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.

Data Extraction Tool to Analyse, Transform and Store Real Data from Electricity Markets

The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

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 a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Voltage Magnitude Analysis Considering Load Curtailment Minimization in Sub-Transmission Networks with Distributed Generators

Most of distribution generation and smart grid research works are dedicated to the study of network operation parameters, reliability among others. However, many of this research works usually uses traditional test systems such as IEEE test systems. This work proposes a voltage magnitude study in presence of fault conditions considering the realistic specifications found in countries like Brazil. The methodology considers a hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzyprobabilistic models and a remedial action algorithm which is based on optimal power flow. To illustrate the application of the proposed method, the paper includes a case study that considers a real 12 bus sub-transmission network.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

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.

Voltage Analysis and Load Curtailment Minimization in Sub-transmission Networks with Distributed Generation

Most of distributed generation and smart grid research works are dedicated to network operation parameters studies, reliability, etc. However, many of these works normally uses traditional test systems, for instance, IEEE test systems. This paper proposes voltage magnitude and reliability studies in presence of fault conditions, considering realistic conditions found in countries like Brazil. The methodology considers a hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models and a remedial action algorithm which is based on optimal power flow. To illustrate the application of the proposed method, the paper includes a case study that considers a real 12-bus sub-transmission network.

Simulador em Realidade Virtual para Aprendizagem da Física

A forma como aprendemos depende do contexto tecnológico e sociocultural que nos rodeia, actualmente a inclusão de tecnologia recente na sala de aula não é mais considerada opcional, mas sim uma necessidade pois a forma como o aluno aprende está em constante evolução. Tendo em atenção esta necessidade, foi desenvolvido no decorrer desta tese um simulador em realidade virtual que utiliza comandos/interfaces hápticos. O objectivo deste simulador é ensinar conceitos de física de forma interactiva. Os dispositivos hápticos permitem adicionar o sentido táctil ou de toque à interacção entre homem e máquina, permitindo assim aceder a novas sensações relativas ao seu uso nomeadamente com objectivos de aprendizagem. O simulador desenvolvido designado por “Forces of Physics” aborda três tipos de forças da física: forças de atrito, forças gravitacionais e forças aerodinâmicas. Cada tipo de força corresponde a um módulo do simulador contendo uma simulação individual em que são explicados conceitos específicos dessa força num ambiente visual estimulante e com uma interacção mais realista devido à inclusão do dispositivo háptico Novint Falcon. O simulador foi apresentado a vários utilizadores bem como á comunidade científica através de apresentações em conferências. A avaliação foi realizada com recurso a um questionário com dez perguntas, cinco de sobre aprendizagem e cinco sobre a utilização, tendo sido preenchido por 14 utilizadores. O simulador obteve uma boa recepção por parte dos utilizadores, tendo vários utilizadores expressado as suas opiniões sobre estado actual do simulador, do futuro do mesmo e da respectiva validade para uso na sala de aula.

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.

Probabilistic maintenance and optimization strategies for deteriorating civil infrastructures

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role. In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating civil infrastructures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis,through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

Archaeometallurgical study of artefacts from Castro de Vila Nova de São Pedro (Azambuja, Portugal)

Dissertação para obtenção do Grau de Mestre em Conservação e Restauro

Modelação numérica de uma caldeira doméstica a pellets

A biomassa é uma das fontes de energia renovável com maior potencial em Portugal, sendo a capacidade de produção de pellets de biomassa atualmente instalada superior a 1 milhão de toneladas/ano. Contudo, a maioria desta produção destina-se à exportação ou à utilização em centrais térmicas a biomassa, cujo crescimento tem sido significativo nos últimos anos, prevendo-se que a capacidade instalada em 2020 seja de aproximadamente 250 MW. O mercado português de caldeiras a pellets é bastante diversificado. O estudo que realizamos permitiu concluir que cerca de 90% das caldeiras existentes no mercado português têm potências inferiores a 60 kW, possuindo na sua maioria grelha fixa (81%), com sistema de ignição eléctrica (92%) e alimentação superior do biocombustível sólido (94%). O objetivo do presente trabalho foi o desenvolvimento de um modelo para simulação de uma caldeira a pellets de biomassa, que para além de permitir otimizar o projeto e operação deste tipo de equipamento, permitisse avaliar as inovações tecnológicas nesta área. Para tal recorreu-se o BiomassGasificationFoam, um código recentemente publicado, e escrito para utilização com o OpenFOAM, uma ferramenta computacional de acesso livre, que permite a simulação dos processos de pirólise, gasificação e combustão de biomassa. Este código, que foi inicialmente desenvolvido para descrever o processo de gasificação na análise termogravimétrica de biomassa, foi por nós adaptado para considerar as reações de combustão em fase gasosa dos gases libertados durante a pirólise da biomassa (recorrendo para tal ao solver reactingFoam), e ter a possibilidade de realizar a ignição da biomassa, o que foi conseguido através de uma adaptação do código de ignição do XiFoam. O esquema de ignição da biomassa não se revelou adequado, pois verificou-se que a combustão parava sempre que a ignição era inativada, independentemente do tempo que ela estivesse ativa. Como alternativa, usaram-se outros dois esquemas para a combustão da biomassa: uma corrente de ar quente, e uma resistência de aquecimento. Ambos os esquemas funcionaram, mas nunca foi possível fazer com que a combustão fosse autossustentável. A análise dos resultados obtidos permitiu concluir que a extensão das reações de pirólise e de gasificação, que são ambas endotérmicas, é muito pequena, pelo que a quantidade de gases libertados é igualmente muito pequena, não sendo suficiente para libertar a energia necessária à combustão completa da biomassa de uma maneira sustentável. Para tentar ultrapassar esta dificuldade foram testadas várias alternativas, , que incluíram o uso de diferentes composições de biomassa, diferentes cinéticas, calores de reação, parâmetros de transferência de calor, velocidades do ar de alimentação, esquemas de resolução numérica do sistema de equações diferenciais, e diferentes parâmetros dos esquemas de resolução utilizados. Todas estas tentativas se revelaram infrutíferas. Este estudo permitiu concluir que o solver BiomassGasificationFoam, que foi desenvolvido para descrever o processo de gasificação de biomassa em meio inerte, e em que a biomassa é aquecida através de calor fornecido pelas paredes do reator, aparentemente não é adequado à descrição do processo de combustão da biomassa, em que a combustão deve ser autossustentável, e em que as reações de combustão em fase gasosa são importantes. Assim, é necessário um estudo mais aprofundado que permita adaptar este código à simulação do processo de combustão de sólidos porosos em leito fixo.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire’s shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.