Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.

Ancillary service market simulation

Power systems operation in a liberalized environment requires that market players have access to adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper deals with ancillary services negotiation in electricity markets. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of ancillary services using two different methods (Linear Programming and Genetic Algorithm approaches) is included in the paper.

Ancillary services market clearing simulation: a comparison between deterministic and heuristic methods

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tool must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case based on California Independent System Operator (CAISO) data concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

Estratégias de oferta em leilões simétricos no âmbito dos mercados liberalizados de electricidade

Mestrado em Engenharia Electrotécnica – Sistemas Eléctricos de Energia

Auto-regulação na gestão de conflitos em retalho

Mestrado em Engenharia Informática

Arquitecturas e ferramentas para gestão de redes e sistemas: estudo de um caso

Mestrado em Engenharia Informática - Área de Especialização em Arquitecturas, Sistemas e Redes

Edição gráfica de layout fabril no apoio ao planeamento e controlo da produção

O contributo da área de investigação Interacção Humano-Computador (HCI) está patente não só na qualidade da interacção, mas também na diversificação das formas de interacção. A HCI define-se como sendo uma disciplina que se dedica ao desenho, desenvolvimento e implementação de sistemas de computação interactivos para uso humano e estudo dos fenómenos relevantes que os rodeiam. Pretende-se, no âmbito desta tese de mestrado, o desenvolvimento de um Editor Gráfico de Layout Fabril a integrar num SAD para suporte ao Planeamento e Controlo da Produção. O sistema deve ser capaz de gerar um layout fabril do qual constam, entre outros objectos, as representações gráficas e as respectivas características/atributos do conjunto de recursos (máquinas/processadores) existentes no sistema de produção a modelar. O módulo desenvolvido será integrado no projecto de I&D ADSyS (Adaptative Decision Support System for Interactive Scheduling with MetaCognition and User Modeling Experience), melhorando aspectos de interacção referentes ao sistema AutoDynAgents, um dedicado ao escalonamento, planeamento e controlo de produção. Foi realizada a análise de usabilidade a este módulo com a qual se pretendeu realizar a respectiva avaliação, através da realização de um teste de eficiência e do preenchimento de um inquérito, da qual se identificaram um conjunto de melhorias e sugestões a serem consideradas no refinamento deste módulo.

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 he 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.

Using attractor dynamics to generate decentralized motion control of two mobile robots transporting a long object in coordination

Dynamical systems theory is used here as a theoretical language and tool to design a distributed control architecture for a team of two mobile robots that must transport a long object and simultaneously avoid obstacles. In this approach the level of modeling is at the level of behaviors. A “dynamics” of behavior is defined over a state space of behavioral variables (heading direction and path velocity). The environment is also modeled in these terms by representing task constraints as attractors (i.e. asymptotically stable states) or reppelers (i.e. unstable states) of behavioral dynamics. For each robot attractors and repellers are combined into a vector field that governs the behavior. The resulting dynamical systems that generate the behavior of the robots may be nonlinear. By design the systems are tuned so that the behavioral variables are always very close to one attractor. Thus the behavior of each robot is controled by a time series of asymptotically stable states. Computer simulations support the validity of our dynamic model architectures.

Dimensionamento de uma central de miniprodução fotovoltaica para uma exploração agrícola direcionada à indústria de laticínios

Este documento apresenta o trabalho desenvolvido no âmbito da disciplina de “Dissertação/Projeto/Estágio”, do 2º ano do Mestrado em Energias Sustentáveis. O crescente consumo energético das sociedades desenvolvidas e emergentes, associado ao consequente aumento dos custos de energia e dos danos ambientais resultantes, promove o desenvolvimento de novas formas de produção de energia, as quais têm como prioridade a sua obtenção ao menor custo possível e com reduzidos impactos ambientais. De modo a poupar os recursos naturais e reduzir a emissão com gases de efeito de estufa, é necessária a diminuição do consumo de energia produzida a partir de combustíveis fósseis. Assim, devem ser criadas alternativas para um futuro sustentável, onde as fontes renováveis de energia assumam um papel fundamental. Neste sentido, a produção de energia elétrica, através de sistemas solares fotovoltaicos, surge como uma das soluções. A presente dissertação tem como principal objetivo a realização do dimensionamento de uma central de miniprodução fotovoltaica, com ligação à rede elétrica, em uma exploração agrícola direcionada à indústria de laticínios, e o seu respetivo estudo de viabilidade económica. A exploração agrícola, que serve de objeto de estudo, está localizada na Ilha Graciosa, Açores, sendo a potência máxima a injetar na Rede Elétrica de Serviço Público, pela central de miniprodução, de 10 kW. Para o dimensionamento foi utilizado um software apropriado e reconhecido na área da produção de energia elétrica através de sistemas fotovoltaicos – o PVsyst –, compreendendo as seguintes etapas: a) definição das caraterísticas do local e do projeto; b) seleção dos módulos fotovoltaicos; c) seleção do inversor; d) definição da potência de ligação à rede elétrica da unidade de miniprodução. Posteriormente, foram estudadas diferentes hipóteses de sistemas fotovoltaicos, que se distinguem na opção de estrutura de fixação utilizada: dois sistemas fixos e dois com eixo incorporado. No estudo de viabilidade económica foram realizadas duas análises distintas a cada um dos sistemas fotovoltaicos considerados no dimensionamento, nomeadamente: uma análise em regime remuneratório bonificado e uma análise em regime remuneratório geral. Os resultados obtidos nos indicadores económicos do estudo de viabilidade económica realizado, serviram de apoio à decisão pelo sistema fotovoltaico mais favorável ao investimento. Conclui-se que o sistema fotovoltaico com inclinação adicional é a opção mais vantajosa em ambos os regimes remuneratórios analisados. Comprova-se, assim, que o sistema fotovoltaico com maior valor de produção de energia elétrica anual, que corresponde ao sistema fotovoltaico de dois eixos, não é a opção com maior rentabilidade em termos económicos, isto porque a remuneração proveniente da sua produção excedente não é suficiente para colmatar o valor do investimento mais acentuado de modo a obter indicadores económicos mais favoráveis, que os do sistema fotovoltaico com inclinação adicional. De acordo com o estudo de viabilidade económica efetuado independentemente do sistema fotovoltaico que seja adotado, é recuperado o investimento realizado, sendo a remuneração efetiva superior à que foi exigida. Assim, mesmo tendo em consideração o risco associado, comprova-se que todos os sistemas fotovoltaicos, em qualquer dos regimes remuneratórios, correspondem a investimentos rentáveis.

Metodologia multicritério na seleção de sistemas de informação na gestão da cadeia de abastecimento

Os sistemas de informação empresariais têm vindo nos últimos anos a afirmar- se como ferramentas essenciais no mercado cada vez mais competitivo em que as empresas se encontram. Pretende-se com este trabalho aplicar uma metodologia de apoio à tomada de decisão baseada num modelo decisão multicritério que permite a avaliação e seleção de um sistema de informação (SI) num determinado contexto. Para atingir esse objetivo foi utilizado o estudo de caso na empresa Proef, SGSP. Foi então realizada uma revisão bibliográfica sobre a gestão da cadeia de abastecimento, logística e teoria da decisão para dar suporte a todo o trabalho prático. Posteriormente foi aplicada a metodologia Multicritério para Apoio à seleção de SI – MMASSI/TI com base nos processos existentes na organização. Com recurso à aplicação informática foi obtido o sistema de informação mais adequado ao contexto de decisão, sendo este resultado avaliado através de uma análise de sensibilidade e robustez. Desta dissertação surgiram limitações e também recomendações futuras.

Finding a school technological partner: a multicriteria method for school information system producer selection

The choice of an information systems is a critical factor of success in an organization's performance, since, by involving multiple decision-makers, with often conflicting objectives, several alternatives with aggressive marketing, makes it particularly complex by the scope of a consensus. The main objective of this work is to make the analysis and selection of a information system to support the school management, pedagogical and administrative components, using a multicriteria decision aid system – MMASSITI – Multicriteria Method- ology to Support the Selection of Information Systems/Information Technologies – integrates a multicriteria model that seeks to provide a systematic approach in the process of choice of Information Systems, able to produce sustained recommendations concerning the decision scope. Its application to a case study has identi- fied the relevant factors in the selection process of school educational and management information system and get a solution that allows the decision maker’ to compare the quality of the various alternatives.

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.