Characterizing the timing behaviour of power-line communication by means of simulation

Although power-line communication (PLC) is not a new technology, its use to support communication with timing requirements is still the focus of ongoing research. Recently, a new infrastructure was presented, intended for communication using power lines from a central location to geographically dispersed nodes using inexpensive devices. This new infrastructure uses a two-level hierarchical power-line system, together with an IP-based network. Within this infrastructure, in order to provide end-toend communication through the two levels of the powerline system, it is necessary to fully understand the behaviour of the underlying network layers. The masterslave behaviour of the PLC MAC, together with the inherent dynamic topology of power-line networks are important issues that must be fully characterised. Therefore, in this paper we present a simulation model which is being used to study and characterise the behaviour of power-line communication.

A power line communication stack for metering, SCADA and large-scale domotic applications

This paper describes the communication stack of the REMPLI system: a structure using power-lines and IPbased networks for communication, for data acquisition and control of energy distribution and consumption. It is furthermore prepared to use alternative communication media like GSM or analog modem connections. The REMPLI system provides communication service for existing applications, namely automated meter reading, energy billing and domotic applications. The communication stack, consisting of physical, network, transport, and application layer is described as well as the communication services provided by the system. We show how the peculiarities of the power-line communication influence the design of the communication stack, by introducing requirements to efficiently use the limited bandwidth, optimize traffic and implement fair use of the communication medium for the extensive communication partners.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Analyzing objects in images for estimating the delamination influence on load carrying capacity of composite laminates

The use of fiber reinforced plastics has increased in the last decades due to their unique properties. Advantages of their use are related with low weight, high strength and stiffness. Drilling of composite plates can be carried out in conventional machinery with some adaptations. However, the presence of typical defects like delamination can affect mechanical properties of produced parts. In this paper delamination influence in bearing stress of drilled hybrid carbon+glass/epoxy quasi-isotropic plates is studied by using image processing and analysis techniques. Results from bearing test show that damage minimization is an important mean to improve mechanical properties of the joint area of the plate. The appropriateness of the image processing and analysis techniques used in the measurement of the damaged area is demonstrated.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

On-line defragmentation for run-time partially reconfigurable FPGAs

Dynamically reconfigurable systems have benefited from a new class of FPGAs recently introduced into the market, which allow partial and dynamic reconfiguration at run-time, enabling multiple independent functions from different applications to share the same device, swapping resources as needed. When the sequence of tasks to be performed is not predictable, resource allocation decisions have to be made on-line, fragmenting the FPGA logic space. A rearrangement may be necessary to get enough contiguous space to efficiently implement incoming functions, to avoid spreading their components and, as a result, degrading their performance. This paper presents a novel active replication mechanism for configurable logic blocks (CLBs), able to implement on-line rearrangements, defragmenting the available FPGA resources without disturbing those functions that are currently running.

Análise de fadiga de uma ponte ferroviária com tabuleiro misto vigado sob ação de tráfego de mercadorias

A presente dissertação centra-se no estudo de fadiga de uma ponte ferroviária com tabuleiro misto vigado pertencente a uma via de transporte de mercadorias. O caso de estudo incide sobre a ponte ferroviária sobre o rio do Sonho, localizada na Estrada de Ferro de Carajás situada no nordeste do Brasil. Nesta linha circulam alguns dos maiores comboios de mercadoria do mundo com cerca de 3.7 km de extensão e com cargas por eixo superiores a 300 kN. Numa primeira fase apresentam-se diversas metodologias de análise da fadiga em pontes ferroviárias metálicas. É também descrita a ferramenta computacional FADBridge, desenvolvida em ambiente MATLAB, e que possibilita o cálculo sistematizado e eficiente do dano de fadiga em detalhes construtivos de acordo com as indicações dos eurocódigos. Em seguida são abordadas as metodologias numéricas utilizadas para a realização das análises dinâmicas do sistema ponte-comboio e os aspetos regulamentares a ter em consideração no dimensionamento de pontes ferroviárias. O modelo numérico de elementos finitos da ponte foi realizado com recurso ao programa ANSYS. Com base neste modelo foram obtidos os parâmetros modais, nomeadamente as frequências naturais e os modos de vibração, tendo sido também analisada a importância do efeito compósito via-tabuleiro e a influência do comportamento não linear do balastro. O estudo do comportamento dinâmico da ponte foi realizado por intermédio de uma metodologia de cargas móveis através da ferramenta computacional Train-Bridge Interaction (TBI). As análises dinâmicas foram efetuadas para a passagem dos comboios reais de mercadorias e de passageiros e para os comboios de fadiga regulamentares. Nestas análises foi estudada a influência dos modos de vibração globais e locais, das configurações de carga dos comboios e do aumento da velocidade de circulação, na resposta dinâmica da ponte. Por último, foi avaliado o comportamento à fadiga de diversos detalhes construtivos para os cenários de tráfego regulamentar e reais. Foi ainda analisada a influência do aumento da velocidade, da configuração de cargas dos comboios e da degradação da estrutura nos valores do dano por fadiga e da respetiva vida residual.

Dynamic load management in a smart home to participate in demand response events

In future power systems, in the smart grid and microgrids operation paradigms, consumers can be seen as an energy resource with decentralized and autonomous decisions in the energy management. It is expected that each consumer will manage not only the loads, but also small generation units, heating systems, storage systems, and electric vehicles. Each consumer can participate in different demand response events promoted by system operators or aggregation entities. This paper proposes an innovative method to manage the appliances on a house during a demand response event. The main contribution of this work is to include time constraints in resources management, and the context evaluation in order to ensure the required comfort levels. The dynamic resources management methodology allows a better resources’ management in a demand response event, mainly the ones of long duration, by changing the priorities of loads during the event. A case study with two scenarios is presented considering a demand response with 30 min duration, and another with 240 min (4 h). In both simulations, the demand response event proposes the power consumption reduction during the event. A total of 18 loads are used, including real and virtual ones, controlled by the presented house management system.

O papel dos sistemas de Non-Intrusive Load Monitoring para a eficiência energética do setor residencial

A sustentabilidade energética do planeta é uma preocupação corrente e, neste sentido, a eficiência energética afigura-se como sendo essencial para a redução do consumo em todos os setores de atividade. No que diz respeito ao setor residencial, o indevido comportamento dos utilizadores aliado ao desconhecimento do consumo dos diversos aparelhos, são factores impeditivos para a redução do consumo energético. Uma ferramenta importante, neste sentido, é a monitorização de consumos nomeadamente a monitorização não intrusiva, que apresenta vantagens económicas relativamente à monitorização intrusiva, embora levante alguns desafios na desagregação de cargas. Abordou-se então, neste documento, a temática da monitorização não intrusiva onde se desenvolveu uma ferramenta de desagregação de cargas residenciais, sobretudo de aparelhos que apresentavam elevados consumos. Para isso, monitorizaram-se os consumos agregados de energia elétrica, água e gás de seis habitações do município de Vila Nova de Gaia. Através da incorporação dos vetores de água e gás, a acrescentar ao da energia elétrica, provou-se que a performance do algoritmo de desagregação de aparelhos poderá aumentar, no caso de aparelhos que utilizem simultaneamente energia elétrica e água ou energia elétrica e gás. A eficiência energética é também parte constituinte deste trabalho e, para tal, implementaram-se medidas de eficiência energética para uma das habitações em estudo, de forma a concluir as que exibiam maior potencial de poupança, assim como rápidos períodos de retorno de investimento. De um modo geral, os objetivos propostos foram alcançados e espera-se que num futuro próximo, a monitorização de consumos não intrusiva se apresente como uma solução de referência no que respeita à sustentabilidade energética do setor residencial.

A comunicação on-line das ONGs e a angariação de fundos

Dissertação de Mestrado apresentado ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação da Mestre Inês Veiga Pereira “Esta versão contém as críticas e sugestões dos elementos do júri”

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Contextual Intelligent Load Management Considering Real-Time Pricing in a Smart Grid Environment

The use of demand response programs enables the adequate use of resources of small and medium players, bringing high benefits to the smart grid, and increasing its efficiency. One of the difficulties to proceed with this paradigm is the lack of intelligence in the management of small and medium size players. In order to make demand response programs a feasible solution, it is essential that small and medium players have an efficient energy management and a fair optimization mechanism to decrease the consumption without heavy loss of comfort, making it acceptable for the users. This paper addresses the application of real-time pricing in a house that uses an intelligent optimization module involving artificial neural networks.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.