Thermoelectric (TE) module integration into a notebook computer is experimentally investigated in this thesis for its energy harvesting opportunities. A detailed Finite Element (FE) model was constructed first for thermal simulations. The model outputs were then correlated with the thermal validation results of the selected system. In parallel, a commercial TE micro-module was experimentally characterized to quantify maximum power generation opportunity from the combined system and component data set. Next, suitable &ldquo / warm spots&rdquo / were identified within the mobile computer to extract TE power with minimum or no notable impact to system performance, as measured by thermal changes in the system, in order to avoid unacceptable performance degradation. The prediction was validated by integrating a TE micro-module to the mobile system under test. Measured TE power generation power density in the carefully selected vicinity of the heat pipe was around 1.26 mW/cm3 with high CPU load. The generated power scales down with lower CPU activity and scales up in proportion to the utilized opportunistic space within the system. The technical feasibility of TE energy harvesting in mobile computers was hence experimentally shown for the first time in this thesis.

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[pt] Os grandes agentes do mercado de energia dedicam muitos esforços na avaliação e decisão da alocação ótima de capital para a implementação de projetos, em decorrência do grande número de projetos candidatos em seus portfólios de investimentos. Essas decisões visam escolher o subconjunto de projetos a ser implementado, pois os recursos orçamentários são geralmente menores que o necessário para a implementação de todos eles. Muitos são os riscos apresentados, e quanto mais riscos e incertezas, maiores se tornam as dificuldades de avaliação e decisões de investimento de maneira otimizada. As metodologias clássicas para avaliação de portfólios de projetos de investimento são baseadas em maximizar os retornos (VPL, TIR, etc) e minimizar o risco (desvio-padrão do VPL, variância, etc). Muitas vezes, estes métodos tradicionais de avaliação podem não conseguir tratar adequadamente as flexibilidades gerenciais (Opções Reais) características dos projetos, assim como os riscos e incertezas, devido às possíveis dificuldades de solução e modelagem matemática (multi-variáveis) dos problemas. O desenvolvimento e aplicação de modelos alternativos, tais como os baseados na Teoria de Opções Reais, inclusive com a utilização de métodos de Inteligência Computacional, podem se mostrar mais adequados para estes problemas. Nesta tese é desenvolvida uma metodologia híbrida, apresentando um modelo de Opções Reais Fuzzy para a avaliação de projetos de Revamp por um agente do mercado de Geração Termelétrica de Energia, a partir de um Portfólio de Opções Reais em ambiente de incertezas. Para a seleção do subconjunto de projetos por faixa orçamentária, é aplicado um Algoritmo Genético para otimização multi-critério, através da utilização de um índice de ponderação retorno x risco (lâmbda). / [en] Large players in energy market dedicate many efforts in valuation and optimal capital allocation decision for their project implementation, due the large candidate projects number in their investment portfolios. These decisions aim to choose the projects subset to be implemented, because the monetary resources are generally smaller than necessary for all projects implementation. There are many risks, and with risks and uncertainties, greater become the difficulties in analysis and optimally investment decisions. The classical methods to investment portfolios are based on to maximize returns (NPV, IRR, among others) and to minimize risks (NPV standard deviation, variance, among others). Often, these traditional methods may not be able to handle properly the projects managerial flexibilities (Real Options), as well the risks and uncertainties, due to possible solution difficulties and mathematical modeling problems (multi variables). Alternative models development and implementation, such as those based on Real Options Theory, including the use of Computational Intelligence methods, may be more suitable for these problems. In this thesis, a hybrid methodology is developed, presenting a Fuzzy Real Options model for Revamp projects valuation by a Thermoelectric Power Generation market player, from a Real Options Portfolio in uncertainties environment. For selecting the projects subset by budget range, a multi-criteria Genetic Algorithm optimization is applied, using a weighting return x risk index (lambda).

The growing need of energy worldwide has lead to an increasing demand for alternative sources of power generation. Thermoelectric materials are one of the ‘green energy sources’ which convert directly heat into electricity, and vice–versa. The efficiency of this conversion is dependent on ‘figure of merit’ (z T), which depends on the material’s Seebeck coefficient (S), electrical resistivity (ρ) and thermal conductivity (κ) through the relation z T=S2T/ρκ, where T is the temperature. High values of z T lead to high efficiency, and therefore, z T must be maximized. Lead telluride is well–established thermoelectric material in the temperature range 350 K and 850 K. The aim of this thesis is to improve the z T of the material by adopting two different approaches – (i) doping/alloying and (ii) introducing additional interfaces in bulk i.e. having two phase PbTe. In this thesis, first an introduction about the thermoelectric phenomenon is given, along with the material parameters on which z T depends. A survey of literature associated with PbTe is done and the current status of thermoelectric devices is summarized briefly. This is followed by a description of the synthesis procedure and the measurement techniques adopted in this work. The first approach is the conventional alloying and doping of the material by which carrier concentration of the material is controlled so that maximum power factor Sρ2 is achieved and a simultaneous reduction of thermal conductivity takes place by mass fluctuation scattering. Under this, two systems have been studied. The first system is PbTe1−ySey alloys doped with In (nominal composition: Pb0.999In0.001Te1−ySey, y=0.01, 0.05, 0.10, 0.20, 0.25, 0.30). The compounds were single phase and polycrystalline. Lattice constants obtained from Rietveld refinement of X–ray diffraction (XRD) data showed that Vegard’s law was followed, indicating solid solution formation between PbTe and PbSe. Compositional analysis showed lower indium content than the nominal composition. Temperature dependent Seebeck coefficient showed all the samples to be n–type while Pisarenko plots showed that indium did not act as a resonant dopant. Electrical resistivity increased with temperature, while mobility vs T fitting showed a mixed scattering mechanism of acoustic phonon and ionized impurity scattering. Thermal conductivity followed a T1 dependence, which indicated acoustic phonon scattering. At high temperature, slight bipolar effect was observed, which showed the importance of control-ling carrier concentration for good thermoelectric properties. A z T of 0.66 was achieved at 800 K. The second alloy studied under this approach was Mn doped Pb1−ySnyTe alloy (nominal composition Pb0.96−yMn0.04SnyTe (y=0.56, 0.64, 0.72, 0.80)). All the samples followed Vegard’s law, showing formation of complete solid solution between PbTe and SnTe. Microstructure analysis showed grain size distribution of <1 µm to more than 10 µm. Seebeck coefficient showed all samples were p-type and the role of two valence band conduction in p–type PbTe based materials. Electrical resistivity showed a de-crease possibly due to (i) large carrier concentration or (ii) increased mobility due to Mn2+ ions. Thermal conductivity decreased systematically with decreasing Sn content. Bipolar effect was observed at high temperatures. Accordingly, the highest z T of 0.82 at 720 K was obtained for the sample with Sn (y=0.56) content due to optimum carrier concentration and maximum disorder. The second approach of having additional interfaces in bulk focuses on reducing thermal conductivity by scattering phonons. Under this approach, three systems were studied. The first system is PbTe with bismuth (Bi) secondary phase. The XRD and Ra-man studies showed that bismuth was not a dopant in PbTe, while micrographs showed micrometer–sized Bi secondary phase dispersed in bulk of PbTe. Reduction in Seebeck coefficient showed possible hole donation across PbTe–Bi interfaces, while electrical re-sistivity and thermal conductivity showed that the role of electrons at the interfaces was more important than phonons for the present bismuth concentrations. For the parent PbTe, z T of 0.8 at 725 K was reached, which, however decreased for bismuth added samples. The second system studied under the two phase approach was indium (In) added PbTe. Indium was not found to act as dopant in PbTe, while micrometer sized indium phase was found in PbTe bulk. A decrease in the electronic thermal conductivity ac-companied by a simultaneous increase of the electrical resistivity and Seebeck coefficient throughout the measurement range indicated increased scattering of electrons at PbTe-In interfaces. Higher values of the lattice thermal conductivity showed that the PbTe–In interfaces were ineffective at scattering phonons, which was initially expected due to the lattice mismatch between PbTe and In. For PbTe with 3 at. % In phase, z T value of 0.78 at 723 K was achieved. Under the two phase approach, as a comparative study, PbTe with both micrometer sized Bi and In phases together was prepared, in which no improvement in z T was found. A comparison of both the approaches showed that the alloying approach is better than the two–phase approach. This is because micrometer sized secondary phase scatter the electrons more than the phonons, leading to adverse effect on the transport coef-ficients, and hence, on z T. Alloying, on the other hand, is more beneficial in reducing thermal conductivity by mass fluctuation scattering, along with a simultaneous reduction of electrical resistivity.