Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method 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.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

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 simulates the electricity markets environment. 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 the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

Intelligent decision support systems for collaboration in industrial plants

Dissertação apresentada para obtenção do Grau de Doutor em Sistemas de Informação Industriais, Engenharia Electrotécnica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Towards a unified European electricity market: The contribution of data-mining to support realistic simulation studies

Worldwide electricity markets have been evolving into regional and even continental scales. The aim at an efficient use of renewable based generation in places where it exceeds the local needs is one of the main reasons. A reference case of this evolution is the European Electricity Market, where countries are connected, and several regional markets were created, each one grouping several countries, and supporting transactions of huge amounts of electrical energy. The continuous transformations electricity markets have been experiencing over the years create the need to use simulation platforms to support operators, regulators, and involved players for understanding and dealing with this complex environment. This paper focuses on demonstrating the advantage that real electricity markets data has for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations will bring to the participant countries. A case study using MASCEM (Multi-Agent System for Competitive Electricity Markets) is presented, with a scenario based on real data, simulating the European Electricity Market environment, and comparing its performance when using several different market mechanisms.

A Communication and Resources Management Scheme to Support the Smart Grid Integration of Multiplayers Access to Resources Information

The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.

Solar Intensity Forecasting using Artificial Neural Networks and Support Vector Machines

This paper presents several forecasting methodologies based on the application of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), directed to the prediction of the solar radiance intensity. The methodologies differ from each other by using different information in the training of the methods, i.e, different environmental complementary fields such as the wind speed, temperature, and humidity. Additionally, different ways of considering the data series information have been considered. Sensitivity testing has been performed on all methodologies in order to achieve the best parameterizations for the proposed approaches. Results show that the SVM approach using the exponential Radial Basis Function (eRBF) is capable of achieving the best forecasting results, and in half execution time of the ANN based approaches.

Short-term Wind Speed Forecasting using Support Vector Machines

Wind speed forecasting has been becoming an important field of research to support the electricity industry mainly due to the increasing use of distributed energy sources, largely based on renewable sources. This type of electricity generation is highly dependent on the weather conditions variability, particularly the variability of the wind speed. Therefore, accurate wind power forecasting models are required to the operation and planning of wind plants and power systems. A Support Vector Machines (SVM) model for short-term wind speed is proposed and its performance is evaluated and compared with several artificial neural network (ANN) based approaches. A case study based on a real database regarding 3 years for predicting wind speed at 5 minutes intervals is presented.

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.

Sistema de Informação como suporte ao Sistema de Gestão da Qualidade

Dissertação de Mestrado em Engenharia Informática

Otimização do sítio web e google adwords

Relatório de estágio apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Empreendedorismo e Internacionalização, sob orientação de Doutora Ana Azevedo.

Eficiência energética na iluminação pública

O presente trabalho aborda a temática da eficiência energética em sistemas de iluminação pública. A principal motivação prende-se com o peso significativo que a parcela energética destes sistemas ocupa na economia mundial. O uso eficiente de energia é uma crescente preocupação devido à diminuição de recursos, às consequências climáticas cada vez mais marcadas e ao elevado custo da energia, representando ainda um papel fundamental ao nível económico e de competitividade. A Iluminação Pública (IP) representa um peso importante nas despesas correntes dos municípios. É assim importante encontrar uma solução que permita manter níveis de segurança e conforto necessários às populações e que proporcione uma redução substancial do peso da IP nas despesas municipais. Neste sentido, este trabalho propõe-se estudar esta problemática, apresentando uma sistematização de soluções eficientes, quer a nível de lâmpadas e luminárias como também ao nível de tecnologias que auxiliem e complementem a eficiência de uma instalação de iluminação pública. A dissertação está dividida em duas partes. A primeira parte sistematiza os consumos verificados em Portugal, a vários níveis (consumo de energia elétrica, evolução do consumo energético de iluminação pública, etc.) abordando as políticas de eficiência energética, e são descritos alguns procedimentos que possibilitam a poupança energética na iluminação pública, aliada a instalações eficientes. A segunda parte da dissertação contempla o estudo de um caso prático cujo objetivo é propor soluções técnicas que permitam melhorar a eficiência energética na iluminação pública de Esposende, face à situação atual do concelho. Serão propostas várias soluções, tais como luminárias LED, balastros electrónicos reguláveis, lâmpadas de menor consumo e até mesmo o uso da telegestão.

Revisão, aplicação e avaliação dos mercados eletrónicos - emarket

Os mercados eletrónicos são sistemas de informação (SI) utilizados por várias entidades organizacionais distintas dentro de um ou vários níveis em termos das cadeias de valor económico (Journal Electronic Markets, 2012). Segundo (Bakos, 1998) têm um papel central na economia, facilitando a troca de informações, produtos, serviços e pagamentos. Durante o processo, é criado valor económico para o comprador, fornecedor, intermediários do mercado e para a sociedade em geral. O comércio eletrónico é o ato de realizar um qualquer tipo de negócio através de uma via eletrónica e é constituído por modelos diversificados onde se destacam o Business to Business (B2B) e o Business to Consumer (B2C). O modelo B2B possui uma quota de 90 % de todo o comércio, sendo esse sucesso intrinsecamente relacionado em grande parte às vantagens que as suas plataformas oferecem às empresas que inseridas nelas (Anacom, 2004). O âmbito principal deste trabalho é o estudo dos B2B, tendo sido para tal definidos os seguintes objetivos: Realizar a identificação do estado atual bem como a evolução dos mercados B2B em Portugal; Caraterização das funcionalidades das plataformas que atuam no tecido nacional e por fim fazer a criação de um conjunto de orientações de apoio a empresas que desejam fazer a inserção nestes mercados. Para serem alcançados os objetivos propostos na dissertação, foram inquiridas várias organizações ao mesmo tempo que foi realizada uma pesquisa de temas e artigos relacionados com os mercados eletrónicos e plataformas B2B, recorrendo a sites como a B-On.pt, e utilizando o motor de busca Google. Este relatório apresenta a seguinte estrutura: No capítulo 1 é apresentada a introdução teórica das matérias apresentadas nos capítulos seguintes; O capítulo 2 é centrado no comércio eletrónico, definições mais comuns e são demonstrados os modelos mais preponderantes do comércio eletrónico; No capítulo 3 são expostas todas as vertentes do modelo B2B, as principais plataformas B2B a atuar no tecido nacional, as suas funcionalidades e modo de operação bem como uma apresentação das principais plataformas a nível mundial; No capítulo 4 são apresentados um conjunto de tópicos de auxílio a empresas que desejem fazer a sua inserção neste tipo de mercados; Por último são descritas as principais conclusões retiradas na realização da dissertação. Em suma, este trabalho reúne um conjunto de dados e orientações úteis, decorrentes do estudo realizado de auxílio a empresas a aderirem aos mercados eletrónicos, visto ser uma abordagem promissora para as organizações.