The thickness of the transport layer in stratified geomorphic flows

River Flow 2010

Magma flow pattern in dykes of the Azores revealed by ansiotropy of magnetic susceptibility

The localization of magma melting areas at the lithosphere bottom in extensional volcanic domains is poorly understood. Large polygenetic volcanoes of long duration and their associated magma chambers suggest that melting at depth may be focused at specific points within the mantle. To validate the hypothesis that the magma feeding a mafic crust, comes from permanent localized crustal reservoirs, it is necessary to map the fossilized magma flow within the crustal planar intrusions. Using the AMS, we obtain magmatic flow vectors from 34 alkaline basaltic dykes from São Jorge, São Miguel and Santa Maria islands in the Azores Archipelago, a hot-spot related triple junction. The dykes contain titanomagnetite showing a wide spectrum of solid solution ranging from Ti-rich to Ti-poor compositions with vestiges of maghemitization. Most of the dykes exhibit a normal magnetic fabric. The orientation of the magnetic lineation k1 axis is more variable than that of the k3 axis, which is generally well grouped. The dykes of São Jorge and São Miguel show a predominance of subhorizontal magmatic flows. In Santa Maria the deduced flow pattern is less systematic changing from subhorizontal in the southern part of the island to oblique in north. These results suggest that the ascent of magma beneath the islands of Azores is predominantly over localized melting sources and then collected within shallow magma chambers. According to this concept, dykes in the upper levels of the crust propagate laterally away from these magma chambers thus feeding the lava flows observed at the surface.

O papel dos sistemas de Non-Intrusive Load Monitoring para a eficiência energética do setor residencial

A sustentabilidade energética do planeta é uma preocupação corrente e, neste sentido, a eficiência energética afigura-se como sendo essencial para a redução do consumo em todos os setores de atividade. No que diz respeito ao setor residencial, o indevido comportamento dos utilizadores aliado ao desconhecimento do consumo dos diversos aparelhos, são factores impeditivos para a redução do consumo energético. Uma ferramenta importante, neste sentido, é a monitorização de consumos nomeadamente a monitorização não intrusiva, que apresenta vantagens económicas relativamente à monitorização intrusiva, embora levante alguns desafios na desagregação de cargas. Abordou-se então, neste documento, a temática da monitorização não intrusiva onde se desenvolveu uma ferramenta de desagregação de cargas residenciais, sobretudo de aparelhos que apresentavam elevados consumos. Para isso, monitorizaram-se os consumos agregados de energia elétrica, água e gás de seis habitações do município de Vila Nova de Gaia. Através da incorporação dos vetores de água e gás, a acrescentar ao da energia elétrica, provou-se que a performance do algoritmo de desagregação de aparelhos poderá aumentar, no caso de aparelhos que utilizem simultaneamente energia elétrica e água ou energia elétrica e gás. A eficiência energética é também parte constituinte deste trabalho e, para tal, implementaram-se medidas de eficiência energética para uma das habitações em estudo, de forma a concluir as que exibiam maior potencial de poupança, assim como rápidos períodos de retorno de investimento. De um modo geral, os objetivos propostos foram alcançados e espera-se que num futuro próximo, a monitorização de consumos não intrusiva se apresente como uma solução de referência no que respeita à sustentabilidade energética do setor residencial.

Otimização Energética do Sistema de Refrigeração da máquina de impregnar ZELL

Esta dissertação teve como objetivo fundamental a otimização energética do sistema de refrigeração da máquina de impregnar tela ZELL e, como objetivo adicional, a avaliação da qualidade da água do circuito, justificada pela acentuada degradação dos rolos devido à corrosão provocada pela recirculação da água de arrefecimento. Inicialmente fez-se o levantamento de informações do processo produtivo para caracterizar o funcionamento do sistema de refrigeração, tendo-se selecionado duas telas de poliéster designadas neste estudo por P1 e P2 e, também, uma tela de nylon designada por N. Foram efetuados ensaios, um para cada tela, para a atual temperatura de setpoint da água à saída da torre de arrefecimento (30ºC). Realizou-se outro ensaio para a tela N mas com uma temperatura de setpoint de 37ºC, ao qual se chamou N37. Deste modo, determinou-se as potências térmicas removidas pela água de refrigeração e as potências térmicas perdidas por radiação e por convecção, tendo-se verificado que na generalidade dos rolos as referências P1 e P2 apresentam valores mais elevados. Em termos percentuais, a potência térmica removida pela água de refrigeração nos grupos tratores 1 e 3 e no conjunto de rolos de R1 a R29 corresponde a 48%, 10% e 70%, respetivamente. Com a avaliação às necessidades de arrefecimento da máquina ZELL, confirmou-se que os caudais atuais de refrigeração dos rolos garantem condições, mais que suficientes, de funcionamento dos rolamentos. Assim sendo, fez-se uma análise no sentido da diminuição do caudal total que passou de 10,25 L/s para 7,65 L/s. Considerando esta redução, determinou-se o caudal de ar húmido a ser introduzido na torre de arrefecimento. O valor determinado foi de 4,6 m3ar húmido/s, o que corresponde a uma redução de cerca de 32% em relação ao caudal atual que é de 6,8 m3ar húmido/s. Com os resultados das análises efetuadas à água do circuito de refrigeração, concluiu-se que a água de reposição e a água de recirculação possuem má qualidade para uso na generalidade dos sistemas de refrigeração, principalmente devido aos elevados valores de concentração de ferro e condutividade elétrica, responsáveis pela intensificação da corrosão no interior dos rolos.

Immunological status of ENL (Erythema Nodosum Leprosum) patients: its relationship to bacterial load and levels of circulanting IL-2R

Recent data suggest that the clinical course of reactional states in leprosy is closely related to the cytokine profile released locally or systemically by the patients. In the present study, patients with erythema nodosum leprosum (ENL) were grouped according to the intensity of their clinical symptoms. Clinical and immunological aspects of ENL and the impact of these parameters on bacterial load were assessed in conjunction with patients' in vitro immune response to mycobacterial antigens. In 10 out of the 17 patients tested, BI (bacterial index) was reduced by at least 1 log from leprosy diagnosis to the onset of their first reactional episode (ENL), as compared to an expected 0.3 log reduction in the unreactional group for the same MDT (multidrug therapy) period. However, no difference in the rate of BI reduction was noted at the end of MDT among ENL and unreactional lepromatous patients. Accordingly, although TNF-alpha (tumor necrosis factor) levels were enhanced in the sera of 70.6% of the ENL patients tested, no relationship was noted between circulating TNF-alpha levels and the decrease in BI detected at the onset of the reactional episode. Evaluation of bacterial viability of M. leprae isolated from the reactional lesions showed no growth in the mouse footpads. Only 20% of the patients demonstrated specific immune response to M. leprae during ENL. Moreover, high levels of soluble IL-2R (interleukin-2 receptor) were present in 78% of the patients. Circulating anti-neural (anti-ceramide and anti-galactocerebroside antibodies) and anti-mycobacterial antibodies were detected in ENL patients' sera as well, which were not related to the clinical course of disease. Our data suggest that bacterial killing is enhanced during reactions. Emergence of specific immune response to M. leprae and the effective role of TNF-alpha in mediating fragmentation of bacteria still need to be clarified.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Contextual Intelligent Load Management Considering Real-Time Pricing in a Smart Grid Environment

The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

Determining the Adjustment Baseline Parameters to Define an Accurate Customer Baseline Load

Demand response is an energy resource that has gained increasing importance in the context of competitive electricity markets and of smart grids. New business models and methods designed to integrate demand response in electricity markets and of smart grids have been published, reporting the need of additional work in this field. In order to adequately remunerate the participation of the consumers in demand response programs, improved consumers’ performance evaluation methods are needed. The methodology proposed in the present paper determines the characterization of the baseline approach that better fits the consumer historic consumption, in order to determine the expected consumption in absent of participation in a demand response event and then determine the actual consumption reduction. The defined baseline can then be used to better determine the remuneration of the consumer. The paper includes a case study with real data to illustrate the application of the proposed methodology.

Cost Allocation Model for Distribution Networks Considering High Penetration of Distributed Energy Resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitiveenvironment of electricity markets impose the use of new approaches in several domains. The networkcost allocation, traditionally used in transmission networks, should be adapted and used in the distribu-tion networks considering the specifications of the connected resources. The main goal is to develop afairer methodology trying to distribute the distribution network use costs to all players which are usingthe network in each period. In this paper, a model considering different type of costs (fixed, losses, andcongestion costs) is proposed comprising the use of a large set of DER, namely distributed generation(DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehi-cles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). Theproposed model includes three distinct phases of operation. The first phase of the model consists in aneconomic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen’s andBialek’s tracing algorithms are used and compared to evaluate the impact of each resource in the net-work. Finally, the MW-mile method is used in the third phase of the proposed model. A distributionnetwork of 33 buses with large penetration of DER is used to illustrate the application of the proposedmodel.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.