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.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

O jornalismo de proximidade como promotor da cultura e identidade de uma região: o caso do Porto Canal

Relatório de estágio apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Jornalismo.

Estudo numérico e experimental sobre o comportamento dinâmico de um edifício

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Estruturas

Velocidade da corrosão do aço em argamassas cimentícias modificadas com polímeros

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química

Projecto de execução de estruturas e fundações de uma obra de arte corrente em betão armado e pré-esforçado

Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Civil.

Reabilitação do edifício localizado na rua Miguel Bombarda nº 69 em Loulé

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

Análise de ciclo de vida de duas soluções de ETICS

Dissertação de natureza científica para obtenção do grau de mestre em Engenharia Civil

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.

Acompanhamento da reabilitação estrutural da ETA de Morgavel

Relatório de Estágio para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

Teor crítico de cloretos para iniciação da corrosão do aço no betão: influência do estado de superfície da armadura

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Química e Biológica - Processos Químicos

Sustainable waste recycling solution for the glass fibre reinforced polymer composite materials industry

In this paper the adequacy and the benefit of incorporating glass fibre reinforced polymer (GFRP) waste materials into polyester based mortars, as sand aggregates and filler replacements, are assessed. Different weight contents of mechanically recycled GFRP wastes with two particle size grades are included in the formulation of new materials. In all formulations, a polyester resin matrix was modified with a silane coupling agent in order to improve binder-aggregates interfaces. The added value of the recycling solution was assessed by means of both flexural and compressive strengths of GFRP admixed mortars with regard to those of the unmodified polymer mortars. Planning of experiments and data treatment were performed by means of full factorial design and through appropriate statistical tools based on analyses of variance (ANOVA). Results show that the partial replacement of sand aggregates by either type of GFRP recyclates improves the mechanical performance of resultant polymer mortars. In the case of trial formulations modified with the coarser waste mix, the best results are achieved with 8% waste weight content, while for fine waste based polymer mortars, 4% in weight of waste content leads to the higher increases on mechanical strengths. This study clearly identifies a promising waste management solution for GFRP waste materials by developing a cost-effective end-use application for the recyclates, thus contributing to a more sustainable fibre-reinforced polymer composites industry.

Tipificação de fundações de torres eólicas em parques industriais, para diversos tipos de solos

Dissertação para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Edificações

Dimensionamento de um pavilhão industrial misto com fundações indirectas

Trabalho de Projecto para obtenção do grau de Mestre em Engenharia Civil Perfil Estruturas

Estudo do comportamento sísmico de barragens de gravidade com ancoragens passivas

Dissertação de Natureza Científica para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Estruturas