Decision Support Concerning Demand Response Programs Design and Use – A Conceptual Framework and Simulation Tool

Demand response can play a very relevant role in the context of power systems with an intensive use of distributed energy resources, from which renewable intermittent sources are a significant part. More active consumers participation can help improving the system reliability and decrease or defer the required investments. Demand response adequate use and management is even more important in competitive electricity markets. However, experience shows difficulties to make demand response be adequately used in this context, showing the need of research work in this area. The most important difficulties seem to be caused by inadequate business models and by inadequate demand response programs management. This paper contributes to developing methodologies and a computational infrastructure able to provide the involved players with adequate decision support on demand response programs and contracts design and use. The presented work uses DemSi, a demand response simulator that has been developed by the authors to simulate demand response actions and programs, which includes realistic power system simulation. It includes an optimization module for the application of demand response programs and contracts using deterministic and metaheuristic approaches. The proposed methodology is an important improvement in the simulator while providing adequate tools for demand response programs adoption by the involved players. A machine learning method based on clustering and classification techniques, resulting in a rule base concerning DR programs and contracts use, is also used. A case study concerning the use of demand response in an incident situation is presented.

Comparison of contaminant removal effectiveness and air change efficiency as indicator of air diffusion quality.

The ventilation efficiency concept is an attempt to quantify a parameter that can easily distinguish the different options for air diffusion in the building spaces. Thirteen strategies of air diffusion were measured in a test chamber through the application of the tracer gas method, with the objective to validate the calculation by Computational fluid dynamics (CFD). Were compared the Air Change Efficiency (ACE) and the Contaminant Removal Effectiveness (CRE), the two indicators most internationally accepted. The main results from this work shows that the values of the numerical simulations are in good agreement with experimental measurements and also, that the solutions to be adopted for maximizing the ventilation efficiency should be the schemes that operate with low speeds of supply air and small differences between supply air temperature and the room temperature.

Levels and risks of particulate-bound PAHs in indoor air influenced by tobacco smoke: a field measurement

Considering tobacco smoke as one of the most health-relevant indoor sources, the aim of this work was to further understand its negative impacts on human health. The specific objectives of this work were to evaluate the levels of particulate-bound PAHs in smoking and non-smoking homes and to assess the risks associated with inhalation exposure to these compounds. The developed work concerned the application of the toxicity equivalency factors approach (including the estimation of the lifetime lung cancer risks, WHO) and the methodology established by USEPA (considering three different age categories) to 18 PAHs detected in inhalable (PM10) and fine (PM2.5) particles at two homes. The total concentrations of 18 PAHs (ΣPAHs) was 17.1 and 16.6 ng m−3 in PM10 and PM2.5 at smoking home and 7.60 and 7.16 ng m−3 in PM10 and PM2.5 at non-smoking one. Compounds with five and six rings composed the majority of the particulate PAHs content (i.e., 73 and 78 % of ΣPAHs at the smoking and non-smoking home, respectively). Target carcinogenic risks exceeded USEPA health-based guideline at smoking home for 2 different age categories. Estimated values of lifetime lung cancer risks largely exceeded (68–200 times) the health-based guideline levels at both homes thus demonstrating that long-term exposure to PAHs at the respective levels would eventually cause risk of developing cancer. The high determined values of cancer risks in the absence of smoking were probably caused by contribution of PAHs from outdoor sources.

Ultrafine Particles in Ambient Air of an Urban Area: Dose Implications for Elderly

Due to their detrimental effects on human health, the scientific interest in ultrafine particles (UFP) has been increasing, but available information is far from comprehensive. Compared to the remaining population, the elderly are potentially highly susceptible to the effects of outdoor air pollution. Thus, this study aimed to (1) determine the levels of outdoor pollutants in an urban area with emphasis on UFP concentrations and (2) estimate the respective dose rates of exposure for elderly populations. UFP were continuously measured over 3 weeks at 3 sites in north Portugal: 2 urban (U1 and U2) and 1 rural used as reference (R1). Meteorological parameters and outdoor pollutants including particulate matter (PM10), ozone (O3), nitric oxide (NO), and nitrogen dioxide (NO2) were also measured. The dose rates of inhalation exposure to UFP were estimated for three different elderly age categories: 64–70, 71–80, and >81 years. Over the sampling period levels of PM10, O3 and NO2 were in compliance with European legislation. Mean UFP were 1.7 × 104 and 1.2 × 104 particles/cm3 at U1 and U2, respectively, whereas at rural site levels were 20–70% lower (mean of 1 ×104 particles/cm3). Vehicular traffic and local emissions were the predominant identified sources of UFP at urban sites. In addition, results of correlation analysis showed that UFP were meteorologically dependent. Exposure dose rates were 1.2- to 1.4-fold higher at urban than reference sites with the highest levels noted for adults at 71–80 yr, attributed mainly to higher inhalation rates.

Virtual Communities of Practice: Design and Development of Support Platforms and Participation Fostering

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

A atividade dos estabilizadores da omoplata e movimento compensatório do tronco no gesto de alcance em indivíduos pós AVE

Introdução: Lesões como o AVE interferem com a capacidade de recrutar níveis adequados de atividade muscular, podendo levar ao aparecimento de movimentos compensatórios como a excessiva translação anterior do tronco, associada ao gesto de alcance. Objetivos: Descrever a relação entre a atividade dos estabilizadores da omoplata e o movimento compensatório do tronco no gesto de alcance, em 4 indivíduos pós AVE. Pretendeu-se também analisar o papel dos estabilizadores da omoplata na função do membro superior. Métodos: Quatro indivíduos com diagnóstico de AVE, que apresentavam alterações no nível de actividade dos estabilizadores da omoplata contralesional, foram sujeitos a uma avaliação realizada em três momentos, antes (M0), durante (M1) e após (M2) e a um período de intervenção, segundo os princípios do Conceito de Bobath. Recorreu-se à electromiografia de superfície para avaliar a atividade e o timming dos músculos grande dorsal, trapézio superior e trapézio inferior do hemicorpo contralesional e ao software de Avaliação Postural (SAPO) para analisar o deslocamento do tronco no sentido anterior, associados à realização do gesto de alcance. Foram aplicadas as escalas RPS e MESUPES para avaliar as componentes de movimento do gesto de alcance e a função do membro superior, respetivamente. Recorreu-se ao registo fotográfico para análise dos componentes de movimento na posição de sentado e em pé.Resultados: Os dados eletromiográficos registam atividade dos estabilizadores da omoplata unicamente num indivíduo em M2. A análise do deslocamento anterior do tronco revela melhorias em M1 em todos os indivíduos, sendo que em M2 essa evolução positiva não foi observada em três dos participantes. Entre M0 e M2, na escala RPS registam-se melhorias de 7 a 9 pontos no alvo próximo e de 5 a 10 pontos no alvo distante. Na escala MESUPES verificam-se melhorias entre 5 a 18 pontos na sub-escala braço e entre 5 a 8 pontos na sub-escala mão, em M2. A avaliação do registo fotográfico revela modificações nos componentes de movimento dos quatro indivíduos, nomeadamente na integração dos MI na base de suporte, na atividade do tronco inferior e superior e no alinhamento do MS contralesional. Conclusão: A melhoria do nível da atividade dos estabilizadores dinâmicos da omoplata sugere ter influência na diminuição do movimento compensatório do tronco no gesto de alcance e parece ter um papel na melhoria da eficácia distal do MS do mesmo lado.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Topics regarding access to european information institutions: European Union so close and yet so far

From the 1990s, the Parliament, the Council and the European Commission adopted a new approach to disclosure of their working papers. Legal instruments to regulate and allow a fairly broad access to internal working documents of these institutions were created. European institutions also exploited the potential of Information and Communication Technologies, developing new instruments to register the documents produced and make them accessible to the public. The commitment to transparency sought to shows a more credible European government, and reduces the democratic deficit. However, the data analysis regarding access to EU institutions documents shows that general public is still far from direct contact with European bodies.

Short-term Wind Forecasting to Support Virtual Power Player Operation

This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Man-agement in a distribution power network, with intensive use of wind based power generation. The proposed fore-casting methodology aims to support the operation in the scope of the intraday resources scheduling model, name-ly with a time horizon of 10 minutes. A case study using a real database from the meteoro-logical station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evalu-ated and compared with a previous developed forecast-ing model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind short-term forecasting.

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.

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.