scientifically, it is highly unorganized in India. The culture ponds/tanks are often maintained in very poor manner and the fishes are subjected to high degree of stress during transportation from the production facility to retail vendors. The situation is no better at retail outlets, where fishes are maintained in crowded condition without proper aeration or food. All these could result in high prevalence of diseases caused by motile aeromonads. No systematic study has been carried out to understand the prevalence of motile aeromonads in ornamental fishes and carriage water . It also gives an account of the production of extracellular virulence factors and the antibiogram of the different species of motile aeromonads isolated. The growth characteristics and virulence potential of a representative strain of Aeromonas hydrophila is also studied. The nucleotide sequencing of the strain was carried out and sequences deposited in Genbank. Survival and immune response of Cyprinus carpio under different stress conditions and on probiotic treatment with Bacillus NL110, when challenged with A. hydrophila is also dealt within this thesis.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

Non-uniform data distribution for communication-efficient parallel clustering

Global communicationrequirements andloadimbalanceof someparalleldataminingalgorithms arethe major obstacles to exploitthe computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication costin parallel data mining algorithms and, in particular, in the k-means algorithm for cluster analysis. In the straightforward parallel formulation of the k-means algorithm, data and computation loads are uniformly distributed over the processing nodes. This approach has excellent load balancing characteristics that may suggest it could scale up to large and extreme-scale parallel computing systems. However, at each iteration step the algorithm requires a global reduction operationwhichhinders thescalabilityoftheapproach.Thisworkstudiesadifferentparallelformulation of the algorithm where the requirement of global communication is removed, while maintaining the same deterministic nature ofthe centralised algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real-world distributed applications or can be induced by means ofmulti-dimensional binary searchtrees. The approachcanalso be extended to accommodate an approximation error which allows a further reduction ofthe communication costs. The effectiveness of the exact and approximate methods has been tested in a parallel computing system with 64 processors and in simulations with 1024 processing element

Intercomparison of simulated global vegetation distribution in response to 6 kyr BP orbital forcing

The response of ten atmospheric general circulation models to orbital forcing at 6 kyr BP has been investigated using the BIOME model, which predicts equilibrium vegetation distribution, as a diagnostic. Several common features emerge: (a) reduced tropical rain forest as a consequence of increased aridity in the equatorial zone, (b) expansion of moisture-demanding vegetation in the Old World subtropics as a consequence of the expansion of the Afro–Asian monsoon, (c) an increase in warm grass/shrub in the Northern Hemisphere continental interiors in response to warming and enhanced aridity, and (d) a northward shift in the tundra–forest boundary in response to a warmer growing season at high northern latitudes. These broadscale features are consistent from model to model, but there are differences in their expression at a regional scale. Vegetation changes associated with monsoon enhancement and high-latitude summer warming are consistent with palaeoenvironmental observations, but the simulated shifts in vegetation belts are too small in both cases. Vegetation changes due to warmer and more arid conditions in the midcontinents of the Northern Hemisphere are consistent with palaeoenvironmental data from North America, but data from Eurasia suggests conditions were wetter at 6 kyr BP than today. The models show quantitatively similar vegetation changes in the intertropical zone, and in the northern and southern extratropics. The small differences among models in the magnitude of the global vegetation response are not related to differences in global or zonal climate averages, but reflect differences in simulated regional features. Regional-scale analyses will therefore be necessary to identify the underlying causes of such differences among models.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

The Poisson-exponential distribution: a Bayesian approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

The McDonald extended distribution: properties and applications

We study a five-parameter lifetime distribution called the McDonald extended exponential model to generalize the exponential, generalized exponential, Kumaraswamy exponential and beta exponential distributions, among others. We obtain explicit expressions for the moments and incomplete moments, quantile and generating functions, mean deviations, Bonferroni and Lorenz curves and Gini concentration index. The method of maximum likelihood and a Bayesian procedure are adopted for estimating the model parameters. The applicability of the new model is illustrated by means of a real data set.