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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
41

[en] FIRM ENERGY MONTHLY ALLOCATION OF SHPS IN SHP AND BIOMASS PORTFOLIOS / [pt] ESTRATÉGIAS DE SAZONALIZAÇÃO DA GARANTIA FÍSICA DE PCHS EM PORTFOLIOS PCH E BIOMASSA

FRANCISCO RALSTON FONSECA 14 July 2010 (has links)
[pt] A busca por uma matriz limpa de geração de energia vem incentivando a expansão de fontes alternativas de geração de energia ao redor do mundo. No Brasil, Pequenas Centrais Hidroelétricas (PCHs) e Usinas a Biomassa de Cana de Açúcar (Biomassa) vêm se mostrando alternativas atraentes nos últimos anos. No entanto, ambas as tecnologias são caracterizadas por perfis de geração sazonais (mas complementares). Este fato gera riscos que por muitas vezes inviabilizam a comercialização de maneira individual da energia produzida por essas usinas. As PCHs, em particular, têm uma opção de mitigação de parte desse risco participando do Mecanismo de Realocação de Energia (MRE). O MRE traz às PCHs a flexibilidade de sazonalizar sua Garantia Física ao longo do ano, o que se mostra uma ferramenta adicional para mitigar o risco da sazonalidade da geração hidráulica no Brasil. Neste trabalho, será estudado como a combinação de PCHs e Biomassas em um mesmo portfólio pode trazer ganhos sinérgicos para os Geradores. Em particular, será estudado como essa combinação altera a estratégia de sazonalização da Garantia Física da PCH participante do MRE e como essa sazonalização diferenciada resulta em benefícios para os geradores. Para isto, será proposto um modelo de otimização estocástica utilizado para simular o processo decisório de como sazonalizar a Garantia Física de PCHs combinadas com Biomassas em uma proporção fixa ou no contexto de otimização de portfólios compostos por estes dois tipos de usinas. Serão apresentados estudos de caso mostrando diferentes estratégias de comercialização de energia por parte destes Geradores e como a decisão de sazonalização da Garantia Física da PCH se comporta em cada um desses casos. / [en] The search for clean energy development has motivated the expansion of renewable sources of generation around the world. In Brazil, Small Hydro Plants (SHP) and Cogenaration from Sugarcane waste (Biomass) have proven themselves to be attractive alternatives during the last years. Nevertheless, both tecnologies have seazonal (yet complementary) availability. This fact results in financial risks that can make the commercialization of these plants energy individually too risky. SHPs have the option of mitigating their risk by joining the Energy Realocation Mecanism (ERM). The ERM, additionally, gives the SHPs the flexibility of allocate its firm energy in different manners along the year, which can be a valuable tool in mitigating the risks due to the seasonal availability of these plants. In this work, the combination of SHPs and Biomass in a single portfolio will be studied as a tool to mitigate the risks each plant faces individually. In particular, we will study the impact that this combination has over the decision process of SHPs on how to allocate their firm energy and how this different allocation can prove to be beneficial to both generators. In order to do so, a stochastic optimization model will be proposed to simulate the decision process of the SHPs on how to allocate its firm energy when combined in a portfolio with a Biomass in a fixed proportion or in the context of portfolio optimization. Case studies will be presented showing different strategies of commercialization by these generators and how the firm energy allocation decision by the SHP changes in each case.
42

Ensaios em matemática aplicada: estimação e trajetórias bootstrap de oferta de sangue e estudo de desempenho de extensões do algoritmo de Programação Dinâmica Dual Estocástica

Costa, Michelle Bandarra Marques 26 September 2017 (has links)
Submitted by Michelle Bandarra (michelle.bandarra@gmail.com) on 2017-11-09T12:55:55Z No. of bitstreams: 1 Dissertação EMAp Michelle Bandarra_correcoes_bib.pdf: 10393257 bytes, checksum: 31966b31b85e1d4936e9244bc9cbf592 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2017-11-22T17:42:42Z (GMT) No. of bitstreams: 1 Dissertação EMAp Michelle Bandarra_correcoes_bib.pdf: 10393257 bytes, checksum: 31966b31b85e1d4936e9244bc9cbf592 (MD5) / Made available in DSpace on 2017-11-29T13:56:40Z (GMT). No. of bitstreams: 1 Dissertação EMAp Michelle Bandarra_correcoes_bib.pdf: 10393257 bytes, checksum: 31966b31b85e1d4936e9244bc9cbf592 (MD5) Previous issue date: 2017-09-26 / We study two topics of applied mathematics. The first topic is devoted to the estimation of blood supply time series and the generation of simulated trajectories. The main goal is to contribute to the literature of stock management of perishable goods. We use Autoregressive Vetors models and two bootstrap techniques when residuals are nonGaussian. We conclude that both techniques are suitable for the problem at hand and are good approaches to enhance predictability of the blood supply time series. The second topic is devoted to the study of different extensions of the Stochastic Dual Dynamic Programming algorithm (SDDP). We compare the computational performance of two algorithms applied to portfolio selection models. The first one is Multicut Decomposition Algorithm (MuDA) which modifies SDDP by including multiple cuts (instead of just one) per stage and per iteration. The second, Cut Selection Multicut Decomposition Algorithms (CuSMuDA), combines MuDA with cut selection strategies and, to the best of our knowledge, has not been proposed so far in the literature. We compare two Cut Selection strategies, CS1 and CS2. We run simulations for 6 different instances of the portfolio problem. Results show the attractiveness of CuSMuDA CS2, which was much quicker than MuDA (between 5,1 and 12,6 times quicker) and much quicker than the other cut selection strategy, CuSMuDA CS1 (between 10,3 and 21,9 times quicker). / Estudamos dois tópicos distintos da matemática aplicada. O primeiro tópico dedica-se à estimação e geração de trajetórias futuras de séries de oferta de sangue, contribuindo para a literatura de gestão de estoque de bens perecíveis. São utilizados modelos de Vetores Auto Regressivos (VAR) e as trajetórias são geradas por duas técnicas distintas de bootstrap presentes na literatura que consideram a não-normalidade dos erros do modelo. Conclui-se que ambas técnicas são adequadas e abordagens possíveis para melhorar a previsibilidade das séries de oferta de sangue. O segundo tópico dedica-se ao estudo de diferentes extensões do algoritmo de Programação Dinâmica Dual Estocástica (Stochastic Dual Dynamic Programming, SDDP). Sob a ótica de modelos de seleção de carteira, são comparados os desempenhos computacionais de dois algoritmos. O primeiro é uma modificação do SDDP que calcula múltiplos cortes por iteração, Multicut Decomposition Algorithm (MuDA). O segundo introduz estratégias de seleção de corte ao MuDA, no que denominamos de Cut Selection Multicut Decomposition Algorithm, CuSMuDA e, até onde sabemos, ainda não foi proposto pela literatura. São comparadas duas estratégias de seleção de corte distintas, CS1 e CS2. Foram rodadas simulações para 6 casos do problema de seleção de carteira e os resultados mostram a atratividade do modelo proposto CuSMuDA CS2, que obteve tempos computacionais entre 5,1 e 12,6 vezes menores que o MuDA e entre 10,3 e 21,9 vezes menores que o CuSMuDA CS1.
43

[en] OPTIMIZATION UNDER UNCERTAINTY FOR INTEGRATED TACTICAL AND OPERATIONAL PLANNING OF THE OIL SUPPLY CHAIN / [pt] OTIMIZAÇÃO SOB INCERTEZA PARA O PLANEJAMENTO OPERACIONAL E TÁTICO INTEGRADO DA CADEIA DO PETRÓLEO

ADRIANA LEIRAS 15 June 2011 (has links)
[pt] A natureza incerta e os altos incentivos econômicos do negócio de refino são forças motrizes para melhorias nos processos de planejamento das refinarias. Decisões tomadas na cadeia do petróleo diferem principalmente na gama de atividades (integração espacial) e no horizonte de planejamento (integração temporal). O objetivo desta tese é abordar o problema da integração da cadeia do petróleo sob incerteza em diferentes níveis de decisão. Modelos de programação matemática tático e operacional são propostos. O modelo tático maximiza o lucro esperado da cadeia de suprimentos e aloca metas de produção para as refinarias considerando restrições logísticas. O modelo operacional maximiza o lucro esperado de cada refinaria determinando a quantidade de material processada por unidade de processo em um dado período. Ambos os modelos são lineares estocásticos de dois estágios, onde a incerteza é incorporada nos parâmetros dominantes de cada nível (preço e demanda no nível tático e suprimento de petróleo e capacidade das unidades no nível operacional). A integração espacial é discutida no nível tático (considerando a cadeia de suprimentos), enquanto a integração temporal é discutida na interação entre os dois níveis. Duas abordagens de integração temporal são consideradas: hierárquica, onde o fluxo de informações é somente do modelo tático para o operacional, e iterativa, onde há retorno do nível operacional para o tático. Um estudo de escala industrial foi conduzido para demonstrar os benefícios da integração em ambiente estocástico. Resultados são oferecidos no contexto de um estudo usando dados da indústria brasileira do petróleo para demonstrar a eficácia das abordagens propostas. / [en] The uncertain nature and high economic incentives of the refining business are driving forces for improvements in the refinery planning process. Decisions made at the oil chain differ mainly in the range of activities (spatial integration) and planning horizon (temporal integration). This thesis purpose is to address the problem of the oil chain integration under uncertainty at different decision levels. Tactical and operational mathematical programming models are proposed. The tactical model maximizes the expected profit of the supply chain and allocates the production targets to refineries taking logistics constraints into account. The operational model maximizes the expected profit of each refinery determining the amount of material that is processed at each process unit in a given period. Both models are two-stage stochastic linear programs where uncertainty is incorporated in the dominant random parameters at each level (price and demand at the tactical level and oil supply and process capacity unit at the operational level).Spatial integration is discussed at the tactical level (considering supply chain), whereas the temporal integration is discussed in the interaction between the two levels. Two temporal integration approaches are considered: hierarchical, where the flow of information is only from the tactical to the operational model, and iterative, where there is feedback from the tactical to the operational model. An industrial scale study was conducted to discuss the benefits of integration in a stochastic environment. Results are offered in the context of a study using data from the Brazilian oil industry to demonstrate the effectiveness of the proposed approaches.
44

[en] OPTMIZATION UNDER UNCERTAINTY: AN INTEGRATED OIL CHAIN APPLICATION / [pt] OTIMIZAÇÃO SOB INCERTEZA DE CARTEIRAS DE INVESTIMENTOS: APLICAÇÃO À CADEIA INTEGRADA DE PETRÓLEO E DERIVADOS

MARIA CELINA TAVARES CARNEIRO 19 August 2008 (has links)
[pt] Nos últimos anos, nota-se uma forte tendência no Brasil de oferta de petróleos cada vez mais pesados e ácidos em contraposição a uma crescente demanda de derivados mais leves dentro de especificações mais rígidas. Dessa forma, o Brasil se depara com a necessidade em adaptar suas refinarias e rede logística a esse novo perfil. Nesse contexto é importante a avaliação da cadeia integrada de petróleo e derivados no longo prazo, visando auxiliar a tomada de decisão em relação aos projetos que devem ser considerados na carteira de investimentos. Por se tratar de uma decisão de longo prazo, é importante levar em consideração as incertezas relacionadas aos parâmetros considerados, como: oferta e preço de petróleos, demanda e preço de derivados e outros. Assim, tornase possível a avaliação de uma carteira de projetos de investimentos considerando os riscos existentes. Este trabalho propõe apresentar uma metodologia de otimização sob incerteza, que utilize programação estocástica em conjunto com técnicas de otimização de portfólio, aplicada ao estudo de uma carteira de investimentos na área de abastecimento de petróleo. O estudo é focado em um modelo de programação linear que maximiza o resultado presente líquido esperado ao longo de um horizonte de tempo estipulado, dado um nível de risco aceitável. Foram propostas duas abordagens de medida de risco: Conditional Value-at-Risk (CVaR) e Minimax. A partir dos resultados numéricos, ficou comprovado que a decisão otimizada de investimento na área de petróleo e derivados apresenta variação com o nível de risco que se pretende assumir. / [en] Over the last years, a strong trade-off between crude oil offer and oil product demand has been posed in Brazil: while the oil produced in Brazil is getting heavier, its` products must be light, constrained by rigid specifications. Hence, the country needs to adapt its refineries and logistic network to this new profile. In this context, a long term analysis of the integrated oil chain is a relevant task. This analysis helps the decision maker to choose projects that should be considered in portfolio investment. During the decision process, it is important to take into account uncertainties related to some parameters: crude oil prices, crude oil offer, product prices, expected demand and others. By doing that, it is possible for the analyst to evaluate a project portfolio considering risks. The present work proposes a methodology for optimization under uncertainty, applied to the study of a portfolio investment for the downstream oil industry, employing both stochastic programming and portfolio optimization techniques. The study is focused on a linear programming model that maximizes the expected net present value (NPV) along the specified time horizon and risk level. Two approaches have been proposed to measure risk: Conditional Value-at-Risk (CVaR) and Minimax. The results show that the investment choice in the oil chain varies with the imposed risk level.
45

Optimal Power Allocation and Scheduling of Real-Time Data for Cognitive Radios

January 2016 (has links)
abstract: In this dissertation, I propose potential techniques to improve the quality-of-service (QoS) of real-time applications in cognitive radio (CR) systems. Unlike best-effort applications, real-time applications, such as audio and video, have a QoS that need to be met. There are two different frameworks that are used to study the QoS in the literature, namely, the average-delay and the hard-deadline frameworks. In the former, the scheduling algorithm has to guarantee that the packet's average delay is below a prespecified threshold while the latter imposes a hard deadline on each packet in the system. In this dissertation, I present joint power allocation and scheduling algorithms for each framework and show their applications in CR systems which are known to have strict power limitations so as to protect the licensed users from interference. A common aspect of the two frameworks is the packet service time. Thus, the effect of multiple channels on the service time is studied first. The problem is formulated as an optimal stopping rule problem where it is required to decide at which channel the SU should stop sensing and begin transmission. I provide a closed-form expression for this optimal stopping rule and the optimal transmission power of secondary user (SU). The average-delay framework is then presented in a single CR channel system with a base station (BS) that schedules the SUs to minimize the average delay while protecting the primary users (PUs) from harmful interference. One of the contributions of the proposed algorithm is its suitability for heterogeneous-channels systems where users with statistically low channel quality suffer worse delay performances. The proposed algorithm guarantees the prespecified delay performance to each SU without violating the PU's interference constraint. Finally, in the hard-deadline framework, I propose three algorithms that maximize the system's throughput while guaranteeing the required percentage of packets to be transmitted by their deadlines. The proposed algorithms work in heterogeneous systems where the BS is serving different types of users having real-time (RT) data and non-real-time (NRT) data. I show that two of the proposed algorithms have the low complexity where the power policies of both the RT and NRT users are in closed-form expressions and a low-complexity scheduler. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016
46

Stochastic Optimization for Feasibility Determination: An Application to Water Pump Operation in Water Distribution Network

January 2018 (has links)
abstract: The energy consumption by public drinking water and wastewater utilities represent up to 30%-40% of a municipality energy bill. The largest energy consumption is used to operate motors for pumping. As a result, the engineering and control community develop the Variable Speed Pumps (VSPs) which allow for regulating valves in the network instead of the traditional binary ON/OFF pumps. Potentially, VSPs save up to 90% of annual energy cost compared to the binary pump. The control problem has been tackled in the literature as “Pump Scheduling Optimization” (PSO) with a main focus on the cost minimization. Nonetheless, engineering literature is mostly concerned with the problem of understanding “healthy working conditions” (e.g., leakages, breakages) for a water infrastructure rather than the costs. This is very critical because if we operate a network under stress, it may satisfy the demand at present but will likely hinder network functionality in the future. This research addresses the problem of analyzing working conditions of large water systems by means of a detailed hydraulic simulation model (e.g., EPANet) to gain insights into feasibility with respect to pressure, tank level, etc. This work presents a new framework called Feasible Set Approximation – Probabilistic Branch and Bound (FSA-PBnB) for the definition and determination of feasible solutions in terms of pumps regulation. We propose the concept of feasibility distance, which is measured as the distance of the current solution from the feasibility frontier to estimate the distribution of the feasibility values across the solution space. Based on this estimate, pruning the infeasible regions and maintaining the feasible regions are proposed to identify the desired feasible solutions. We test the proposed algorithm with both theoretical and real water networks. The results demonstrate that FSA-PBnB has the capability to identify the feasibility profile in an efficient way. Additionally, with the feasibility distance, we can understand the quality of sub-region in terms of feasibility. The present work provides a basic feasibility determination framework on the low dimension problems. When FSA-PBnB extends to large scale constraint optimization problems, a more intelligent sampling method may be developed to further reduce the computational effort. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2018
47

Vícestupňové vnořené vzdálenosti v stochastické optimalizaci / Multistage nested distance in stochastic optimization

Horejšová, Markéta January 2018 (has links)
Multistage stochastic optimization is used to solve many real-life problems where decisions are taken at multiple times, e.g., portfolio selection problems. Such problems need the definition of stochastic processes, which are usually approxim- ated by scenario trees. The choice of the size of the scenario trees is the result of a compromise between the best approximation and the possibilities of the com- puter technology. Therefore, once a master scenario tree has been generated, it can be needed to reduce its dimension in order to make the problem computation- ally tractable. In this thesis, we introduce several scenario reduction algorithms and we compare them numerically for different types of master trees. A simple portfolio selection problem is also solved within the study. The distance from the initial scenario tree, the computational time, and the distance between the optimal objective values and solutions are compared for all the scenario reduction algorithms. In particular, we adopt the nested distance to measure the distance between two scenario trees. 1
48

Optimisation stochastique et adaptative pour surveillance coopérative par une équipe de micro-véhicules aériens / Adaptive stochastic optimization for cooperative coverage with a swarm of Micro Air Vehicles

Renzaglia, Alessandro 27 April 2012 (has links)
L'utilisation d'équipes de robots a pris de l'ampleur ces dernières années. Cela est dû aux avantages que peut offrir une équipe de robot par rapport à un robot seul pour la réalisation d'une même tâche. Cela s'explique aussi par le fait que ce type de plates-formes deviennent de plus en plus abordables et fiables. Ainsi, l'utilisation d'une équipe de véhicules aériens devient une alternative viable. Cette thèse se concentre sur le problème du déploiement d'une équipe de Micro-Véhicules Aériens (MAV) pour effectuer des missions de surveillance sur un terrain inconnu de morphologie arbitraire. Puisque la morphologie du terrain est inconnue et peut être complexe et non-convexe, les algorithmes standards ne sont pas applicables au problème particulier traité dans cette thèse. Pour y remédier, une nouvelle approche basée sur un algorithme d'optimisation cognitive et adaptatif (CAO) est proposée et évaluée. Une propriété fondamentale de cette approche est qu'elle partage les mêmes caractéristiques de convergence que les algorithmes de descente de gradient avec contraintes qui exigent une connaissance parfaite de la morphologie du terrain pour optimiser la couverture. Il est également proposé une formulation différente du problème afin d'obtenir une solution distribuée, ce qui nous permet de surmonter les inconvénients d'une approche centralisée et d'envisager également des capacités de communication limitées. De rigoureux arguments mathématiques et des simulations étendues établissent que l'approche proposée fournit une méthodologie évolutive et efficace qui intègre toutes les contraintes physiques particulières et est capable de guider les robots vers un arrangement qui optimise localement la surveillance. Finalement, la méthode proposée est mise en œuvre sur une équipe de MAV réels pour réaliser la surveillance d'un environnement extérieur complexe. / The use of multi-robot teams has gained a lot of attention in recent years. This is due to the extended capabilities that the teams offer compared to the use of a single robot for the same task. Moreover, as these platforms become more and more affordable and robust, the use of teams of aerial vehicles is becoming a viable alternative. This thesis focuses on the problem of deploying a swarm of Micro Aerial Vehicles (MAV) to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. Since the terrain's morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this thesis. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms, which require perfect knowledge of the terrain's morphology to optimize coverage. In addition, it is also proposed a different formulation of the problem in order to obtain a distributed solution, which allows us to overcome the drawbacks of a centralized approach and to consider also limited communication capabilities. Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations able to navigate the robots to an arrangement that (locally) optimizes the surveillance coverage. The proposed method is finally implemented in a real swarm of MAVs to carry out surveillance coverage in an outdoor complex area.
49

Estudo de confiabilidade aplicado à otimização da operação em tempo real de redes de abastecimento de água / Study of reliability applied to real time optimization of operation of water network supply

Frederico Keizo Odan 28 June 2013 (has links)
A presente pesquisa realizou o estudo da confiabilidade aplicado à otimização da operação em tempo real de sistemas de abastecimento de água (SAA). Almeja-se que a otimização da operação em tempo real empregue técnicas que a tornem robusta, ou seja, que considerem as incertezas inerentes a um SAA real. Para tanto, é necessário associar ao modelo de otimização um previsor de demanda e um simulador hidráulico. O previsor produzirá estimativas de demandas futuras para o horizonte desejado, o qual alimentará o simulador, a fim de que sejam determinadas as estratégias operacionais otimizadas para atendimento das demandas previstas. Implementou-se o método de otimização AMALGAM (\"A Multialgorithm Genetically Adaptive Method\"), juntamente com as demais rotinas computacionais necessárias para integrar o simulador hidráulico (EPANET 2) e o previsor de demanda baseado na Rede Neural Dinâmica (DAN2). O modelo desenvolvido foi aplicado nos setores de abastecimento Eliana, Iguatemi e Martinez, os quais são operados pelo Departamento Autônomo de Água e Esgotos (DAAE) da cidade de Araraquara, SP. Os modelos das redes de água foram calibrados por meio de dados de vazão e carga de pressão coletados em campanhas de campo. As estratégias operacionais resultantes foram comparadas as operações praticadas pelo DAAE, resultando em reduções no custo do consumo de energia de 14%, 13% e 30% para os setores Eliana, Iguatemi e Martinez, respectivamente. / This research project proposes the study of reliability applied to real time optimization of operation of water supply network (WSN). It is desired to obtain robust real time optimization of operation through the use of adequate techniques which accounts the inherent uncertainty of a real WSN. To accomplish the task it is necessary to associate to the optimization model a demand forecaster and a hydraulic simulator. The forecaster will produce the future demand for the planning horizon to serve as input for the simulator, so it is possible to obtain the optimized operation to meet the predicted demand. It was implemented the AMALGAM (\"A Multialgorithm Genetically Adaptive Method\") to serve as optimization model as well as the necessary computational routine to link the EPANET hydraulic simulator as well as the demand forecaster based on DAN2. The developed model was applied to the sectors Eliana, Iguatemi and Martinez, which are part of the water system operated by the Autonomous Department of Water and Sewer (DAAE) of Araraquara, SP. The water network model was calibrated using data collected on field campaign to gather pressure and flow data. The optimized operation was compared to the operation from DAAE, resulting in reduction of energy consumption cost of 14%, 13% and 30% respectively for the sectors Eliana, Iguatemi and Martinez.
50

Risk-Averse Optimization and its Applications in Power Grids with Renewable Energy Integration

Dashti, Hossein, Dashti, Hossein January 2017 (has links)
Electric power is one of the most critical parts of everyday life; from lighting, heating, and cooling homes to powering televisions and computers. The modern power grids face several challenges such as efficiency, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, poor maintenance, human errors, terrorist/cyber attacks, and natural phenomena. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic optimization approaches provide mathematical frameworks for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the uncertainty into the decision making process to find an optimal trade off between cost and risk. In this dissertation, we focus on application of three risk-averse approaches to power systems modeling and optimization. Particularly, we develop models and algorithms addressing the cost-effectiveness and reliability issues in power grids with integrations of renewable energy resources. First, we consider a unit commitment problem for centralized hydrothermal systems where we study improving reliability of such systems under water inflow uncertainty. We present a two-stage robust mixed-integer model to find optimal unit commitment and economic dispatch decisions against extreme weather conditions such as drought years. Further, we employ time series analysis (specifically vector autoregressive models) to construct physical based uncertainty sets for water inflow into the reservoirs. Since extensive formulation is impractical to solve for moderate size networks we develop an efficient Benders' decomposition algorithm to solve this problem. We present the numerical results on real-life case study showing the effectiveness of the model and the proposed solution method. Next, we address the cost effectiveness and reliability issues considering the integration of solar energy in distributed (decentralized) generation (DG) such as microgrids. In particular, we consider optimal placement and sizing of DG units as well as long term generation planning to efficiently balance electric power demand and supply. However, the intermittent nature of renewable energy resources such as solar irradiance imposes several difficulties in decision making process. We propose two-stage stochastic programming model with chance constraints to control the risk of load shedding (i.e., power shortage) in distributed generation. We take advantage of another time series modeling approach known as autoregressive integrated moving average (ARIMA) model to characterize the uncertain solar irradiance more accurately. Additionally, we develop a combined sample average approximation (SAA) and linearization techniques to solve the problem more efficiently. We examine the proposed framework with numerical tests on a radial network in Arizona. Lastly, we address the robustness of strategic networks including power grids and airports in general. One of the key robustness requirements is the connectivity between each pair of nodes through a sufficiently short path, which makes a network cluster more robust with respect to potential disruptions such as man-made or natural disasters. If one can reinforce the network components against future threats, the goal is to determine optimal reinforcements that would yield a cluster with minimum risk of disruptions. We propose a risk-averse model where clusters represents a R-robust 2-club, which by definition is a subgraph with at least R node/edge disjoint paths connecting each pair of nodes, where each path consists of at most 2 edges. And, develop a combinatorial branch-and-bound algorithm to compare with an equivalent mathematical programming approach on random and real-world networks.

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