This dissertation explores a core tension in contemporary hydropower governance between two competing views: hydropower as a machine and as a geography. Policymakers rely on economic and engineering optimization models to plan energy system adaptations to climate change. The developers of those models tend to narrowly view the hydropower system as a combination of infrastructure and operations constraints that are knowable and replicable. They rely on quantifications of values to produce “optimal” outcomes given constraints. This is hydropower the machine. But more holistically, hydropower is a geography. Hydropower transforms waterways, implicating competing interests and values in limited resources: surface water and the landscapes it traverses. The first paper of this dissertation, published in Natural Resources Journal, takes an institutional economics lens to introduce the tension between the two views. Through an examination of two optimization models and an overview of hydropower law in California, that article argues that otherwise well-regarded and well-funded models of the hydropower system nonetheless fall short of informing policy because those models rely on an incomplete concept of governance. A partial resolution of the tension may be possible. The history of the development of one hydropower optimization model confirms the bulk of literature on co-production and collaborative science research. The participation of hydropower practitioners into the research and modelling process influenced the parameters and inputs to the model. And in doing so, the collaborative research produced a product that was credible, legitimate, and salient. As such, despite the mechanistic view of hydropower prevalent in the optimization model, the end result fit better within the immediate political context as communicated by the oversight committee. Additionally, the combination of formal rules for collaboration and the simultaneous creation of informal knowledge networks helped to bolster acceptability of the product. Even after formal processes and funding ended, the knowledge networks continued and were able to facilitate the eventual implementation of the model as a decision support tool. Yet, a closer look at just one aspect of hydropower governance—litigation in federal courts—reinforces the divide between hydropower the machine and the geography. A review of the past century of federal hydropower caselaw offers a window in to the multiple and evolving ways that water and energy law influence one another. Ultimately that history embraces variation as a core characteristic of hydropower governance, a condition which is at odds with the practical needs of modelling.

A bioenergetics model was constructed for stream-resident drift-feeding salmonids. Model predictions of surplus power (energy available per unit time for lll growth and reproduction) were not statistically distinguishable from observations of surplus power in three laboratory studies. Of 40 experimental trials in these three studies, the model correctly predicted surplus power in 39 cases (p < 0.05). I collected observations of rainbow trout (Oncorhynchus mykiss) focal velocity and physical habitat availability in the Green River of northeastern Utah, USA (1988-1990). In the winter of 1988, Flaming Gorge Dam generated hydropower and delivered an lJDStable discharge regime with a higher mean discharge to the Green River. During 1989 and 1990, Flaming Gorge Dam's operation was curtailed by drought. Therefore, the Green River exhibited a more stable discharge regime with lower mean daily discharge. During winters exhibiting the stable discharge regime, all size classes of rainbow trout selected slower focal velocities than under an unstable winter discharge regime. Season had less influence on microhabitat selection of large fish than smaller individuals. Rainbow trout larger than 33 cm (total length) find and use positions with low focal velocities and high velocity shear regardless of season. In contrast, during the summer, fish less than 33 cm TL find and use positions with much higher focal velocities and greater velocity shear compared to the winter. Four bioenergetic models were tested with the focal velocity use data. Two optimal goal models produced excellent fits (r2 = 0.91 and 0.93) to observed focal velocity use of rainbow trout larger than 33 cm TL. These results were consistent with the hypothesis that large rainbow trout were finding optimal focal velocity positions in stable discharge summers and under both discharge regimes in winter. Rainbow trout movement was quantified along two scales with radio-telemetered fish: 1) weekly observations generated estimates of distances moved at intervals greater than one day and 2) multiple observations of a fish in one day produced estimates of distances moved over hours. I found an unstable discharge regime significantly reduces movement measured weekly (F = 11.10, P = 0.0019); hourly movement rates (m/h) were also reduced (F = 5.90, P = 0.0273).

Prompt and efficient intervention is vital in reducing casualty figures during epidemic outbreaks, disasters, sudden civil strife or terrorism attacks. This can only be achieved if there is a fit-for-purpose and location-specific emergency response plan in place, incorporating geographical, time and vehicular capacity constraints. In this research, a comprehensive emergency response model for situations of uncertainties (in locations' demand and available resources), typically obtainable in low-resource countries, is designed. It involves the development of algorithms for optimizing pre-and post-disaster activities. The studies result in the development of four models: (1) an adaptation of a machine learning clustering algorithm, for pre-positioning depots and emergency operation centers, which optimizes the placement of these depots, such that the largest geographical location is covered, and the maximum number of individuals reached, with minimal facility cost; (2) an optimization algorithm for routing relief distribution, using heterogenous fleets of vehicle, with considerations for uncertainties in humanitarian supplies; (3) a genetic algorithm-based route improvement model; and (4) a model for integrating possible new locations into the routing network, in real-time, using emergency severity ranking, with a high priority on the most-vulnerable population. The clustering approach to solving dept location-allocation problem produces a better time complexity, and the benchmarking of the routing algorithm with existing approaches, results in competitive outcomes.

System models have been used to improve water management and environmental decision making. In spite of the many existing mathematical models and tools that attempt to improve environmental decision making, few efforts have been made to identify how scarce resources (e.g., water, budget) can be more efficiently allocated to improve the environmental and ecological performance of different ecosystems (e.g., wetland habitat). This dissertation presents a set of management tools to improve the environmental and ecological performance. These tools are described in three studies. First, a simple optimization model is developed to help regulators and watershed managers determine cost-effective best management practices (BMPs) to reduce phosphorus load at the Echo Reservoir Watershed, Utah. The model minimizes the costs of BMP implementation to achieve a specified phosphorus load reduction target. Second, a novel approach is developed to quantify wetland habitat performance. This performance metric is embedded in a new optimization model to recommend water allocations and invasive vegetation control in wetlands. Model recommendations are subject to constraints such as water availability, spatial connectivity of wetland, hydraulic infrastructure capacities, vegetation growth and responses to management, plus financial and time resources available to allocate water and invasive vegetation control. Third, an agent-based model is developed to simulate the spread of the invasive Phragmites australis (common reed), one of the most successful invasive plant species in wetlands. Results of the agent-based model are embedded into an optimization model (developed in the second study) to recommend invasive vegetation control actions. The second and third studies were applied at the Bear River Migratory Bird Refuge, which is the largest wetland complex on the Great Salt Lake, Utah. These three studies provide a set of decision-support tools that recommend: (1) BMPs to reduce phosphorus loading in a watershed, (2) management strategies to improve wetland bird habitat, and (3) control strategies to minimize invasive Phragmites spread. Together, these models provide important insights and recommendations for managers to make informed decisions to manage excess nutrients in water bodies as well as to improve wetland management.

Aquifer storage and recovery (ASR) involves artificially recharging an aquifer through well(s) using surplus water for later recovery in high-demand months. The operators of the studied ASR system developed the system as a means of receiving additional water rights to supplement their pre-existing water rights for extraction in dry months. However, the region’s water regulators define the performance of this ASR system as the amount of the injected water that is recoverable from the same wells during extraction periods. The study proposes recovery effectiveness (REN) as the performance index of this ASR system. REN equals the injectate proportion that the same wells can recover. Quantifying the system's achievable REN is required to determine the amount of the additional water rights. Similarity between the injected water and native groundwater, however, prevents an accurate REN estimation using on-field techniques. This necessitates the use of computer modeling for estimating REN in this system. The study employs simulation, statistical, and optimization models to quantify and maximize REN in the studied ASR system in Utah.

abstract: Imagine you live in a place without any storm water or wastewater systems! Wastewater and storm water systems are two of the most crucial systems for urban infrastructure. Water resources have become more limited and expensive in arid and semi-arid regions. According to the fourth World Water Development Report, over 80% of global wastewater is released into the environment without adequate treatment. Wastewater collection and treatment systems in the Kingdom of Saudi Arabia (KSA) covers about 49% of urban areas; about 25% of treated wastewater is used for landscape and crop irrigation (Ministry of Environment Water and Agriculture [MEWA], 2017). According to Guizani (2016), during each event of flooding, there are fatalities. In 2009, the most deadly flood occurred in Jeddah, KSA within more than 160 lives lost. As a consequence, KSA has set a goal to provide 100% sewage collection and treatment services to every city with a population above 5000 by 2025, where all treated wastewater will be used. This research explores several optimization models of planning and designing collection systems, such as regional wastewater and stormwater systems, in order to understand and overcome major performance-related disadvantages and high capital costs. The first model (M-1) was developed for planning regional wastewater system, considering minimum costs of location, type, and size sewer network and wastewater treatment plants (WWTPs). The second model (M-2) was developed for designing a regional wastewater system, considering minimum hydraulic design costs, such as pump stations, commercial diameters, excavation costs, and WWTPs. Both models were applied to the Jizan region, KSA. The third model (M-3) was developed to solve layout and pipe design for storm water systems simultaneously. This model was applied to four different case scenarios, using two approaches for commercial diameters. The fourth model (M-4) was developed to solve the optimum pipe design of a storm sewer system for given layouts. However, M-4 was applied to a storm sewer network published in the literature. M-1, M-2, and M-3 were developed in the general algebraic modeling system (GAMS) program, which was formulated as a mixed integer nonlinear programming (MINLP) solver, while M-4 was formulated as a nonlinear programming (NLP) procedure. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

<p>In today's retail environment, there are many consumer packaged goods (CPG) in the same category with various brands and differential products under the same brand. These differential products appear in different dimension sizes, display facing areas, purchasing costs and selling prices which are competing for a limited space in retail store shelves. Product assortment and space allocation of the chosen products to a limited shelf space is becoming more and more important for retailers. In this thesis we critically review the existing literature of shelf space allocation optimization models and solution techniques. We then propose a comprehensive model for shelf space allocation for a product category. Products are allocated to a two-dimensional area of a shelf section where a shelf section consists of multilevel vertical shelves. We account for adjustable shelf heights and product and brand integrity in a shelf section. Unlike the existing optimization models in the literature, we model our demand not only as a function of the space allocated to a product, in terms of the number of display facings, but also as a function of vertical product location in a shelf section and price sensitivity. Stackability of the products is also considered and products can be stacked depending on their package. Our objective is to maximize the retailer's daily gross profit. We numerically show that incorporating price changes and adjustable shelf spaces can have major impacts on the retailers' profit. Finally, we provide directions and suggestions for future research in this growing area of research.</p> / Master of Applied Science (MASc)

This dissertation investigated the ability of optimization-based biomechanical models to predict torso muscular activity. Two optimization models were considered: the Minimum Intensity Compression (MIC) model and the Sum of the Cubed Intensities (SCI) model. For each model, two sets of muscle geometries (moment arms, lines of action, and cross-sectional areas) were used as inputs: one was a compilation of several studies and one was reported by Han, Ahn, Goel, Takeuchi, and McGowan (1992). For each of the four model combinations, either 10 or 18 muscles were used to formulate predictions. With computer simulations, the four models were used to predict muscle. forces under loading conditions including three types of moments, flexion/extension, lateral bending, and torsional. The results indicated large differences in the predicted forces due to the different models and muscle geometries. Changes in force predictions for identical muscles were also found when the number of muscles in the formulation increased from 10 to 18. The models also predicted varying active and inactive regions of the muscles in response to changing moments. To determine empirically the activity of muscles and test the accuracy of optimization-based models and inputs, combinations of the three moments were also applied to subjects through loads held in the hands. The subjects maintained a static posture during physical exertions while attempting flexion/extension, lateral bending, and torsion to counter the external loads. Results indicated little improvement in prediction of actual muscle activity by including 18 muscles instead of 10. The SCI model with compilation geometry provided the best predictions of actual muscle activity judged by overall R? values for each muscle and by the number of subjects the model accounted for over 50% of the variation. Actual activities of five of the eight muscles monitored were well predicted: left and right rectus abdominis, left external oblique, and left and right erector spinae. The left and right latissimus dorsi were poorly predicted by the models, which was due to the use of the muscle in shoulder Stability which was not accounted for in trunk optimization models. The experimental method included application of moments to subjects through loads held by the hands and by loads attached to a harness mounted at the shoulder level. The left and right erector spinae were the only muscles which exhibited the same activity for both apparatuses used to apply moments. The left and right latissimus dorsi showed the largest increase in activity when loads were held with the hands instead of applied via the harness. Differences between hand and harness loading were also found for the left and right rectus abdominis muscles and left and right external obliques at varying moment conditions. / Ph. D.

A comercialização de energia elétrica de fontes renováveis, ordinariamente, constitui-se uma atividade em que as operações são estruturadas sob condições de incerteza, por exemplo, em relação ao preço \"spot\" no mercado de curto prazo e a geração de energia dos empreendimentos. Deriva desse fato a busca dos agentes pela formulação de estratégias e utilização de ferramentais para auxiliá-los em suas tomadas de decisão, visando não somente o retorno financeiro, mas também à mitigação dos riscos envolvidos. Análises de investimentos em fontes renováveis compartilham de desafios similares. Na literatura, o estudo da tomada de decisão considerada ótima sob condições de incerteza se dá por meio da aplicação de técnicas de programação estocástica, que viabiliza a modelagem de problemas com variáveis randômicas e a obtenção de soluções racionais, de interesse para o investidor. Esses modelos permitem a incorporação de métricas de risco, como por exemplo, o Conditional Value-at-Risk, a fim de se obter soluções ótimas que ponderem a expectativa de resultado financeiro e o risco associado da operação, onde a aversão ao risco do agente torna-se um condicionante fundamental. O objetivo principal da Tese - sob a ótica dos agentes geradores, consumidores e comercializadores - é: (i) desenvolver e implementar modelos de otimização em programação linear estocástica com métrica CVaR associada, customizados para cada um desses agentes; e (ii) aplicá-los na análise estratégica de operações como forma de apresentar alternativas factíveis à gestão das atividades desses agentes e contribuir com a proposição de um instrumento conceitualmente robusto e amigável ao usuário, para utilização por parte das empresas. Nesse contexto, como antes frisado, dá-se ênfase na análise do risco financeiro dessas operações por meio da aplicação do CVaR e com base na aversão ao risco do agente. Considera-se as fontes renováveis hídrica e eólica como opções de ativos de geração, de forma a estudar o efeito de complementaridade entre fontes distintas e entre sites distintos da mesma fonte, avaliando-se os rebatimentos nas operações. / The renewable energy trading, ordinarily, is an activity in which mostly operations are structured under uncertainty conditions, for instance, in relation to the energy spot price and assets generation. Derives from this fact the search of the agents for strategies formulation based on computational tools to assist their decision-making process, not only seeking financial returns, but also to mitigate the risks involved. Investments analysis in renewable sources share the same challenges. In the literature, the study of optimal decision-making under uncertainty conditions is made through the application of stochastic programming techniques, which enable modeling problems with random variables and find rational solutions. These models allow the incorporation of risk metrics, as the \"Conditional Value-at-Risk (CVaR)\", to provide optimal solutions that weigh the expected financial results and the associated risk, in which the agent\'s risk-aversion becomes an essential condition for defining the operation strategy. From the perspective of generators, consumers and traders agents, the main purposes of this thesis are: (i) to develop customized optimization models with CVaR metric associated, optimized in stochastic linear programming; and (ii) to apply the models for strategic analysis of operations under the risk-return binomial, focusing on the management activities of each of these agents, and considering renewable sources as option. In this context, the emphasis is on analysis of the operations financial risks through the application of CVaR and based on the agent\'s risk-aversion level. Furthermore, the hydro and wind renewables sources are options of generation assets in order to study the seasonal generation complementarity effect among them and the consequences on energy trading strategies.

A energia elétrica constitui um dos propulsores da economia de um país, assumindo um papel extremamente importante e estratégico, pois influi diretamente na capacidade produtiva. A expansão da produção de energia elétrica não se consegue somente com medidas de curto prazo, pois as obras deste setor demandam um longo tempo de execução, medido em anos e dependendo da magnitude da obra, o prazo pode até superar uma década. O parque gerador nacional é constituído predominantemente por usinas hidroelétricas, complementado por usinas térmicas que utilizam diversas fontes de combustível, havendo a necessidade de minimizar a produção das térmicas, em virtude do alto custo de geração, em relação ao custo de geração hidroelétrica. Garantir o suprimento da demanda futura de energia elétrica é uma tarefa complexa de planejamento que basicamente, depende da análise de dois cenários que se compõem: o primeiro cenário é o que desenha o crescimento futuro da economia e neste caso, desde que não ocorram fatos extraordinários como o recente crescimento econômico experimentado pela China, a previsão da demanda não acarreta surpresa de grande significância; o segundo cenário, traz como característica a incerteza, uma vez que a produção das hidroelétricas depende da quantidade de água disponível dos cursos de água, que por sua vez, depende do regime de chuvas passado e corrente. O índice pluviométrico é um dado estocástico, ocorrendo ao sabor da probabilidade, o que remete a um estudo de casos e seus desdobramentos, acarretando um leque de possibilidades de estados muito grande, dificultando as análises sobre a previsão futura. Planejar o setor elétrico compreende prever um crescimento de demanda e equipar o setor com máquinas de geração, necessárias para atender a demanda, com uma margem de risco calculada. Para isto, utilizam-se modelos de simulação que possibilitem o exercício de previsão, combinando-se os dois cenários citados, visualizando os estados sub seqüentes, decorrentes de decisões tomadas. A dificuldade desta tarefa é devida à quantidade de alternativas da situação futura, resultante de um fenômeno combinatório de possibilidades que exige para simulação dos modelos, não só uma grande capacidade de processamento dos computadores como também, uma estratégia de tratamento do problema, baseada em processos numéricos especializados e dirigidos a este tipo de problema. Dada a importância e magnitude deste assunto, qualquer esforço que venha a contribuir para uma melhoria do planejamento do setor elétrico, traz benefícios significativos, o que corrobora com os propósitos desta tese, que busca em primeiro lugar, propor soluções técnicas viáveis e econômicas para o problema de otimização da geração de energia elétrica, e em segundo lugar, apresentar uma solução para este tipo de problema, com uma abordagem inovadora, provida de um potencial significativo para aplicação em muitos outros tipos de problemas similares. / Electrical power could be considered as one of the economy propulsion vector of a country, assuming extremely important and strategic role because it makes direct influence to the production capacity. The expansion of electrical energy production could not only be done in a short time because constructions in this area take many years and could require more then a decade depending on the magnitude of them. The national power generation group is constituted mainly by hydro power plants complemented by thermal power plants which use several kinds of fuel which generation cost is high, if compared to hydro power generation, and should be minimized. It is a complex planning issue to supply the future power demand which basically depends on the analysis of two compoundable scenarios: the first one refers to the forecast of future economy growing and in this case, unless unpredicted issues occur such as the recent high economy growing experimented by China, the future demand does not show any surprise and is easy to predict; the second one, has inside the uncertainty because the hydro plants productions depends on the water quantity of rivers which depends on the past and current rainfall regimen. The quantity of rainfall is a stochastic data and follows the rules of probability and this drives to the study of cases and its deployments which are numerous causing difficulties to forecast the future. The planning of the electrical area has to examine the future demand and provide the necessary power generation equipment assuming a certain risk. To have it done, simulation models are used to predict the future, combining the two scenarios cited before, and viewing the results promoted by decision took in a step before. The difficult of this task is caused by the big amount of future alternatives provided by the combinatorial phenomena which require, to process the model, a computer with high processing capacity and specialized and specific methods that can resolve this king of problem. Because of the importance and magnitude of this issue, every effort which contributes to the improvement of power planning is welcome and this corroborates with this thesis which has an objective to propose technical, viable and economic solutions to solve the optimization problems with a new approach and has potential to be applied in many others kind of similar problems.