Evaluation of tools and cutting conditions on carbon fibre reinforced laminates

The distinctive characteristics of carbon fibre reinforced plastics, like low weight or high specific strength, had broadened their use to new fields. Due to the need of assembly to structures, machining operations like drilling are frequent. In result of composites inhomogeneity, this operation can lead to different damages that reduce mechanical strength of the parts in the connection area. From these damages, delamination is the most severe. A proper choice of tool and cutting parameters can reduce delamination substantially. In this work the results obtained with five different tool geometries are compared. Conclusions show that the choice of an adequate drill can reduce thrust forces, thus delamination damage.

Drilling damage in composite material

The characteristics of carbon fibre reinforced laminates have widened their use from aerospace to domestic appliances, and new possibilities for their usage emerge almost daily. In many of the possible applications, the laminates need to be drilled for assembly purposes. It is known that a drilling process that reduces the drill thrust force can decrease the risk of delamination. In this work, damage assessment methods based on data extracted from radiographic images are compared and correlated with mechanical test results—bearing test and delamination onset test—and analytical models. The results demonstrate the importance of an adequate selection of drilling tools and machining parameters to extend the life cycle of these laminates as a consequence of enhanced reliability.

Estudo de aplicação de coletores solares na produção de eletricidade e calor para uso doméstico

O trabalho apresentado explora o aproveitamento da energia solar para suprir as necessidades energéticas, tanto elétricas como térmicas, de uma habitação tipo 3, recorrendo ao uso de coletores solares, em que o fluido de trabalho é água. As necessidades elétricas serão supridas através da produção de energia com recurso a um ciclo de Rankine, em que fluido de trabalho é um frigorigénio aquecido através de um permutador cujo fluido quente será uma mistura, de água com anticongelante, aquecida pelo coletor solar. Por outro lado, as necessidades térmicas serão satisfeitas através do calor libertado no ciclo de Rankine. Na instalação solar estará integrado um acumulador térmico com apoio energético, caldeira elétrica, que funcionará nas situações em que coletor seja insuficiente para satisfazer as necessidades térmicas. No final será feita uma avaliação económica para que possa atestar a viabilidade económica do projeto, tendo em conta os seus custos totais versus as poupanças energéticas que este sistema origina.

Levantamento elétrico e análise de equipamento para acabamento térmico e químico de telas para pneus

Esta dissertação surge da necessidade da empresa Continental - Indústria Têxtil do Ave S.A. – grupo Continental Mabor atualizar os esquemas elétricos de uma máquina para acabamento térmico e químico de telas para pneus. Esta máquina (Zell) é crucial no seu processo produtivo e foi ao longo dos anos sofrendo sucessivas atualizações no sentido de melhorar o seu processo de produção. Existiam já, esquemas elétricos da máquina realizados em EPLAN no entanto não foram acompanhando as sucessivas alterações sofridas pela mesma. Deste modo, torna-se assim necessário não só produzir um suporte digital desses mesmos esquemas como também proceder à sua atualização. Ao longo desta dissertação o leitor pode obter informações relativas à Zell e ao processo de análise, estudo e documentação dos seus quadros elétricos em software EPLAN Electric P8. São ainda abordadas técnicas de leitura e desenho em EPLAN e descritas algumas das funcionalidade que este permite. É abordada a base de dados criada com informações relativas à máquina tais como características dos seus motores, variadores de frequência e localização dos quadros elétricos ao longo dos pisos que a compõe. No âmbito desta dissertação foi redigido o “Manual da Máquina de Impregnar Zell” e neste relatório abordam-se os principais tópicos que o compõe. Com a atualização dos esquemas tornou-se necessário proceder à identificação de diversos componentes na máquina. Esse processo de identificação é também aqui retratado. O leitor pode por fim obter informações de como realizar um processo de troca de variadores de frequência da marca ABB de diferentes gamas tendo em consideração as ligações em causa para o fazer. Pretende-se assim que o leitor fique com uma noção geral das diferentes vertentes que foram abordadas neste trabalho realizado sob a forma de um estágio com duração de seis meses no seio desta indústria.

Soluções de melhoria de desempenho térmico na reabilitação de edifícios

O presente relatório pretende descrever a atividade desenvolvida durante o estágio efetuado na empresa Porto Vivo, SRU - Sociedade de Reabilitação Urbana da Baixa Portuense, SA, à qual se encontra inserida num tema importantíssimo nos dias de hoje: a Reabilitação Urbana. Irá ainda ser dada uma ênfase especial a um caso de estudo relativo ao comportamento térmico de um edifício em reabilitação no Centro Histórico do Porto promovido pela mesma empresa. Este estágio que decorreu desde o dia 3 de Dezembro até 3 de Junho, teve em vista a conclusão do Mestrado em Engenharia Civil, na área das Construções, no Instituto Superior de Engenharia do Porto e foi realizado no âmbito da disciplina de DIPRE. Neste documento, procurar-se-á descrever, com detalhe e objetividade, toda a informação necessária para dar a conhecer o trabalho desenvolvido ao longo do estágio. Procurar-se-á igualmente abordar o tema da Reabilitação Urbana, o funcionamento da própria empresa, Porto Vivo, SRU e ainda expor as principais implicações ao nível das soluções construtivas exigidas pelo estudo do comportamento térmico de edifícios a reabilitar.

Implementação de plataforma web e android no âmbito da associação Anddvis

Trabalho de Projeto apresentado ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Marketing Digital, sob orientação do Mestre Paulo Gonçalves

Impostos diferidos: uma análise à sua contabilização mediante a dimensão das empresas

Dissertação de Mestrado Apresentado ao Instituto Superior de Contabilidade e Administração do Porto para obtenção do grau de Mestre em Contabilidade e Finanças, sob a orientação de: Orientador: Doutor José Campos Amorim Coorientadora: Doutora Albertina Paula Monteiro

Day-ahead Resource Scheduling Including Demand Response for Electric Vehicles

The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs i n the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Othe r important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs.

A Hybrid Simulated Annealing approach to handle Energy Resource Management considering an intensive use of Electric Vehicles

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed generators, storage units, demand response and EVs. The large number of resources causes more complexity in the energy resource management, taking several hours to reach the optimal solution which requires a quick solution for the next day. Therefore, it is necessary to use adequate optimization techniques to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA version, resulting in a cost reduction of 1.94%. For this scenario, the proposed approach is approximately 94 times faster than the deterministic approach.

Distributed Generation and Demand Response Dispatch for a Virtual Power Player Energy and Reserve Provision

Recent changes in the operation and planning of power systems have been motivated by the introduction of Distributed Generation (DG) and Demand Response (DR) in the competitive electricity markets' environment, with deep concerns at the efficiency level. In this context, grid operators, market operators, utilities and consumers must adopt strategies and methods to take full advantage of demand response and distributed generation. This requires that all the involved players consider all the market opportunities, as the case of energy and reserve components of electricity markets. The present paper proposes a methodology which considers the joint dispatch of demand response and distributed generation in the context of a distribution network operated by a virtual power player. The resources' participation can be performed in both energy and reserve contexts. This methodology contemplates the probability of actually using the reserve and the distribution network constraints. Its application is illustrated in this paper using a 32-bus distribution network with 66 DG units and 218 consumers classified into 6 types of consumers.

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

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.