Abordagem ao risco de crédito no âmbito do acordo de Basileia III em Portugal

Dissertação para a obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Mestre Adalmiro Álvaro Malheiro de Castro Andrade Pereira

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

Influence of an unstable shoe on compensatory postural adjustments: an experimental evaluation

This study attempted to evaluate the influence of using an unstable shoe in muscle re-cruitment strategies and center of pressure (CoP) displacement after the application of an external perturba-tion. Fourteen healthy female subjects participated in this study. The electromyographic activity of medial ga-strocnemius, tibialis anterior, rectus femoris, biceps femoris, rectus abdominis and erector spinae muscles and the kinetic values to calculate the CoP were collected and analyzed after the application of an external pertur-bation with the subject in standing position, with no shoes and using unstable footwear. The results showed increased in medial gastrocnemius activity during the first compensatory postural adjustments and late com-pensatory postural adjustments when using an unstable shoe. There were no differences in standard deviation and maximum peak of anteroposterior displacement of CoP between measurements. From the experimental findings, one can conclude that the use of an unstable shoe leads to an increase in gastrocnemius activity with no increase in CoP displacement following an unexpected external perturbation.

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.

Imunorreatividade em material histológico arquivado durante um, quatro e sete anos

Quando aplicada no âmbito da Anatomia Patológica, a imuno-histoquímica tem constituído um poderoso meio de identificação/caracterização de várias estruturas histológicas, permitindo delinear prognóstico e terapêutica para várias patologias. Tendo em conta que as amostras histológicas analisadas podem ser conservadas ao longo de vários anos, interessa avaliar a manutenção da antigenicidade ao longo do tempo, de forma a garantir a qualidade final da técnica quando aplicada em material de arquivo. Assim, o principal objetivo deste trabalho foi comparar a imunorreatividade do material histológico arquivado durante um, quatro e sete anos. Foi utilizado material histológico de próstata, pulmão e mama, no qual se procedeu à imunomarcação de citoqueratinas (Clones AE1/AE3), CD34 e proteína p63, por método de multímero/HRP no sistema Ventana BenchMark Ultra®. Foi realizado um ensaio com recuperação antigênica por alta temperatura (RAAT) e outro sem esta etapa. As imunomarcações (n=162) foram classificadas por três avaliadores independentes num escore quantitativo final (escala 0-100). O par média/desvio-padrão do escore final para os casos com sete anos foi de 69,06/19,05, para os casos com quatro anos foi de 66,47/20,73 e para os casos com um ano foi de 69,08/19,35, não se tendo encontrado diferenças estatisticamente significativas. Os casos sem RAAT obtiveram um par média/desvio-padrão de 54,90/17,00, enquanto os casos com RAAT obtiveram 81,50/11,60, o que revelou diferenças estatisticamente significativas (p=0,000). Para os casos em estudo conclui-se que o fator “tempo de arquivo” não está associado a alterações da imunorreatividade. A importância da RAAT na obtenção de imunomarcação de qualidade sai fortemente realçada. ABSTRACT - When applied within the framework of Pathology, immunohistochemistry has been a powerful means of identification/characterization of various histological structures, allowing to outline prognosis and therapy for various diseases. Given that the analyzed histological samples can be preserved for several years, it is interesting to assess the retention of antigenicity over time in order to ensure the quality of the final technique, when applied to stored material. Thus, the main objective of this study was to compare the immunoreactivity of the histological material archived for one, four and seven years. It was used histological material from prostate, lung and breast, in which it was performed the immunostaining of cytokeratins (clones AE1/AE3), CD34 and p63 protein by the method of multimer/HRP system on a Ventana BenchMark Ultra®. It was conducted a test with heat induced epitope retrieval (HIER) and another one without this step. The stained slides (n=162) were classified by three independent assessors using a quantitative score (scale 0-100). The pair mean/standard deviation of the score for cases with seven years was 69,06/19,05, for cases with four years was 66,47/20,73 and for cases with one year was 69,08/19,35, which did not revealed any statistically significant differences. The cases without HIER had a couple mean/standard deviation of 54.90/17.00 while the cases with HIER obtained 81.50/11.60, which revealed statistically significant differences (p=0.000). For this case study it was concluded that the factor archive period is not associated with changes in immunoreactivity. The importance of HIER in obtaining high quality immunostaining comes out strongly highlighted.

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.

IGRT com CBCT: o impacto na precisão em radioterapia

Mestrado em Radiações Aplicadas às Tecnologias da Saúde.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.