A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Renewable-based generation integration in electricity markets with virtual power producers

The development of renewable energy sources and Distributed Generation (DG) of electricity is of main importance in the way towards a sustainable development. However, the management, in large scale, of these technologies is complicated because of the intermittency of primary resources (wind, sunshine, etc.) and small scale of some plants. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. VPPs can ensure a secure, environmentally friendly generation and optimal management of heat, electricity and cold as well as optimal operation and maintenance of electrical equipment, including the sale of electricity in the energy market. For attaining these goals, there are important issues to deal with, such as reserve management strategies, strategies for bids formulation, the producers’ remuneration, and the producers’ characterization for coalition formation. This chapter presents the most important concepts related with renewable-based generation integration in electricity markets, using VPP paradigm. The presented case studies make use of two main computer applications:ViProd and MASCEM. ViProd simulates VPP operation, including the management of plants in operation. MASCEM is a multi-agent based electricity market simulator that supports the inclusion of VPPs in the players set.

Bid definition method for electricity markets based on an adaptive multiagent system

Electricity markets are complex environments with very particular characteristics. MASCEM is a market simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is multiagent based, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. Each agent has the knowledge about a different method for defining a strategy for playing in the market, the main agent chooses the best among all those, and provides it to the market player that requests, to be used in the market. This paper also presents a methodology to manage the efficiency/effectiveness balance of this method, to guarantee that the degradation of the simulator processing times takes the correct measure.

Using an ambient intelligent architecture for developing an intelligent tutoring system for training operators of power system control centres

This paper describes an architecture conceived to integrate Power Sys-tems tools in a Power System Control Centre, based on an Ambient Intelligent (AmI) paradigm. This architecture is an instantiation of the generic architecture proposed in [1] for developing systems that interact with AmI environments. This architecture has been proposed as a consequence of a methodology for the inclu-sion of Artificial Intelligence in AmI environments (ISyRAmI - Intelligent Sys-tems Research for Ambient Intelligence). The architecture presented in the paper will be able to integrate two applications in the control room of a power system transmission network. The first is SPARSE expert system, used to get diagnosis of incidents and to support power restoration. The second application is an Intelligent Tutoring System (ITS) incorporating two training tools. The first tutoring tool is used to train operators to get the diagnosis of incidents. The second one is another tutoring tool used to train operators to perform restoration procedures.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Cyber-physical intelligence in the context of power systems

Cyber-Physical Intelligence is a new concept integrating Cyber-Physical Systems and Intelligent Systems. The paradigm is centered in incorporating intelligent behavior in cyber-physical systems, until now too oriented to the operational technological aspects. In this paper we will describe the use of Cyber-Physical Intelligence in the context of Power Systems, namely in the use of Intelligent SCADA (Supervisory Control and Data Acquisition) systems at different levels of the Power System, from the Power Generation, Transmission, and Distribution Control Centers till the customers houses.

Ensino e aprendizagem da Bioquímica em Ciências da Saúde: retenção de conhecimentos adquiridos e sucesso académico versus tipo de exame preferido, sua preparação e stress associado

Num contexto de reestruturação da formação em Ciências da Saúde baseada em competências, torna-se necessário estudar as diferentes formas de avaliação e o stress que lhe está associado, observando de que forma estas poderão interferir no sucesso académico dos alunos e real aquisição de competências. O objectivo do trabalho foi avaliar estas questões no âmbito da Bioquímica de diferentes cursos de Tecnologias da Saúde. A metodologia foi adaptada de controlled pre-test-postest, e os resultados obtidos apontam para que os alunos que mais estudam, ao longo do ano e não no “sprint final”, parecem possuir menor stress e serem melhor sucedidos.

Multi-agent negotiation for coalition formation and management in electricity markets

Negotiation is a fundamental tool for reaching understandings that allow each involved party to gain an advantage for themselves by the end of the process. In recent years, with the increasing of compe-titiveness in most sectors, negotiation procedures become present in practically all of them. One particular environment in which the competitiveness has been increasing exponentially is the electricity markets sector. This work is directed to the study of electricity markets’ partici-pating entities interaction, namely in what concerns the formation, management and operation of aggregating entities – Virtual Power Players (VPPs). VPPs are responsible for managing coalitions of market players with small market negotiating influence, which take strategic advantage in entering such aggregations, to increase their negotiating power. This chapter presents a negotiation methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using MASCEM, taking advantage of its ability to provide the means to model and simulate VPPs. VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself.

Estudo das alterações imunofenotípicas de populações linfocitárias em doentes com artrite reumatóide

A Artrite Reumatóide (AR) é uma doença auto-imune que devido às suas características tornam por si só os indivíduos afectados susceptíveis a infecções; imunocomprometimento que por vezes ainda é agravado por terapêuticas imunomodulatórias usadas para o seu tratamento. Este estudo tem como objectivo analisar, as populações/sub-populações de linfócitos conjuntamente com a análise das imunoglobulinas G e M, apresentando como factores discriminatórios, a idade, o sexo e a presença de terapêutica imunomodulatória, nos doentes com AR. Os resultados sugerem uma depleção significativa dos linfócitos B e um aumento dos T, os quais dão indícios para um aumento da susceptibilidade a infecções.

Estudo electromiográfico do conflito sub-acromial

Objectivo: estudar a actividade muscular no complexo articular do ombro e tronco durante o serviço e a sua relação com o Conflito Sub-Acromial. Metodologia: estudo observacional, analítico, transversal. Amostra de 15 atletas de voleibol distribuídas em 3 grupos e avaliadas através de electromiografia de superfície em músculos do ombro e do tronco. Foi avaliada a percentagem de activação muscular nas diferentes fases do serviço. Resultados: aumento de actividade do Trapézio Superior e Infraespinhoso e diminuição do Recto Abdominal e Oblíquo Externo no grupo com Conflito Sub-Acromial. Conclusão: existem diferenças significativas nas atletas com e sem Conflito Sub-Acromial.

Extended-spectrum β-lactamases from the North of Portugal in the boundaries to Spain: emergence of high resistance to 4th generation cephalosporins

During the past 15 years, emergence and dissemination of third-generation cephalosporins resistance in nosocomial Enterobacteriaceae became a serious problem worldwide, due to the production of extended-spectrum-β-lactamases (ESBLs). The aim of this study was to investigate among the presence of ESBL-producing enterobacteria among Portuguese clinical isolates nearby Spain, to investigate the antimicrobial susceptibility patterns and to compare the two countries. The β-lactamases genes, blaTEM, blaSHV and blaCTX-M were detected by molecular methods. Among the ESBL-producing isolates it was found extraordinary levels (98.9%) of resistance to the fourth-generation cephalosporin Cefepime. These findings point to the need of reevaluate the definition of ESBL.

Factores condicionantes da dádiva benévola de sangue em estudantes universitários em Luanda

A proporção de colheitas de dadores voluntários pode ser usada como indicador não só do potencial de disponibilidade de sangue mas também da sua segurança. Em Luanda, Angola, a dádiva voluntária correspondeu a cerca de 23% das dádivas em 2004. Foi realizado um estudo observacional descritivo transversal pela aplicação de um questionário constituído por itens que captam factores que motivam ou inibem a dádiva benévola de sangue, numa amostra de estudantes universitários em Luanda. Apesar da baixa dádiva voluntária indicada pelos respondentes, verificou-se uma elevada disponibilidade para a dádiva benévola, essencial no combate ao HIV.

FineFIX®: fixador alcoólico alternativo para a prática citológica?

Um dos factores que contribui para a baixa sensibilidade da citologia urinária é a fixação em etanol. Neste trabalho foi avaliada a citomorfologia e as características tintoriais de lavados vesicais fixados pelo FineFIX® ou pelo RCL2® comparativamente aos fixados pelo etanol. A análise dos achados microscópicos revelou que as preparações fixadas pelo RCL2® apresentam qualidade inferior (p=0,001), enquanto as fixadas pelo FineFIX® apresentam qualidade semelhante às fixadas pelo etanol (p=0,204). Relativamente às características nucleares, o FineFIX® providencia qualidade superior ao etanol (p=0,027). O FineFIX® é um potencial fixador citológico por aumentar o detalhe nuclear, característica importante para o diagnóstico.