O serviço de referência - do tradicional ao virtual: o caso da Biblioteca do tribunal de Contas

Relatório de Estágio apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Ciências da Informação e da Documentação

Virtual Power Player Using Demand Response to Deal with Unexpected Low Wind Power Generation

Demand response is assumed as an essential resource to fully achieve the smart grids operating benefits, namely in the context of competitive markets and of the increasing use of renewable-based energy sources. Some advantages of Demand Response (DR) programs and of smart grids can only be achieved through the implementation of Real Time Pricing (RTP). The integration of the expected increasing amounts of distributed energy resources, as well as new players, requires new approaches for the changing operation of power systems. The methodology proposed in this paper aims the minimization of the operation costs in a distribution network operated by a virtual power player that manages the available energy resources focusing on hour ahead re-scheduling. When facing lower wind power generation than expected from day ahead forecast, demand response is used in order to minimize the impacts of such wind availability change. In this way, consumers actively participate in regulation up and spinning reserve ancillary services through demand response programs. Real time pricing is also applied. The proposed model is especially useful when actual and day ahead wind forecast differ significantly. Its application is illustrated in this paper implementing the characteristics of a real resources conditions scenario in a 33 bus distribution network with 32 consumers and 66 distributed generators.

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.

Maximizing the Social Welfare of Virtual Power Players Operation in Case of Excessive Wind Power

The integration of growing amounts of distributed generation in power systems, namely at distribution networks level, has been fostered by energy policies in several countries around the world, including in Europe. This intensive integration of distributed, non-dispatchable, and natural sources based generation (including wind power) has caused several changes in the operation and planning of power systems and of electricity markets. Sometimes the available non-dispatchable generation is higher than the demand. This generation must be used; otherwise it is wasted if not stored or used to supply additional demand. New policies and market rules, as well as new players, are needed in order to competitively integrate all the resources. The methodology proposed in this paper aims at the maximization of the social welfare in a distribution network operated by a virtual power player that aggregates and manages the available energy resources. When facing a situation of excessive non-dispatchable generation, including wind power, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. This method is especially useful when actual and day-ahead resources forecast differ significantly. The distribution network characteristics and concerns are addressed by including the network constraints in the optimization model. The proposed methodology has been implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20.310 consumers and 548 distributed generators, some of them non-dispatchable and with must take contracts. The implemented scenario corresponds to a real day in Portuguese power system.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Environmental management and mudit training at the New University of Lisbon by University-Industry co-operation

Environmental Training in Engineering Education (ENTREE 2001) - integrated green policies: progress for progress, p. 329-339 (Florence, 14-17 November 2001; proceedings published as book)

Intelligent Remuneration and Tariffs for Virtual Power Players

Power systems have been through deep changes in recent years, namely due to the operation of competitive electricity markets in the scope the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers) to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). 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. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

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.

Environmental impact assessment in higher education and training in Portugal

Journal of Cleaner Production, nº 16, p. 639-645

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.

Customisable e-training programmes based on trainees profiles

Dissertation presented at Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa to obtain the Master degree in Electrical and Computer Engineering

Is There Sufficient Training of Health Care Staff on Noise Reduction in Neonatal Intensive Care Units? a Pilot Study From NeoNoise Project

Evidence indicates that exposure to high levels of noise adversely affects human health, and these effects are dependent upon various factors. In hospitals, there are many sources of noise, and high levels exert an impact on patients and staff, increasing both recovery time and stress, respectively. The goal of this pilot study was to develop, implement and evaluate the effectiveness of a training program (TP) on noise reduction in a Neonatal Intensive Care Units (NICU) by comparing the noise levels before and after the implementation of the program. A total of 79 health professionals participated in the study. The measurements of sound pressure levels took into account the layout of the unit and location of the main sources of noise. General results indicated that LAeq levels before implementation of the training program were often excessive, ranging from 48.7 ± 2.94 dBA to 71.7 ± 4.74 dBA, exceeding international guidelines. Similarly following implementation of the training program noise levels remained unchanged (54.5 ± 0.49 dBA to 63.9 ± 4.37 dBA), despite a decrease in some locations. There was no significant difference before and after the implementation of TP. However a significant difference was found for Lp, Cpeak, before and after training staff, suggesting greater care by healthcare professionals performing their tasks. Even recognizing that a TP is quite important to change behaviors, this needs to be considered in a broader context to effectively control noise in the NICU.

Neuromarketing e o consumidor virtual

As neurociências aliadas ao marketing, constituem um novo paradigma com grande potencial, no que diz respeito ao conhecimento profundo do consumidor e do seu comportamento de compra: o Neuromarketing. O Neuromarketing tem uma forte componente científica que estuda e define fisiologicamente os mecanismos subjacentes à cognição, com foco específico nas bases neurais dos processos mentais e suas manifestações comportamentais e uma componente económica e social em que os Marketeers se questionam acerca dos métodos tradicionais para conhecer profundamente o seu cliente e aplicar em toda a sua potencialidade o marketing one-to-one, criar relações de fidelidade e evitar a falta de diferenciação que ainda se verifica em algumas empresas. Na óptica do consumidor este tema é ainda desconhecido e podemos afirmar com alguma certeza que também será um pouco assustador pensar que seja possível conhecer tão bem o nosso cérebro e a nossa maneira de pensar enquanto consumidores, que nos consigam “manipular” no momento da decisão de compra. O presente estudo tem como finalidade perceber o que pensa o consumidor desta nova área, o que sente em relação aos métodos usados em Neuromarketing e se já têm alguma percepção de que diariamente já são confrontados com técnicas de Neuromarketing e ainda, o que pensam delas.

Effects of dual-task training on balance and executive functions in Parkinson's disease: A pilot study

The aim of this study was to analyze the efficacy of cognitive-motor dual-task training compared with single-task training on balance and executive functions in individuals with Parkinson's disease. Fifteen subjects, aged between 39 and 75 years old, were randomly assigned to the dual-task training group (n = 8) and single-task training group (n = 7). The training was run twice a week for 6 weeks. The single-task group received balance training and the dual-task group performed cognitive tasks simultaneously with the balance training. There were no significant differences between the two groups at baseline. After the intervention, the results for mediolateral sway with eyes closed were significantly better for the dual-task group and anteroposterior sway with eyes closed was significantly better for the single-task group. The results suggest superior outcomes for the dual-task training compared to the single-task training for static postural control, except in anteroposterior sway with eyes closed.