Design of a sea-level tsunami detection network for the Gulf of Cadiz

The devastating impact of the Sumatra tsunami of 26 December 2004, raised the question for scientists of how to forecast a tsunami threat. In 2005, the IOC-UNESCO XXIII assembly decided to implement a global tsunami warning system to cover the regions that were not yet protected, namely the Indian Ocean, the Caribbean and the North East Atlantic, the Mediterranean and connected seas (the NEAM region). Within NEAM, the Gulf of Cadiz is the more sensitive area, with an important record of devastating historical events. The objective of this paper is to present a preliminary design for a reliable tsunami detection network for the Gulf of Cadiz, based on a network of sea-level observatories. The tsunamigenic potential of this region has been revised in order to define the active tectonic structures. Tsunami hydrodynamic modeling and GIS technology have been used to identify the appropriate locations for the minimum number of sea-level stations. Results show that 3 tsunameters are required as the minimum number of stations necessary to assure an acceptable protection to the large coastal population in the Gulf of Cadiz. In addition, 29 tide gauge stations could be necessary to fully assess the effects of a tsunami along the affected coasts of Portugal, Spain and Morocco.

The Bambuí health and ageing study (BHAS): methodological approach and preliminary results of a population-based cohort study of the elderly in Brazil

OBJECTIVE: A cohort study has been designed to identify predictors of adverse health events in the elderly. The methodology of the study and preliminary descriptive results are presented. METHODS: The study population comprises all residents of Bambuí (Minas Gerais, Brazil), aged 60 or more years (n=1.742). From these, 92.2% were interviewed and 85.9% underwent clinical examination, consisting of haematological and biochemical tests, serology for Trypanosoma cruzi, anthropometric and blood pressure measures and electrocardiogram. Aliquots of serum, plasma and DNA were stored for future investigations. The baseline interview included sociodemographic characteristics, self-referred health condition and history of selected diseases, medication use, health service use, source of medical care, physical activities, smoking, drinking and eating habits, reproductive history, physical functioning, life events, social support and mental health. Individuals are being followed up annually. RESULTS: The following characteristics predominated among participants: women (60,0%), married (48.9%) or widowed (35.4%), people living in households with up to 2 residents (73.8%), heads of family (76.7%), people with monthly income between 1.00 and 2.99 Brazilian minimum wages (62.0%) and people with up to 4 years of schooling (89.1%). The median age was 68 years. Among the cohort members, only 1.7% were lost in the first follow-up. CONCLUSIONS: In general, the characteristics of the study population were very similar to those from other epidemiological studies of the elderly based on large Brazilian cities. The small number of losses to follow-up indicates that the choice of Bambuí was adequate, assuring the feasibility of a long term cohort study.

Epidemiology of bacterial meningitis among children in Brazil, 1997-1998

OBJECTIVE: To document the incidence and the descriptive epidemiology of bacterial meningitis among individuals under age 20 in a geographically defined region in Brazil during the two-year period immediately preceding the introduction of Haemophilus influenzae type b (Hib) vaccines into the national immunization program of Brazil. METHODS: Population-based epidemiological study of all cases of bacterial meningitis reported among residents of Campinas, Brazil, under age 20 (n=316,570) during the period of 1997-98, using comprehensive surveillance records compiled by the Campinas Health Department from cases reported among hospital inpatients, outpatients, emergency room visits, death certificates, and autopsy reports. RESULTS: The incidence of bacterial meningitis (n=274) was 334.9, 115 and 43.5 cases/10(5) person-years (pys) for residents of Campinas under age 1, 5 and 20, respectively. All cases were hospitalized, with an average length of stay of 12 days. Documented prior antibiotic use was 4.0%. The case-fatality rate of bacterial meningitis in individuals under age 20 was 9% (24/274) with 75% of deaths occurring in children under the age of five. The incidence of Hib meningitis (n=26) was 62.8 and 17 cases/10(5) pys in children age <1 and <5, respectively. CONCLUSIONS: The incidence of Hib meningitis in children under the age of 5 in Campinas during 1997-98 was similar to that reported in the US, Western Europe, and Israel prior to widespread Hib vaccine use in those regions. This study provides a baseline for later studies to evaluate changes in the etiology and incidence of bacterial meningitis in children after introduction of routine Hib vaccination in Brazil.

Sistema de neurónios espelho no síndrome de down: estudo das variações dos ritmos Mu No Eeg

A capacidade de compreensão das acções dos outros e de imitação tem sido descrita como fundamental para a cognição social do ser humano. Recentemente tem sido atribuída a responsabilidade desta capacidade a um sistema neuronal denominado de Sistema de Neurónios Espelho, que se tem demonstrado estar afectado em perturbações mentais que se caracterizam por alterações severas da teoria da mente e da empatia, como é o caso do autismo. No caso do Síndrome de Down, verifica-se a coexistência de boas competências sociais e de capacidades práxicas e de imitação intactas, com dificuldades de interpretação de situações sociais e de reconhecimento de emoções, que nos levam a questionar acerca da actividade do seu Sistema de Neurónios Espelho. As oscilações do ritmo de frequências um (8-13 Hz) no córtex sensório-motor perante a observação de acções são consideradas um reflexo da actividade dos neurónios espelho, estando estabelecido que em pessoas saudáveis ocorre uma supressão mu na realização de movimentos com o membro superior e na sua observação quando realizados por outras pessoas. Neste estudo registou-se electroencefalograficamente a supressão dos ritmos mu em 11 pessoas com SD e em 20 pessoas sem SD nas seguintes condições: observação de um vídeo com duas bolas em movimento, observação de um vídeo com um movimento repetido de uma mão e realização movimentos com a mão. A baseline foi registada através da observação de um ponto estático. Constatamos que existe supressão dos ritmos mu na observação das acções dos outros em pessoas com Síndrome Down da mesma forma que ocorre na realização do próprio movimento, sugerindo uma relativa preservação do funcionamento dos neurónios espelho e dos mecanismos básicos de cognição social. Estes resultados vão de encontro aos estudos que apontam para a integridade das capacidades de imitação no Síndrome Down. Verificamos também que não se encontram diferenças significativas na supressão dos ritmos mu entre os grupos de pessoas com Síndrome Down e de Controlo em relação às condições usadas na investigação.

Exposure to airborne ultrafine particles from cooking in Portuguese homes

Cooking was found to be a main source of submicrometer and ultrafine aerosols from gas combustion in stoves. Therefore, this study consisted of the determination of the alveolar deposited surface area due to aerosols resulting from common domestic cooking activities (boiling fish, vegetables, or pasta, and frying hamburgers and eggs). The concentration of ultrafine particles during the cooking events significantly increased from a baseline of 42.7 μm2/cm3 (increased to 72.9 μm2/cm3 due to gas burning) to a maximum of 890.3 μm2/cm3 measured during fish boiling in water, and a maximum of 4500 μm2/cm3 during meat frying. This clearly shows that a domestic activity such as cooking can lead to exposures as high as those of occupational exposure activities.

Análise do investimento direto estrangeiro em Portugal utilizando a metodologia de box-jenkins

Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientadora: Professora Doutora Patrícia Ramos

Natural Gas location design and operation

A major determinant of the level of effective natural gas supply is the ease to feed customers, minimizing system total costs. The aim of this work is the study of the right number of Gas Supply Units – GSUs - and their optimal location in a gas network. This paper suggests a GSU location heuristic, based on Lagrangean relaxation techniques. The heuristic is tested on the Iberian natural gas network, a system modelized with 65 demand nodes, linked by physical and virtual pipelines. Lagrangean heuristic results along with the allocation of loads to gas sources are presented, using a 2015 forecast gas demand scenario.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Using demand response to deal with unexpected low wind power generation in the context of smart grid

Demand response is assumed an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed aims the minimization of the operation costs in a smart grid operated by a virtual power player. It is especially useful when actual and day ahead wind forecast differ significantly. When facing lower wind power generation than expected, RTP is used in order to minimize the impacts of such wind availability change. The proposed model application is here illustrated using the scenario of a special wind availability reduction day in the Portuguese power system (8th February 2012).

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.