Multi-agent management system for electric vehicle charging

This paper proposes an implementation, based on a multi-agent system, of a management system for automated negotiation of electricity allocation for charging electric vehicles (EVs) and simulates its performance. The widespread existence of charging infrastructures capable of autonomous operation is recognised as a major driver towards the mass adoption of EVs by mobility consumers. Eventually, conflicting requirements from both power grid and EV owners require automated middleman aggregator agents to intermediate all operations, for example, bidding and negotiation, between these parts. Multi-agent systems are designed to provide distributed, modular, coordinated and collaborative management systems; therefore, they seem suitable to address the management of such complex charging infrastructures. Our solution consists in the implementation of virtual agents to be integrated into the management software of a charging infrastructure. We start by modelling the multi-agent architecture using a federated, hierarchical layers setup and as well as the agents' behaviours and interactions. Each of these layers comprises several components, for example, data bases, decision-making and auction mechanisms. The implementation of multi-agent platform and auctions rules, and of models for battery dynamics, is also addressed. Four scenarios were predefined to assess the management system performance under real usage conditions, considering different types of profiles for EVs owners', different infrastructure configurations and usage and different loads on the utility grid (where real data from the concession holder of the Portuguese electricity transmission grid is used). Simulations carried with the four scenarios validate the performance of the modelled system while complying with all the requirements. Although all of these have been performed for one charging station alone, a multi-agent design may in the future be used for the higher level problem of distributing energy among charging stations. Copyright (c) 2014 John Wiley & Sons, Ltd.

Bacterial enzymes and multi-enzymatic systems for cleaning-up dyes from the environment

Synthetic dyes are xenobiotic compounds that are being increasingly used in several industries, with special emphasis in the paper, textile and leather industries. Over 100,000 commercial dyes exist today and more than 7 × 105 tons of dyestuff is produced annually, of which 1–1.5 × 105 tons is released into the wastewaters (Rai et al in Crit Rev Environ Sci Tecnhol 35:219–238, 2005). Among these, azo dyes, characterized by the presence of one or more azo groups (–N=N–), and anthraquinonic dyes represent the largest and most versatile groups.

Calibração automática para sistema de localização Multi-câmara

Mestrado em Engenharia Electrotécnica e de Computadores - Sistemas Autónomos

Aplicação do multi-layer stream mapping para a gestão da eficiência de recursos e produtividade de um sistema de produção

Mestrado em Engenharia Mecânica – Especialização Gestão Industrial

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

Análise de Desempenho e Recomendação para Soluções IVR

Mestrado em Engenharia Informática, Área de Especialização em Tecnologias do Conhecimento e da Decisão

Investigar em educação: desafios da construção de conhecimento e da formação de investigadores num campo multi-referenciado

O livro que agora se apresenta decorre do trabalho desenvolvido num ciclo de seminários realizados no ano lectivio de 2008/09 e pretende explicitar e sistematizar alguns elementos em torno das principais questões organizadoras: o quê investigar em educação? Como se investiga em educação? Para quê investigar em educação? Porém, não são respostas definitivas ou acabadas que se pretendem alcançar, nem tão pouco podemos escamotear que se tratam de respostas situadas em função dos percursos formativos e profissionais dos investigadores, mestrandos e doutorandos envolvidos nos seminários. Procuramos, acima de tudo, explicitar o posicionamento que, no nosso caso, tem vindo a ser privilegiado em termos de trabalho científico no campo da investigação em educação

Iberian peninsula ecosystem carbon fluxes: a model-data integration study

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia do Ambiente pela Universidade Nova de Lisboa,Faculdade de Ciências e Tecnologia

RealCrono –sistema de difusão de resultados em tempo real na web

A velocidade de difusão de conteúdos numa plataforma web, assume uma elevada relevância em serviços onde a informação se pretende atualizada e em tempo real. Este projeto de Mestrado, apresenta uma abordagem de um sistema distribuído de recolher e difundir resultados em tempo real entre várias plataformas, nomeadamente sistemas móveis. Neste contexto, tempo real entende-se como uma diferença de tempo nula entre a recolha e difusão, ignorando fatores que não podem ser controlados pelo sistema, como latência de comunicação e tempo de processamento. Este projeto tem como base uma arquitetura existente de processamento e publicação de resultados desportivos, que apresentava alguns problemas relacionados com escalabilidade, segurança, tempos de entrega de resultados longos e sem integração com outras plataformas. Ao longo deste trabalho procurou-se investigar fatores que condicionassem a escalabilidade de uma aplicação web dando ênfase à implementação de uma solução baseada em replicação e escalabilidade horizontal. Procurou-se também apresentar uma solução de interoperabilidade entre sistemas e plataformas heterogêneas, mantendo sempre elevados níveis de performance e promovendo a introdução de plataformas móveis no sistema. De várias abordagens existentes para comunicação em tempo real sobre uma plataforma web, adotou-se um implementação baseada em WebSocket que elimina o tempo desperdiçado entre a recolha de informação e sua difusão. Neste projeto é descrito o processo de implementação da API de recolha de dados (Collector), da biblioteca de comunicação com o Collector, da aplicação web (Publisher) e sua API, da biblioteca de comunicação com o Publisher e por fim a implementação da aplicação móvel multi-plataforma. Com os componentes criados, avaliaram-se os resultados obtidos com a nova arquitetura de forma a aferir a escalabilidade e performance da solução criada e sua adaptação ao sistema existente.

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.