Abstract

Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia A eletrificação do setor dos transportes irá desempenhar um papel fundamental no futuro de cidades mais inteligentes e mais sustentáveis. Devido à crescente adoção de veículos elétricos (EVs), se o carregamento não for controlado, poderão surgir eventuais problemas na rede elétrica, tal como sobretensões levando a perdas de energia. Assim, torna-se necessário a criação de estratégias de carregamento mais inteligentes.Esta dissertação apresenta um algoritmo inteligente de controlo de carregamento (SCMA) que simula o carregamento de um EV, numa residência, de acordo com as preferências do utilizador, bem como as da rede. Este algoritmo permite ao utilizador poupanças de energia e beneficiam, simultaneamente, a rede elétrica e o ambiente. Para o seu funcionamento, considera-se que a residência em causa tem uma tarifa energética variável no tempo, de hora em hora. Assume-se também que os dados da variação do preço da eletricidade, da previsão de procura e da geração energia renovável são comunicados ao SCMA.Os SCMA foram simulados de acordo com três objetivos: minimizar o custo de carregamento, minimizar o impacto ambiental e suavizar o diagrama de carga. Par tal, os algoritmos priorizam ou os intervalos de tempo com menor custo de eletricidade, aqueles com maior percentagem de energia renovável, ou ainda os que apresentem maior disponibilidade de potência. Depois, os algoritmos foram simulados para vários casos de estudo.Por último, os cenários de carregamento otimizado foram comparados entre eles e com um caso de carregamento não otimizado. O SCMA foi bem sucedido na otimização do carregamento do EV sob todos os objetivos. The transport electrification will play an important role in future smarter and more sustainable cities. Due to the increasing adoption of electric vehicles (EVs), if the charging cycles are not managed, a number of grid related issues, such as overload, could arise, which can lead to power outages. Thus, smarter charging strategies are required.This dissertation presents smart charging management algorithm (SCMA) that simulate the charging of the EV according to the user’s preferences, as well as the grid’s requirements. Such algorithm allows further energy savings to the consumer, whilst benefiting the grid and the environment. It is considered that the residential dwelling is operating under time-varying electricity prices, that change on an hourly basis. This work assumes that electricity pricing variations, the load forecast and the renewable energy generation data are provided to the SCMA.The SCMA was simulated under three objectives: to minimize the charging cost, minimize the environmental impact and to flatten the load profile. In order to do this, the algorithms either prioritize the time slots with the lowest electricity price, or the ones with higher RES share, or the ones with more available power, respectively. Then, the algorithms were tested for a series of case studies.Lastly, the optimized charging scenarios were compared among them and to an unmanaged charging scenario. The SCMA successfully optimized the EV’s charging cycles under any objective. - - - - - -

Abstract

In this study, a new cost model is developed for CO2 pipeline transport, which starts with the physical properties of CO2 transport and includes different kinds of steel grades and up-to-date material and construction costs. This pipeline cost model is used for a new developed tool to determine the configuration leading to the lowest levelized costs for CO2 transport, for point-to-point pipelines as well as for simple networks on different types of terrain and for different time frames. The model optimizes inlet pressure, diameter, steel grade and number of pumping stations. Results show that gaseous CO2 transport can give lower levelized costs than liquid CO2 transport for point-to-point pipelines and for simple networks, if the CO2 is stored in a reservoir with a low required injection pressure, like depleted natural gas fields. However, for storage fields with a required injection pressure of 8 MPa or higher (like aquifers), CO2 can be better transported in a liquid form. For onshore pipelines transporting liquid CO2, the optimal inlet pressure is 9-13 MPa and pumping stations are installed roughly every 50-100 km. For offshore pipelines, pumping stations are not an option and the inlet pressure is determined by the length of the pipeline. The maximum inlet pressure is about 25 MPa and for even longer pipelines, a larger diameter is selected. The levelized costs (excluding initial compression) for transporting 100 kg/s (about 3 Mt/y) over 100 km are between 1.8 and 33 (sic)/t for liquid and 4.0-6.4 (sic)/t for gaseous CO2 transport. For larger mass flows the costs are decreasing, for instance transporting 200 kg/s (about 6 Mt/y) over 100 km are 1.2-1.8 (sic)/t for liquid and 3.0-3.8 (sic)/t for gaseous CO2 transport. Furthermore, results show that higher steel grades lead to lower investment costs for onshore pipelines transporting liquid CO2. Using X120 in the long term reduces the pipeline costs up to 17%. For gaseous CO2 transport, lower steel grades (like X42 and X52) are the best option. Also offshore pipelines do not benefit from the development of higher steel grades over time because the thickness should be at least 2.5% of the outer diameter. The results indicate that oversizing the pipeline, to transport CO2 from an additional source that is coming available later, is not always cost-attractive. This strongly depends on the time span of which further CO2 sources are available and on the mass flows. Oversizing appears earlier cost-attractive compared to two point-to-point pipelines if the source with the largest mass flow becomes available first. (C) 2014 Elsevier Ltd. All rights reserved.