Qualidade, higiene e segurança na gestão das empresas

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração para obtenção do Grau de Mestre em Auditoria. Orientada por: Mestre Alcina Dias

Multiagent system for adaptive strategy formulation in electricity markets

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.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had 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 behaviour. 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 dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Avaliação dos ajustes posturais em indivíduos com instabilidade funcional do tornozelo

Introdução:O Controlo Postural é um processo neural complexo envolvido na organização da estabilidade e orientação da posição do corpo no espaço. A Instabilidade Funcional (IF) do tornozelo é descrita como uma perceção subjetiva de instabilidade articular, que afeta o controlo postural. Apesar de vários estudos terem investigado os fatores inerentes à IF ainda existe inconsistência nos resultados da literatura sobre os mecanismos envolvidos nesta. Objetivo (s):avaliar os ajustes posturais envolvidos na resposta a uma perturbação externa realizada de forma previsível e imprevisível em indivíduos com IF. Métodos:Estudo observacional analítico transversal, teve uma amostra de 20 indivíduos, que foram divididos em grupo com IF e grupo de controlo. Foi recolhida atividade eletromiográfica bilateral dos músculos longo e curto peroneal (PL e PC), tibial anterior (TA) e solear (SOL) associado a uma perturbação externa aplicada de forma previsível e imprevisível. Os ajustes posturais foram avaliados através da análise do início da atividade muscular, da magnitude global dos ajustes posturais compensatórios e antecipatórios e magnitude das respostas de curta e média latência Resultados: Na perturbação imprevisível não se verificaram diferenças significativas no início da atividade muscular (p>0,05). Enquanto na magnitude das respostas de curta e média latência verificou-se diferenças nos músculos TA (Ia,p=0,000; II, p=0,011), CP (Ia,p=0,029; II, p=0,001) e LP (Ia, p=0,030) entre o membro com IF e o controlo e no LP (II, p=0,011) entre o membro sem IF do grupo com IF e o controlo. Na perturbação previsível observaram-se diferenças nos ajustes posturais antecipatórios (APA) dos músculos TA (p=0,006) e LP (p=0,020) entre o membro sem IF do grupo com IF e o controlo. Conclusão: Os indivíduos com IF apresentam défices na magnitude das respostas de média e curta latência numa perturbação imprevisível e nos APA na perturbação previsível.

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.

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.