Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

Highway agencies usually face budget uncertainty for pavement maintenance and rehabilitation activities due to limitation in resources and changes in government policies. Highway agencies perform maintenance planning for the pavement network commonly based on the nominal available budget without taking the variability of budget into consideration. The maintenance program based on deterministic budget consideration results in suboptimal maintenance decisions that impact the overall network conditions, if the budget falls short in some future year in the planning horizon. As a result, it is important for highway agencies to adopt maintenance and rehabilitation policies that are protected against the uncertainty in maintenance and rehabilitation budget. In this study a multi-period linear integer programming model is proposed with its robust counterpart considering uncertain maintenance and rehabilitation budget. The proposed model is able to provide a maintenance and rehabilitation program for the pavement network that results in minimal impact of budget variability on the network conditions. A case study was carried out for a network of ten pavement sections. The solution of the robust optimization model was compared to those with deterministic model. The results show that the robust optimization model is an attractive method that can minimize the effect of budget uncertainty on pavement conditions at the network level. / text

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Janet K. Allen; Committee Member: Benjamin Klein; Committee Member: Farrokh Mistree; Committee Member: Seung-Kyum Choi.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2008. / Includes bibliographical references (p. 57-59).

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Allen, Janet K.; Committee Member: Choi, Seung-Kyum; Committee Member: Mistree, Farrokh. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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As compared to short-term forecasting (e.g., 1 day), it is often challenging to accurately forecast the volume of precipitation in a medium-term horizon (e.g., 1 week). As a result, fluctuations in water inflow can trigger generation shortage and electricity price spikes in a power system with major or predominant hydro resources. In this paper, we study a two-stage robust scheduling approach for a hydrothermal power system. We consider water inflow uncertainty and employ a vector autoregressive (VAR) model to represent its seasonality and accordingly construct an uncertainty set in the robust optimization approach. We design a Benders' decomposition algorithm to solve this problem. Results are presented for the proposed approach on a real-world case study.

O setor sucroalcooleiro vem ganhando cada vez mais destaque no agronegócio brasileiro. O Produto Interno Bruto (PIB) do setor na safra de 2015 gerou mais de US$113 bilhões ao longo de toda cadeia produtiva (UNICA, 2016). Esse período de ascensão é acompanhado de novos desafios e oportunidades, o que torna o setor um tema fértil para a pesquisa acadêmica, teórica e aplicada. Dada à pluralidade do setor que hoje extravasa seu nicho tradicional, álcool e açúcar, e atua cada vez mais intensamente nos setores energéticos, eletricidade e combustíveis renováveis, faz-se necessário a incorporação da nova dinâmica de produção que esses produtos trazem à realidade administrativa do setor. Assim, além de discutir teoria e metodologia correlatas à modelagem matemática empregada no auxilio à gestão do setor, esse trabalho visa contribuir com a literatura, incorporando e discutindo as novas possibilidades produtivas que a produção de biogás trás ao mix de produção tradicional. As principais ferramentas utilizadas nessa análise são a teoria de portfólios e o arcabouço teórico da otimização robusta. A partir dessas técnicas construiu-se um modelo de otimização onde se busca a minimização do risco para um dado retorno, princípio da teoria de portfólios, avaliando o risco com o CVaR, uma medida de risco mais adequada do que a tradicional variância. Construído esse modelo, analisa-se o papel do biogás, um produto ainda pouco usual nas usinas brasileiras, no portfólio produtivo de uma usina hipotética. Com base nesse modelo implementou-se as técnicas de otimização robusta com o intuito de aferir se os resultados verificados no modelo determinístico se mantém no caso robusto. / The sugarcane sector is gaining a huge prominence in the Brazilian agribusiness. The GDP of the sector in 2015 crop was over then US$ 113 billion along the entire production chain (UNICA, 2016). This auspicious period is accompanied by new challenges and opportunities, which makes the sector a hot field for academic research, theoretical and applied. Given the industry plurality which today goes beyond its traditional niche, alcohol and sugar, the sector is increasingly strongly its share in the energy sector, electricity and renewable fuels. Thus it is necessary to incorporate the new dynamic of production that these products bring to the administrative reality of the sector. Therefore, in addition to discussing theory and methodology related to the mathematical modeling used as a support to sector management, this work aims to contribute to the literature by incorporating and discussing the new production possibilities that biogas production brings to the traditional production mix. The main tools used in this analysis are the portfolio theory and the theoretical and applied framework of robust optimization. From these techniques it is built up an optimization model where one seeks to minimize risk for a given return, the principle of portfolio theory, assessing the risk with CVaR, a better measure of risk than traditional variance. Through this model, the role of biogas, an unusual product in the Brazilian plants, is analyzed considering a hypothetical plant. Based on this model it is implemented robust optimization techniques in order to assess whether the results observed in the deterministic model remains in the case robust.

Este trabalho tem como objetivo avaliar a viabilidade da aplicação de técnicas de otimização robusta (OR) no planejamento da operação de reservatórios para geração de energia hidrelétrica. A OR é uma técnica de otimização que visa encontrar resultados que sejam menos sensíveis às incertezas nas variáveis do modelo através da minimização da variância da função objetivo para diferentes cenários. Desta forma foi desenvolvido um modelo de otimização robusta aplicado à operação de reservatórios para a geração de energia hidrelétrica, chamado HIDRO-OR, utilizando o software General Algebraic Modeling System (GAMS). Para estudo de caso foram utilizados os dados da UHE Sinop, a ser instalada no rio Teles Pires MT. Inicialmente foi realizada uma análise de sensibilidade utilizando diferentes combinações dos coeficientes de ponderação da função objetivo e três conjuntos de cenários. Nesta abordagem, o modelo resultou em vertimentos indesejados para realizar a diminuição do desvio padrão dos resultados entre os diferentes cenários. Uma solução encontrada para o problema foi realizar a otimização em duas etapas. Na primeira etapa ocorre a otimização robusta propriamente dita e são fixados os resultados para o primeiro mês de operação. Na segunda etapa, apenas a função objetivo principal é otimizada e, assim, são corrigidos os vertimentos indesejados. No entanto, com a otimização em duas etapas, não ocorreram mudanças na operação do reservatório para os diferentes coeficientes de ponderação. Ao final do trabalho conclui-se que, apesar dos resultados da análise de sensibilidade terem sido praticamente iguais com a otimização em duas etapas, estes podem ser considerados robustos pois são factíveis para todos os cenários. Por fim, são realizadas sugestões para a continuidade das pesquisas utilizando as técnicas de OR para a operação de usinas hidrelétricas. / This work aims to evaluate the feasibility of robust optimization techniques (OR) for reservoir management for hydropower production. The OR is an optimization technique which aims to find results that are less sensitive to the randomness of variables in the model by minimizing the variance of the objective function for different scenarios. One OR model was developed to the operation of reservoirs for hydropower production, called HYDRO-OR, using the software General Algebraic Modeling System (GAMS). As study case, data from the Sinop hydropower plant were used, which will be constructed in the Teles Pires river - MT. First, a sensitivity analysis was performed using different combinations of weigh coefficients of the objective function with three sets of scenarios. Preliminary results in this approach showed that the model resulted in unwanted spills to force the reduction of the standard deviation of the results from different scenarios. To correct this, the model was reconfigured to perform the optimization in two stages, the first one being the OR itself in which the results were obtained for the first month of planning. In the second step, the model was optimized again for subsequent months. In this case the model corrected the unnecessary spills but the results were quite similar for the three combinations of the weight coefficients. However the results can be considered robust because it is feasible for all scenarios. Finally, suggestions are made for further studies using the techniques of OR for the operation of hydropower plants.

Another important contribution of this thesis is that we propose another novel and highly robust estimator: Kernel Density Estimation Sample Consensus (KDESAC) which employs Random Sample Consensus algorithm combined with Kernel Density Estimation (KDE). The main advantage of KDESAC is that no prior information and no scale estimators are required in the estimation of the parameters. The computational load of KDESAC is much lower than the robust algorithms which estimate the scale in every sample loop. The experiments on synthetic data show that the proposed method is more robust to the heavily corrupted data than other algorithms. KDESAC can tolerate more than 80% outliers and multiple structures. Although Adaptive Scale Sample Consensus (ASSC) can obtain such good performance as KDESAC, ASSC is much slower than KDESAC. KDESAC is also applied to SFM problem and multi-motion estimation with real data. The experiments demonstrate that KDESAC is robust and efficient. / Structure from motion (SFM), the problem of estimating 3D structure from 2D images hereof, is one of the most popular and well studied problems within computer vision. This thesis is a study within the area of SFM. The main objective of this work is to improve the robustness of the SFM algorithm so as to make it capable of tolerating a great number of outliers in the correspondences. For improving the robustness, a stereo image sequence is processed, so the random sampling algorithms can be employed in the structure and motion estimation. With this strategy, we employ Random Sample Consensus (RANSAC) in motion and structure estimation to exclude outliers. Since the RANSAC method needs the prior information about the scale of the inliers, we proposed an auto-scale RANSAC algorithm which determines the inliers by analyzing the probability density of the residuals. The experimental results demonstrate that SFM by the proposed auto-scale RANSAC is more robust and accurate than that by RANSAC. / Chan Tai. / "September 2006." / Adviser: Yun Hui Liu. / Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1716. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 113-120). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.