Water reservoirs are used to regulate river discharge for a variety of reasons, such as flood mitigation, water availability for irrigation, municipal consumption and power production purposes. Recent efforts to increase the amount of renewable power production have seen an increase in intermittent climate-variable power production due to wind and solar power production. The additional variable energy production has increased the need for regulating the capacity of the electrical system, to which hydropower production is a significant contributor. The hydraulic impact on the time lags of flows between production stations have often largely been ignored in optimization planning models in favor of computational efficiency and simplicity. In this thesis, the hydrodynamics in the stream network connecting managed reservoirs were described using the kinematic-diffusive wave (KD) equation, which was implemented in optimization schemes to illustrate the effects of wave diffusion in flow stretches on the resulting production schedule. The effect of wave diffusion within a watershed on the variance of the discharge hydrograph within a river network was also analyzed using a spectral approach, illustrating that wave diffusion increases the variance of the hydrograph while the regulation of reservoirs generally increases the variance of the hydrograph over primarily short periods. Although stream hydrodynamics can increase the potential regulation capacity, the total capacity for power regulation in the Swedish reservoir system also depends significantly on the variability in climatic variables. Alternative formulations of the environmental objectives, which are often imposed as hard constraints on discharge, were further examined. The trade-off between the objectives of hydropower production and improvement of water quality in downstream areas was examined to potentially improve the ecological and aquatic environments and the regulation capacity of the network of reservoirs. / <p>QC 20170210</p>

O presente estudo considera a aplicação do modelo SISAGUA de simulação matemática e de otimização para a operação de sistemas de reservatórios integrados em sistemas complexos para o abastecimento de água. O SISAGUA utiliza a programação não linear inteira mista (PNLIM) com os objetivos de evitar ou minimizar racionamentos, equilibrar a distribuição dos armazenamentos em sistemas com múltiplos reservatórios e minimizar os custos de operação. A metodologia de otimização foi aplicada para o sistema produtor de água da Região Metropolitana de São Paulo (RMSP), que enfrenta a crise hídrica diante de um cenário de estiagem em 2013-2015, o pior na série histórica dos últimos 85 anos. Trata-se de uma região com 20,4 milhões de habitantes. O sistema é formado por oito sistemas produtores parcialmente integrados e operados pela Sabesp (Companhia de Saneamento do Estado de São Paulo). A RMSP é uma região com alta densidade demográfica, localizada na Bacia Hidrográfica do Alto Tietê e caracterizada pela baixa disponibilidade hídrica per capita. Foi abordada a possibilidade de considerar a evaporação durante as simulações, e a aplicação de uma regra de racionamento contínua nos reservatórios, que transforma a formulação do problema em programação não linear (PNL). A evaporação se mostrou pouco representativa em relação a vazão de atendimento à demanda, com cerca de 1% da vazão. Se por um lado uma vazão desta magnitude pode contribuir em um cenário crítico, por outro essa ordem de grandeza pode ser comparada às incertezas de medições ou previsões de afluências. O teste de sensibilidade das diferentes taxas de racionamento em função do volume armazenado permite analisar o tempo de resposta de cada sistema. A variação do tempo de recuperação, porém, não se mostrou muito significativo. / The current study considers the mathematical simulation and optimization model SISAGUA applied to operation of complex multireservoir systems for water supply. The SISAGUA model uses mixed integer nonlinear programming (MINLP) with objectives of avoid or minimize shortages, balance storage distribution in multireservoir systems and minimize operation costs. The optimization methodology was applied in the water supply system from São Paulo Metropolitan Region, which faces a water crisis in a drought scenario in 2013-2015, the worst in the last 85 years historical series. It is a region with 20.4 million inhabitants, and the system consists of eight partially integrated supply systems operated by Sabesp (Sanitation Company of Sao Paulo State). The metropolitan region presents a high population density, located in the Upper Tiete hydrographic basin, characterized by low water availability per capita. It was discussed the possibility of considering evaporation during simulations, and the application of a continuous hedging rule in the reservoirs which modifies the mathematical formulation to nonlinear programming (NLP). Evaporation proved barely representative in relation to demand flow, with about 1% of the flow. On one hand, a flow rate of this magnitude may be considered in a critical scenario, on the other hand, this order of magnitude can be compared to the uncertainties of measurement or inflow forecasts. The sensitivity test of different rationing rates depending on the stored volume can analyze the time of response of each system. The change in recovery time, however, was not very significant.

Hydropower is the most important domestic energy source of Turkey. Thus, wise planning and development of the unused hydropower potential of the country is vital. There are many hydroelectric power plants under planning stage in our country. Altiparmak HEPP is one of them. General Directorate of Electrical Power Resources Survey and Development Administration (EIE) and ANC Enerji conducted two separate feasibility studies for Altiparmak HEPP in 2001 and 2009, respectively. Traditionally, the energy income calculations for HEPPs are based on DSI or EIE Methods in Turkey. Both of these methods evaluate the firm and the secondary energy generations separately. Besides they use fixed prices for these two types of energies. However, hourly electricity prices are used for electricity trading in Turkey. A detailed economic analysis of Altiparmak HEPP is conducted in this study. The economic analysis included various factors, such as tailwater level change, varying operating levels for different seasons and precipitation and evaporation amounts which are not conventionally included in feasibility studies. Moreover, the energy income calculations are conducted with four different methods, the DSI Method, the EIE Method, the ANC Method and the Variable Price Method (VPM). The VPM is developed in this study and it allows utilization of hourly electricity prices in calculating energy income of the HEPP. To shed some light on how hourly electricity prices develop, this thesis includes a chapter on the electricity market which explains the details of electricity trading in our country after the Electricity Market Balancing and Settlement Regulation became active in 2009.

O presente estudo considera a aplicação do modelo SISAGUA de simulação matemática e de otimização para a operação de sistemas de reservatórios integrados em sistemas complexos para o abastecimento de água. O SISAGUA utiliza a programação não linear inteira mista (PNLIM) com os objetivos de evitar ou minimizar racionamentos, equilibrar a distribuição dos armazenamentos em sistemas com múltiplos reservatórios e minimizar os custos de operação. A metodologia de otimização foi aplicada para o sistema produtor de água da Região Metropolitana de São Paulo (RMSP), que enfrenta a crise hídrica diante de um cenário de estiagem em 2013-2015, o pior na série histórica dos últimos 85 anos. Trata-se de uma região com 20,4 milhões de habitantes. O sistema é formado por oito sistemas produtores parcialmente integrados e operados pela Sabesp (Companhia de Saneamento do Estado de São Paulo). A RMSP é uma região com alta densidade demográfica, localizada na Bacia Hidrográfica do Alto Tietê e caracterizada pela baixa disponibilidade hídrica per capita. Foi abordada a possibilidade de considerar a evaporação durante as simulações, e a aplicação de uma regra de racionamento contínua nos reservatórios, que transforma a formulação do problema em programação não linear (PNL). A evaporação se mostrou pouco representativa em relação a vazão de atendimento à demanda, com cerca de 1% da vazão. Se por um lado uma vazão desta magnitude pode contribuir em um cenário crítico, por outro essa ordem de grandeza pode ser comparada às incertezas de medições ou previsões de afluências. O teste de sensibilidade das diferentes taxas de racionamento em função do volume armazenado permite analisar o tempo de resposta de cada sistema. A variação do tempo de recuperação, porém, não se mostrou muito significativo. / The current study considers the mathematical simulation and optimization model SISAGUA applied to operation of complex multireservoir systems for water supply. The SISAGUA model uses mixed integer nonlinear programming (MINLP) with objectives of avoid or minimize shortages, balance storage distribution in multireservoir systems and minimize operation costs. The optimization methodology was applied in the water supply system from São Paulo Metropolitan Region, which faces a water crisis in a drought scenario in 2013-2015, the worst in the last 85 years historical series. It is a region with 20.4 million inhabitants, and the system consists of eight partially integrated supply systems operated by Sabesp (Sanitation Company of Sao Paulo State). The metropolitan region presents a high population density, located in the Upper Tiete hydrographic basin, characterized by low water availability per capita. It was discussed the possibility of considering evaporation during simulations, and the application of a continuous hedging rule in the reservoirs which modifies the mathematical formulation to nonlinear programming (NLP). Evaporation proved barely representative in relation to demand flow, with about 1% of the flow. On one hand, a flow rate of this magnitude may be considered in a critical scenario, on the other hand, this order of magnitude can be compared to the uncertainties of measurement or inflow forecasts. The sensitivity test of different rationing rates depending on the stored volume can analyze the time of response of each system. The change in recovery time, however, was not very significant.

The world's water resources face unsustainable pressure from population growth, changes in consumption patterns, pollution, and overexploitation. Water resources managers have developed holistic approaches such as IWRM (Integrated Water Resources Management) and, more recently, the WEEF (Water-Energy-Environment-Food) nexus to address the situation. However, their application in day-to-day water resources management is still challenging due to the of little knowledge, data, and tools. One area where that challenge needs practical solutions is reservoir operation. The current study aims to improve the reservoir module in the Soil and Water Assessment Tool (SWAT) so that operation rules that aim to meet various water, food, and electricity objectives can be simulated. The improved SWAT model is used to simulate the management of the Sélingué reservoir in Mali, West Africa. The reservoir operation was simulated under three different operation rules: 1) priority to monthly hydropower production (HPP) target (rule 1); 2) respect of predefined monthly target storage (rule 2); 3) priority to downstream environmental flow, irrigation, and municipal water demands (rule 3). Results show that when priority is given to the HPP target (rule 1), 98.3% of the electricity demand is met. At the same time, the dam can supply 81.72% of the water demand to maintain environmental flow and sustain irrigation and municipal water consumption. It also ensures water availability with an annual target storage deviation estimated at 1.8%. When rule 2 is implemented, a gap of 8.5% between electricity production and electricity demand is observed. Rule 2 also failed to sustain environmental flow and supply flow for irrigation and municipal consumption as a gap of 15.39% between the supply and the demand was observed. Similarly to rule 1, It ensures water availability with an annual target storage deviation estimated at 1.25%. When rule 3 is enforced (i.e., the priority is given to environmental flow, irrigation, and municipal water demands) the reservoir can maintain the environmental flow and maintain irrigation, and municipal water requirements with a gap of 17.7% between the supply and the demands. However, HPP production decreases with a gap of 12.56% between the electricity supply and demand. Its capacity to supply water in the long term is low as it has the highest target storage deviation with a value of 18%. These results indicate that rule 1 offers more guarantees considering the food and electricity security and environmental challenges. Note that the simulations are done assuming that these rules are systematically followed. In practice, decision-makers can deviate from a rule in exceptional circumstances to maximize benefits or avert unwanted consequences. Finally, a decision support system (DSS) was developed to assist decision-makers in selecting efficient reservoir operation policies for multipurpose reservoirs combining HPP and irrigation.

<p dir="ltr">Reservoirs play a vital role in water resource management, serving essential functions such as flood mitigation, water supply, power generation, and environmental conservation. In the U.S., many of these structures were constructed in the 1900s, and were primarily designed as single purpose facilities for flood risk reduction. Facing increasing threats of water shortages and groundwater depletion, the transition of these reservoirs to multi-purpose operations has never been more imperative. Operational modifications and optimizations emerge as a promising solution, offering cost-effectiveness, swift implementation, and minimal ecological disruption.</p><p dir="ltr">This dissertation advances the theory and framework of modification and optimization of reservoir operations to facilitate their transition from single to multi-purpose use. This dissertation begins with targeted optimization of static operations and progressively advances to dynamic strategies across complex multi-reservoir-river systems. This dissertation sets three primary objectives: (1) To develop a comprehensive framework for assessing the conversion potential of single-purpose reservoirs and optimizing static operation strategies for enhanced multi-purpose functionality. (2) To devise dynamic control strategies that bolster reservoir performance during extreme events through the implementation of inflow-based pre-release operations. (3) To employ a Multi-Objective Simulation-Optimization (MOSO) framework that integrates large-scale datasets and advanced optimization algorithms, optimizing multi-purpose, multi-reservoir operations in complex systems and enhancing decision-making through Multi-Criteria Decision-Making (MCDM) methods.</p><p dir="ltr">In the first objective, a robust framework is developed to evaluate and facilitate the conversion of single-purpose reservoirs into multi-purpose systems. Leveraging historical data, the proposed framework establishes Maximum Safe Water Levels (MSWLs) to optimize flood control while enhancing water supply capabilities. The methodology incorporates numerical reservoir simulation models alongside historical inflow data analysis of 15 reservoirs operated by the U.S. Army Corps of Engineers, Louisville District, all originally designed exclusively for single-purpose flood control. The findings reveal opportunities for some reservoirs to significantly increase their water supply without compromising flood management efficiency.</p><p dir="ltr">The second objective delves into dynamic control strategies for reservoir operation, with a focus on pre-release mechanisms. This objective utilizes inflow-based forecasting models to assess the impacts of different pre-release timings on flood mitigation. This study focuses on 11 of the reservoirs identified in the first objective as having potential for transition to multi-purpose use, exploring dynamic operational adjustments necessary for enhanced performance. The results show that a 72-hour pre-release lead time markedly enhanced flood control effectiveness, whereas a 24-hour lead time provides a practical compromise, achieving substantial flood mitigation with minimal adverse impacts.</p><p dir="ltr">The third objective involves developing an advanced framework utilizing the Multi-Objective Simulation-Optimization (MOSO) model and extensive datasets to optimize pre-release operations in multi-purpose reservoirs. Implementing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Criteria Decision-Making (MCDM) methods, the framework integrates reservoir simulation models and flow routing to refine operations based on projected flood forecasts. Applied to the Green River watershed in Kentucky, this method produces Pareto-optimal solutions, elucidating the trade-offs between flood control, water supply reliability, and downstream channel performance. The results underscore the framework’s potential to significantly refine operational strategies, bolstered by sensitivity analyses that explore the effects of varying storage levels and inflow conditions, thus fostering adaptive, data-driven management for sustainable water resource optimization.</p><p dir="ltr">This dissertation contributes to the field of water resource management by demonstrating and developing innovative strategies and frameworks for the transition of single purpose reservoirs to multi-purpose systems, modifying flood control and enhancing water supply capabilities. This dissertation provides practical solutions with available data, simulation models, and optimization tools, which enable effective decision-making and operational adjustments under varying conditions. Overall, this dissertation presents a foundation for more resilient, reliable, and adaptive water management practices for reservoirs, that can meet diverse demands in a changing environmental landscape.</p>

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

Diversas atividades humanas são fortemente dependentes do clima e da sua variabilidade, especialmente aquelas relacionadas ao uso da água. A operação integrada de reservatórios com múltiplos usos requer uma série de decisões que definem quanta água deve ser alocada, ao longo do tempo para cada um dos usos, e quais os volumes dos reservatórios a serem mantidos. O conhecimento antecipado das condições climáticas resulta de vital importância para os operadores de reservatórios, pois o insumo dos reservatórios é a vazão dos rios, que por sua vez é dependente de condições atmosféricas e hidrológicas em diferentes escalas de tempo e espaço. A pesquisa trata sobre três importantes elementos de subsídio à tomada de decisão na operação de reservatórios baseada na previsão de variáveis hidrológicas: (a) as previsões de vazão de curto prazo; (b) as previsões de precipitação de longo prazo e (c) as medidas de desempenho das previsões. O reservatório de Furnas, localizado na bacia do Rio Grande, em Minas Gerais, foi selecionado como estudo de caso devido, principalmente, à disponibilidade de previsões quantitativas de chuva e pela importância desse reservatório na região analisada. A previsão de curto prazo de vazão com base na precipitação foi estimada com um modelo empírico (rede neural artificial) e a previsão de precipitação foi obtida pelo modelo regional ETA. Uma metodologia de treinamento e validação da rede neural artificial foi desenvolvida utilizando previsões perfeitas de chuva (considerando a chuva observada como previsão) e utilizando o maior número de dados disponíveis, favorecendo a representatividade dos resultados obtidos. A metodologia empírica alcançou os desempenhos obtidos com um modelo hidrológico conceitual, mostrando-se menos sensitiva aos erros na previsão quantitativa de precipitação nessa bacia. Os resultados obtidos mostraram que as previsões de vazão utilizando modelos empíricos e conceituais e incorporando previsões quantitativas de precipitação são melhores que a metodologia utilizada pelo ONS no local de estudo. A redução dos erros de previsão relativos à metodologia empregada pelo ONS foi em torno de 20% quando usadas previsões quantitativas de precipitação definidas pelo modelo regional ETA e superiores a 50% quando usadas previsões perfeitas de precipitação. Embora essas últimas previsões nunca possam ser obtidas na prática, os resultados sugerem o quanto o incremento do desempenho das previsões quantitativas de chuva melhoraria as previsões de vazão. A previsão de precipitação de longo prazo para a bacia analisada foi também estimada com um modelo empírico de redes neurais artificiais e utilizando índices climáticos como variáveis de entrada. Nesse sentido, foram estimadas previsões de precipitação acumulada no período mais chuvoso (DJF) utilizando índices climáticos associados a fenômenos climáticos, como o El Niño - Oscilação Sul e a Oscilação Decadal do Pacífico, e a modos de variabilidade climática, como a Oscilação do Atlântico Norte e o Modo Anular do Hemisfério Sul. Apesar das redes neurais artificiais terem sido aplicadas em diversos problemas relacionados a hidrometeorologia, a aplicação dessas técnicas na previsão de precipitação de longo prazo é ainda rara. Os resultados obtidos nesse trabalho mostraram que consideráveis reduções dos erros da previsão relativos ao uso apenas da média climatológica como previsão podem ser obtidos com a metodologia utilizada. Foram obtidas reduções dos erros de, no mínimo 50%, e chegando até um valor próximo a 75% nos diferentes testes efetuados no estudo de caso. Uma medida de desempenho da previsão foi desenvolvida baseada no uso de tabelas de contingência e levando em conta a utilidade da previsão. Essa medida de desempenho foi calculada com base nos resultados do uso das previsões por um modelo de operação de reservatório, e não apenas na comparação de vazões previstas e observadas. Nos testes realizados durante essa pesquisa, ficou evidente que não existe uma relação unívoca entre qualidade das previsões e utilidade das previsões. No entanto, em função de comportamentos particulares das previsões, tendências foram encontradas, como por exemplo nos modelos cuja previsão apresenta apenas defasagem. Nesses modelos, a utilidade das previsões tende a crescer na medida que a qualidade das mesmas aumenta. Por fim, uma das grandes virtudes da medida de desempenho desenvolvida nesse trabalho foi sua capacidade de distinguir o desempenho de modelos que apresentaram a mesma qualidade. / Several human activities are strongly dependent on climate and its variability, especially those related to water use. The operation of multi-purpose reservoirs systems defines how much water should be allocated and the reservoir storage volumes to be maintained, over time. Knowing in advance the weather conditions helps the decision making process, as the major inputs to reservoirs are the streamflows, which are dependent on atmospheric and hydrological conditions at different time-space scales. This research deals with three important aspects towards the decision making process of multi-purpose reservoir operation based on forecast of hydrological variables: (a) short-term streamflow forecast, (b) long-range precipitation forecast and (c) performance measures. The Furnas reservoir on the Rio Grande basin was selected as the case study, primarily because of the availability of quantitative precipitation forecasts from the Brazilian Center for Weather Prediction and Climate Studies and due to its importance in the Brazilian hydropower generation system. Short-term streamflow forecasts were estimated by an empirical model (artificial neural network – ANN) and incorporating forecast of rainfall. Quantitative precipitation forecasts (QPFs), defined by the ETA regional model, were used as inputs to the ANN models. A methodology for training and validating the ANN models was developed using perfect precipitation forecasts (i.e., using the observed precipitation as if it was a forecast) and considering the largest number of available samples, in order to increase the representativeness of the results. The empirical methodology achieved the performance obtained with a conceptual hydrological model and seemed to be less sensitive to precipitation forecast error relative to the conceptual hydrological model. Although limited to one reservoir, the results obtained show that streamflow forecasting using empirical and conceptual models and incorporating QPFs performs better than the methodology used by ONS. Reduction in the forecast errors relative to the ONS method was about 20% when using QPFs provided by ETA model, and greater than 50% when using the perfect precipitation forecast. Although the latter can never be achieved in practice, these results suggest that improving QPFs would lead to better forecasts of reservoir inflows. Long-range precipitation forecast was also estimated by an empirical model based on artificial neural networks and using climate indices as input variables. The output variable is the summer (DJF) precipitation over the Furnas watershed. It was estimated using climate indices related to climatic phenomena such as El Niño - Southern Oscillation and the Pacific Decadal Oscillation and modes of climate variability, such as the North Atlantic Oscillation and the Southern Annular Mode. Despite of ANN has been applied in several problems of hydrometeorological areas, the application of such technique for long-range precipitation forecast is still rare. The results obtained demonstrate how the methodology for seasonal precipitation forecast based on ANN can be particularly helpful, with the use of available time series of climate indices. Reductions in the forecast errors achieved by using only the climatological mean as forecast were considerable, being at least of 50% and reaching values close to 75% in several tests. A performance measure based on the use of contingency tables was developed taking into account the utility of the forecast. This performance measure was calculated based on the results of the use of the forecasts by a reservoir operation model, and not only by comparing streamflow observed and forecast. The performed tests show that there is no unequivocal relationship between quality and utility of the forecasts. However, when the forecast has a particular behavior, trends were found in the relationship between utility and quality of the forecast, such as models that generate streamflow forecast with lags in comparison to the observed values. In these models, the utility of the forecasts tends to enhance as the quality increases. Finally, the ability to distinguish the performance of forecast models having similar quality was one of the main merits of the performance measure developed in this research.

L’énergie nucléaire, qui est une technologie de génération largement utilisée dans des systèmes électriques, est caractérisée par des coûts fixes élevés et des coûts variables bas. Pour amortir ses coûts fixes, le nucléaire est préférentiellement utilisé pour une opération en base inflexible, c’est-à-dire opérer à un niveau constant pour répondre à la partie non variable de la demande d’un système électrique et produire au maximum de sa propre capacité. En raison de cette spécificité, l’insertion de la production nucléaire dans les marchés concurrentiels d’électricité n’a pas été profondément étudiée jusqu’à présent. Par conséquent, même dans des marchés concurrentiels, la question de la gestion optimale d’un parc de production nucléaire n’a pas été soulevée parce que la production nucléaire est censée fonctionner en continu (pour couvrir la demande de base). Cependant, il y a des cas ou` la gestion de la production nucléaire semble plus complexe que ne le suggère cette vision simplifiée. En règle générale, lorsque la proportion de l’énergie nucléaire dans un parc de production est élevée, la production nucléaire doit s’adapter aux variations de la demande. Cela soulève la question de la façon optimale de gérer cette technologie de production dans ce contexte. Comme cette question n’a pas été étudiée jusqu’à présent, il est nécessaire de proposer un cadre théorique qui permet une analyse des situations comme celle de la France, avec un marché concurrentiel et où le nucléaire représente 80% de la production, c’est-à-dire beaucoup plus que ce qui serait nécessaire pour couvrir la demande de base. Nous nous plaçons dans un horizon à moyen terme de la gestion (1 à 3 ans) pour tenir compte de la variation saisonnière de la demande. A moyen terme, le gestionnaire d’un parc nucléaire très large (comme le parc français) doit ajuster sa production selon les variations saisonnières de la demande. Dans ce cadre, le stock de combustible nucléaire peut être analysé comme un réservoir puisque les centrales nucléaires s’arrêtent périodiquement (tous les 12 ou 18 mois) pour recharger leur combustible. La gestion de ce réservoir permet de profils différents d’usages de combustible nucléaire au cours des différentes saisons de l’année. Ainsi, nous nous pencherons sur cette question comme une analyse économique rationnelle de l’opération d’un “réservoir” de combustible nucléaire. Nous allons ensuite l’analyser dans un cadre général déterministe dynamique avec deux types de production : nucléaire et thermique non-nucléaire. Nous étudions la gestion optimale de la production dans un marché parfaitement concurrentiel. Ensuite, nous établissons un modèle numérique (basé sur les données du marché français) où les centrales nucléaires ne sont pas opérées à production constante, mais dans un cadre de placement flexible (comme le parc nucléaire français). […] / Nuclear power as a generation technology that is widely used in electricity production systems is characterized by high fixed costs and low variable costs. To amortize its fixed costs, nuclear is preferentially used for inflexible baseload operation, i.e. operate at a constant level to meet the non variable part of electricity demand of a system and produce at its maximum capacity. Because of this specificity, the insertion of nuclear production in competitive electricity markets has not been deeply studied so far. Therefore, even in competitive markets, the question of the optimal management of a nuclear generation set has not been raised because nuclear production is supposed to operate continuously (to cover baseline demand). However, there are cases where the management of nuclear generation seems more complex than suggested by this simplified view. Typically, when the proportion of nuclear energy in a production set is high, the nuclear generation output has to adjust to the variations in demand. This raises the question of the optimal way to manage this production technology in that kind of setting. As this question has not been studied so far, there is a need for a theoretical framework that enables an analysis of situations like the French one, with a competitive market and where nuclear represents 80% of generation, i.e. much more that what would be necessary to cover the baseload demand. We place ourselves in a medium-term horizon of the management (1 to 3 years) to take into account the seasonal variation of the demand level. In the medium-term, the manager of a large nuclear set (like the French set) has to set its seasonal variation of output according to the demand level. Since nuclear units have to stop periodically (from 12 to 18 months) to reload their fuel, we can analyze the nuclear fuel as a stock behaving like a reservoir. The operation of the reservoir allows different profiles of nuclear fuel use during the different demand seasons of the year. Thus, we will look at this question as a rational economic analysis of the operation of a nuclear fuel “reservoir”. We then analyze it within a general deterministic dynamic framework with two types of generation: nuclear and thermal non-nuclear. We study the optimal management of the production in a perfectly competitive market. Then, we establish a numerical model (based on data from the French market) with nuclear plants being not operated strictly as base load power plants but within a flexible dispatch frame (like the French nuclear set). [...]