Fonte de Iluminação LED – Avaliação dos Efeitos Fotobiológicos no Olho e Pele

Tendo por referência a diretiva 2006/95/CE, o trabalho desenvolvido no contexto da disciplina de Dissertação/Projeto/Estágio do Mestrado de Engenharia de Instrumentação e Metrologia, decorreu nas instalações do IEP (Instituto Electrotécnico Português) e teve como objetivo principal o desenvolvimento de um procedimento de avaliação dos efeitos fotobiológicos no olho e pele provocados por fontes de emissão contínua (LED), doravante designado método alternativo ao de referência. Os dois métodos, alternativo e de referência, utilizam respectivamente um foto-radiómetro multicanal e um espetro-radiómetro. O procedimento desenvolvido (método alternativo) de acordo com a norma EN/IEC62471) consiste na aquisição dos valores de irradiância com recurso a um foto-radiómetro e posterior determinação dos valores da radiância, com os quais se faz a avaliação dos efeitos fotobiológicos, para fontes de luz LED (Light Emitting Diode) ou GLS (General Lighting Service). A consulta detalhada da norma EN/IEC62471 e a pesquisa sobre os conceitos, definições, equipamentos e metodologias relacionadas com o tema em causa, constituiu o primeiro passo deste projecto. Com recurso aos dois equipamentos, uma fonte de luz LED (módulo de 12 lâmpadas LED) é avaliada em relação aos perigos (ou riscos) actínico UV e UV-A, ao perigo da luz azul e ainda o perigo térmico na retina e térmico na pele, permitindo fazer uma análise comparativa dos resultados. O método alternativo revelou-se bastante flexível e eficaz, proporcionando bons resultados em termos da irradiância e radiância dos referidos efeitos fotobiológicos. A comparação destes resultados com os valores limites de exposição mencionados na norma EN/IEC6247 permitiu afirmar que a fonte de luz LED avaliada não representa perigo fotobiológico para a saúde humana e classifica-se no grupo de risco “isento”. Uma vez cumpridos os objectivos, entendeu-se que seria uma mais-valia para o trabalho já realizado, estudar outro caso prático. Sendo assim, fez-se a avaliação da radiação de apenas um dos LED´s que constituíam a fonte usada nos ensaios anteriores, com o espetro-radiómetro (método de referência) e com uma distância de 200 mm entre a fonte e o medidor. Neste caso verificaram-se diferenças significativas nas quantidades obtidas quando comparadas com os valores normativos. Concluiu-se que o efeito fotobiológico da luz azul insere-se no grupo de “isento”, sem perigo para a saúde. Contudo, o efeito térmico da retina apresenta um aumento considerável da quantidade de radiância, embora dentro do grupo de risco “isento”. Esta classificação de grupos de risco. Face aos resultados obtidos, pode confirmar-se que as lâmpadas LED apresentam segurança fotobiológica, atendendo aos baixos valores de irradiância e radiância dos efeitos fotobiológicos estudados. Pode ainda afirmar-se que a utilização do foto-radiómetro em alternativa ao espetro-radiómetro se revela mais eficaz do ponto de vista de metodologia prática. Este trabalho demonstra a robustez desses dois equipamentos de avaliação dos efeitos fotobiológicos, e procura estabelecer uma linha de orientação para a prevenção dos efeitos adversos na pele e olhos de todos os seres humanos sujeitos à radiação ótica artificial. Quanto às incertezas de medições, em relação ao processo de medição com foto-radiómetro, a sua estimação não se realizou, devido a não rastreabilidade entre as medições indicadas pelo fabricante, no certificado de calibração e as medidas realizadas por outras entidades. Contudo, é propõe-se a sua realização em trabalhos futuros dentro desse âmbito. As incertezas dos resultados de medições com espetro-radiómetro foram parcialmente estimadas. Atendendo às potencialidades do sistema de medição, propõe-se como trabalho futuro, a aplicação da norma IEC62478, que faz parte da aplicação da norma EN/IEC62471 na avaliação do efeito da luz azul, com base na determinação da temperatura de cor correlacionada (CCT) de lâmpadas ou sistemas de lâmpadas incluindo luminárias. Os valores de irradiância e radiância adquiridos nos processos de avaliação, tanto com foto-radiómetro como espectro-radiómetro foram gravados em ficheiro Excel para um CD e anexados a este trabalho.

Sensor electroquímico seletivo para a Norfloxacina

A Norfloxacina (NFX) é um antibiótico antibacteriano indicado para combater bactérias Gram-negativas e amplamente utilizado para o tratamento de infeções no trato respiratório e urinário. Com a necessidade de realizar estudos clínicos e farmacológicos esenvolveram-se métodos de análise rápida e sensitiva para a determinação da Norfloxacina. Neste trabalho foi desenvolvido um novo sensor eletroquímico sensível e seletivo para a deteção da NFX. O sensor foi construído a partir de modificações efetuadas num elétrodo de carbono vítreo. Inicialmente o elétrodo foi modificado com a deposição de uma suspensão de nanotubos de carbono de paredes múltiplas (MWCNT) de modo a aumentar a sensibilidade de resposta analítica. De seguida um filme polímerico molecularmente impresso (MIP) foi preparado por eletrodeposição, a partir de uma solução contendo pirrol (monómero funcional) e NFX (template). Um elétrodo de controlo não impresso foi também preparado (NIP). Estudouse e caraterizou-se a resposta eletroquímica do sensor para a oxidação da NFX por voltametria de onda quadrada. Foram optimizados diversos parâmetros experimentais, tais como, condições ótimas de polimerização, condições de incubação e condições de extração. O sensor apresenta um comportamento linear entre a intensidade da corrente do pico e o logaritmo da concentração de NFX na gama entre 0,1 e 8μM. Os resultados obtidos apresentam boa precisão, com repetibilidade inferior a 6% e reprodutibilidade inferior a 9%. Foi calculado a partir da curva de calibração um limite de deteção de 0,2 μM O método desenvolvido é seletivo, rápido e de fácil manuseamento. O sensor molecularmente impresso foi aplicado com sucesso na deteção da NFX em amostras de urina real e água.

Modelo de seleção de fornecedores baseado em dados reais – plataforma informática e dados de suporte ao modelo

Atualmente, as estratégias que as empresas optam por seguir para a maximização de recursos materiais e humanos, podem representar a diferença entre o sucesso e o fracasso. A seleção de fornecedores é um fator bastante crítico para o desempenho da empresa compradora, sendo por vezes necessária a resolução de problemas que apresentam um elevado grau de complexidade. A escolha dos métodos a ser utilizados e a eleição dos critérios mais relevantes foi feito com base no estudo de diversos autores e nas repostas obtidas a um inquérito online difundido por uma amostra de empresas portuguesas, criado especificamente para compreender quais os fatores que mais peso tinham nas decisões de escolha de parceiros. Além disso, os resultados adquiridos desta forma foram utilizados para conceder mais precisão às ponderações efetuadas na ferramenta de seleção, na escolha dos melhores fornecedores introduzidos pelos utilizadores da mesma. Muitos estudos literários propõem o uso de métodos para simplificar a tarefa de seleção de fornecedores. Esta dissertação aplica o estudo realizado nos métodos de seleção, nomeadamente o Simple Multi-Attribute Rating Technique (SMART) e Analytic Hierarchy Process (AHP), necessários para o desenvolvimento de uma ferramenta de software online que permitia, a qualquer empresa nacional, obter uma classificação para os seus fornecedores perante um conjunto de critérios e subcritérios.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Intelligent Micro Grid Management using a Multi-Agent approach

Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players’ behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents’ reaction to price changes, is an interesting tool for the micro grid operator.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

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.

Multi-agent Simulation of Bilateral Contracting in Competitive Electricity Markets

Traditional vertically integrated power utilities around the world have evolved from monopoly structures to open markets that promote competition among suppliers and provide consumers with a choice of services. Market forces drive the price of electricity and reduce the net cost through increased competition. Electricity can be traded in both organized markets or using forward bilateral contracts. This article focuses on bilateral contracts and describes some important features of an agent-based system for bilateral trading in competitive markets. Special attention is devoted to the negotiation process, demand response in bilateral contracting, and risk management. The article also presents a case study on forward bilateral contracting: a retailer agent and a customer agent negotiate a 24h-rate tariff.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.