A dissertação trata da aplicação de técnicas de programação não linear para otimização da operação de sistemas hidroenergéticos. Foi analisado o desempenho de diferentes funções objetivo para a otimização energética, assim como de regras para deplecionamento de sistemas hidroenergéticos com configurações em paralelo e em série. Neste caso, foi utilizado um sistema hipotético de reservatórios para permitir maior adequação do sistema aos conceitos que foram testados. O sistema Paranapanema foi escolhido como caso de estudo. Para esta bacia discutem-se aspectos ligados às séries hidrológicas, que constituem o principal dado de entrada do processo de otimização. A aplicação ao sistema Paranapanema foi efetuada com otimização de períodos de até 20 anos. Foram definidas novas regras baseadas na observação dos resultados da otimização de longo período e também apresentados padrões de variação da geração e dos volumes dos reservatórios. Foram feitas comparações com a operação real observada. Apresentou-se uma aplicação à operação de tempo real, que define políticas de operação para curto prazo. Essa aplicação tem horizonte de 12 meses, partindo-se do mês atual e considerando cenários futuros baseados em prognósticos climáticos. Os resultados mostram que a partir das regras definidas é possível ter um entendimento do processo de otimização. Os padrões de variação da geração e dos volumes auxiliam na elaboração de políticas de operação. Dessa forma se tem uma visão abrangente da otimização de um sistema hidroenergético considerando os resultados da otimização dentro de um ciclo sazonal conjuntamente com as implicações da otimização de longo período. / An application analysis of nonlinear programming is developed for hydropower systems optimization. The Paranapanema system is chosen like case study and treats aspects tied to hydrological series, that constitute the main input of optimization process. Different objective functions performance are analyzed for the hydropower optimization. It analyzes rules for emptying of hydropower reservoirs with configurations in parallel and in series. In this case a hypothetical system is used to allow larger system adaptation to the concepts that are tested. An application to the Paranapanema system is made, working with periods optimization of up to 20 years. New rules are defined based on the observation of the results of long period optimization. Also variation standards of power generation and reservoirs volumes are introduced. Comparisons with the observed operation are made. An application to real time operation is made, that defines operation policies for short term. This application has horizon of 12 months, departing of the current month and considering future scenarios based on climate prediction. The results show that is possible to have an optimization process understanding from the defined rules. The generation and volumes variation standards assist in the operation policies elaboration. Thus it has a general vision of hydropower system optimization, considering the results of optimization inside seasonal cycle jointly with the optimization long period implications.

A dissertação trata da aplicação de técnicas de programação não linear para otimização da operação de sistemas hidroenergéticos. Foi analisado o desempenho de diferentes funções objetivo para a otimização energética, assim como de regras para deplecionamento de sistemas hidroenergéticos com configurações em paralelo e em série. Neste caso, foi utilizado um sistema hipotético de reservatórios para permitir maior adequação do sistema aos conceitos que foram testados. O sistema Paranapanema foi escolhido como caso de estudo. Para esta bacia discutem-se aspectos ligados às séries hidrológicas, que constituem o principal dado de entrada do processo de otimização. A aplicação ao sistema Paranapanema foi efetuada com otimização de períodos de até 20 anos. Foram definidas novas regras baseadas na observação dos resultados da otimização de longo período e também apresentados padrões de variação da geração e dos volumes dos reservatórios. Foram feitas comparações com a operação real observada. Apresentou-se uma aplicação à operação de tempo real, que define políticas de operação para curto prazo. Essa aplicação tem horizonte de 12 meses, partindo-se do mês atual e considerando cenários futuros baseados em prognósticos climáticos. Os resultados mostram que a partir das regras definidas é possível ter um entendimento do processo de otimização. Os padrões de variação da geração e dos volumes auxiliam na elaboração de políticas de operação. Dessa forma se tem uma visão abrangente da otimização de um sistema hidroenergético considerando os resultados da otimização dentro de um ciclo sazonal conjuntamente com as implicações da otimização de longo período. / An application analysis of nonlinear programming is developed for hydropower systems optimization. The Paranapanema system is chosen like case study and treats aspects tied to hydrological series, that constitute the main input of optimization process. Different objective functions performance are analyzed for the hydropower optimization. It analyzes rules for emptying of hydropower reservoirs with configurations in parallel and in series. In this case a hypothetical system is used to allow larger system adaptation to the concepts that are tested. An application to the Paranapanema system is made, working with periods optimization of up to 20 years. New rules are defined based on the observation of the results of long period optimization. Also variation standards of power generation and reservoirs volumes are introduced. Comparisons with the observed operation are made. An application to real time operation is made, that defines operation policies for short term. This application has horizon of 12 months, departing of the current month and considering future scenarios based on climate prediction. The results show that is possible to have an optimization process understanding from the defined rules. The generation and volumes variation standards assist in the operation policies elaboration. Thus it has a general vision of hydropower system optimization, considering the results of optimization inside seasonal cycle jointly with the optimization long period implications.

The existing environmental threats and the ever increasing global dependence on electric power highlight the importance of producing power in a sustainable manner. In accordance, it is vital to attract investments in electricity generation projects based on renewable energy sources, also called renewable energy projects (REP). This poses a challenge, as REP tend to be less financially competitive than their fossil fuel based counterparts. Moreover, the power grid has to be upgraded to integrate large amounts of RESs in an efficient and economic manner. An appealing alternative to enhance the financial appealing of REP is to improve the techniques used for their assessment. These tools produce robust and economically sound assessments, but tend to undervalue REP and other projects under uncertainty, as they neglect the flexibility of the projects to be adjusted in response to uncertainty. This can be corrected by extending the tools with the aid of real options (RO) theory. RO theory can be used to extend assessment techniques to value flexibility derived from the projects, their management, and even their environment, which can be used to enhance the financial value of REP in the changing power sector. In addition, the scope of RO theory is increasing to address flexibility in the design of the projects. Therefore, the theory can drive investments in REP and motivate the design of more profitable projects. This research project seeks to analyse the potential of RO theory to increase the financial worth of different types of REP in the current and changing power sector. The novelties of this research are that it expands RO theory by addressing the flexibility within the design of the projects, the potential of RO theory to manage uncertainties that are exclusive to the projects or typical in the power sector, and other relevant areas of research interest. The research produced several RO methodologies to model the planning, operation, and design of hydropower projects, wind power projects, and solar photovoltaic projects in existing power sector environments and environments characterised by high penetration of RESs and consumers with demand response capabilities. The results demonstrate the applicability of RO theory to enhance the financial value of different types of REP under a wide range of circumstances.

Este trabalho de pesquisa está relacionado com a aplicação de técnicas de Redes Neurais Artificiais (RNA\'s) na obtenção de políticas de operação conjunta de usinas hidroelétricas (UH\'s). O comportamento ótimo das usinas hidroelétricas depende tanto da posição relativa de cada UH ao longo da cascata como da relação entre elas. O propósito principal deste trabalho é de aplicar a rede de mapa auto-organizável (SOM) de Kohonen com modelos de sistemas dinâmicos, no aprendizado destas relações, objetivando seu uso para simular a operação ótima de um sistema hidroelétrico real. Este modelo é uma versão não-supervisionada do modelo NARX sendo chamado de modelo ou rede NARX Auto-Organizável (SONARX), capaz de processar padrões espaço-temporais. Desta maneira, um algoritmo de programação não-linear, especialmente desenvolvido para a realização da operação determinística de sistemas hidrotérmicos de potência, é utilizado para otimizar a operação deste conjunto de usinas, considerando-se várias condições hidrológicas distintas e, tendo como função objetivo a minimização do custo com complementação térmica. Em seguida, os resultados desta otimização são então processados e utilizados no treinamento da rede SONARX; ela irá captar os comportamentos relativos de cada uma das UH\'s, trabalhando com afluências futuras que ela mesma irá prever. De forma geral, a partir dos resultados obtidos com um subsistema do sistema sudeste brasileiro, pode-se concluir que a rede SONARX conseguiu assimilar o comportamento ótimo da operação do sistema teste. Os testes mostraram que o previsor neural possui uma forte tendência em seguir os resultados da otimização determinística, otimizando o uso dos recursos hídricos disponíveis para geração de energia elétrica. Posteriormente, a rede neural com função de base radial (RBF) foi aplicada na interpolação das previsões feitas pela rede SONARX, uma vez que este tipo de rede é indicado para esta tarefa. Neste contexto, os centros das funções de base, da rede RBF, passam a ser definidos pela rede SONARX, mitigando o erro inerente a este tipo de modelagem baseada na técnica de MATQV. / This research work is related to the application of Artificial Neural Networks (ANN) in the obtaining of united operation policies in hydroelectric power plants (HP). The optimal behavior of the hydroelectric power plants depends as much on the relative position of each station throughout the cascade as on the relation between them. The main purpose of this work is to apply the self-organizing map network (SOM) of Kohonem with dynamic system models, in the learning of these relations, objectifying its usage to simulate the optimal operation of a real hydroelectric power system. This model is an unsupervised version of the NARX model; which has been called self-organized model or network (SONARX), capable of processing space-time patterns. This way, a nonlinear programming algorithm, especially developed to accomplish the hydrothermal power system deterministic operation, is used to optimize the operation of this group of hydropower plants, considering many distinct hydrological conditions, and aiming the minimized cost with the thermal complementation. After this, the results of this optimization are processed and used in the SONARX network training; it will captivate the relative behavior of each one of the (HP), working with future affluences that it will predict. In a general way, from the acquired results from a subsystem of the brazilian southeast system, we may conclude that the SONARX network managed to assimilate the optimal behavior of the system test operation. The tests showed that the neural predictor has a high tendency towards deterministic optimization results, optimizing the water resources use available for the electric energy generation. After all this, the radial basis function (RBF) network was applied on the interpolation of the predictions made by the SONARX network, once this kind of network is indicated to this task. On this context, the centers of the basis functions, of RBF network, are to be defined by the SONARX network, mitigating the unrelated error to this type of modeling based on MATQV technique.

Climate change is altering hydrological processes with varying degrees in various regions of the world. This research work investigates the possible impacts of climate change on water resource and Hydropower production potential in central and southern Africa. The Congo, Zambezi and Kwanza, Shire, Kafue and Kabompo basins that lie in central and southern Africa are used as case studies. The review of climate change impact studies shows that there are few studies on impacts of climate change on hydropower production. Most of these studies were carried out in Europe and north America and very few in Asia, south America and Africa. The few studies indicate that southern Africa would experience reduction in precipitation and runoff, consequently reductions in hydropower production. There are no standard methods of assessing the resulting impacts. Two approaches were used to assess the impacts of climate change on water resources and hydropower. One approach is lumping changes on country or regional level and use the mean climate changes on mean annual flows as the basis for regional changes in hydropower production. This is done to get an overall picture of the changes on global and regional level. The second approach is a detailed assessment process in which downscaling, hydrological modelling and hydropower simulations are carried out. The possible future climate scenarios for the region of central and southern Africa depicted that some areas where precipitation are likely to have increases while other, precipitation will reduce. The region northern Zambia and southern Congo showed increases while the northern Congo basin showed reductions. Further south in southern African region, there is a tendency of decreases in precipitation. To the west, in Angola, inland showed increases while towards the coast highlighted some decreases in precipitation. On a global scale, hydropower is likely to experience slight changes (0.08%) due to climate change by 2050. Africa is projected for a slight decrease (0.05%), Asia with an increase of 0.27%, Europe a reduction up to 0.16% while America is projected to have an increase of 0.05%. In the eastern African region, it was shown that hydropower production is likely to increase by 0.59%, the central with 0.22% and the western with a 0.03%. The southern, and northern African regions were projected to have reductions of 0.83% and 0.48% respectively. The basins with increases in flow projections have a slight increase on hydropower production but not proportional to the increase in precipitation. The basins with decreases had even high change as the reduction was further increased by evaporation losses. The hydropower production potential of most of southern African basins is likely to decrease in the future due to the impact of climate change while the central African region shows an increasing trend. The hydropower system in these regions will be affected consequently. The hydropower production changes will vary from basin to basin in these regions. The Zambezi, Kafue and Shire river basins have negative changes while the Congo, Kwanza and Kabompo river basins have positive changes. The hydropower production potential in the Zambezi basin decreases by 9 - 34%. The hydropower production potential in the Kafue basin decreases by 8 - 34% and the Shire basin decreases by 7 - 14 %. The southern region will become drier with shorter rainy seasons. The central region will become wetter with increased runoff. The hydropower production potential in the Congo basin reduces slightly and then increases by 4% by the end of the century. The hydropower production potential in the Kwanza basin decreases by 3% and then increases by 10% towards the end of the century and the Kabompo basin production increases by 6 - 18%. It can be concluded that in the central African region hydropower production will, in general, increase while the southern African region, hydropower production will decrease. In summary, the analysis has shown that the southern African region is expected to experience decreases in rainfall and increases in temperature. This will result in reduced runoff. However the northern part of southern Africa is expected to remain relatively the same with slight increase, moving northwards towards the central African region where mainly increases have been registered. The southern African region is likely to experience reductions up to 5 - 20% while the central African region is likely to experience an increase in runoff in the range of 1 - 5%. Lack of data was observed as a critical limiting factor in modelling in the central and southern Africa region. The designs, plans and operations based on poor hydrological data severely compromise performance and decrease efficiency of systems. Climate change is expected to change these risks. The normal extrapolations of historical data will be less reliable as the past will become an increasingly poor predictor of the future. Better (observed) data is recommended in future assessments and if not better tools and methods for data collection/ should be used. Future designs, plans and operations should include and aspect of climate change, if the region is to benefit from the climate change impacts.

Este trabalho de pesquisa está relacionado com a aplicação de técnicas de Redes Neurais Artificiais (RNA\'s) na obtenção de políticas de operação conjunta de usinas hidroelétricas (UH\'s). O comportamento ótimo das usinas hidroelétricas depende tanto da posição relativa de cada UH ao longo da cascata como da relação entre elas. O propósito principal deste trabalho é de aplicar a rede de mapa auto-organizável (SOM) de Kohonen com modelos de sistemas dinâmicos, no aprendizado destas relações, objetivando seu uso para simular a operação ótima de um sistema hidroelétrico real. Este modelo é uma versão não-supervisionada do modelo NARX sendo chamado de modelo ou rede NARX Auto-Organizável (SONARX), capaz de processar padrões espaço-temporais. Desta maneira, um algoritmo de programação não-linear, especialmente desenvolvido para a realização da operação determinística de sistemas hidrotérmicos de potência, é utilizado para otimizar a operação deste conjunto de usinas, considerando-se várias condições hidrológicas distintas e, tendo como função objetivo a minimização do custo com complementação térmica. Em seguida, os resultados desta otimização são então processados e utilizados no treinamento da rede SONARX; ela irá captar os comportamentos relativos de cada uma das UH\'s, trabalhando com afluências futuras que ela mesma irá prever. De forma geral, a partir dos resultados obtidos com um subsistema do sistema sudeste brasileiro, pode-se concluir que a rede SONARX conseguiu assimilar o comportamento ótimo da operação do sistema teste. Os testes mostraram que o previsor neural possui uma forte tendência em seguir os resultados da otimização determinística, otimizando o uso dos recursos hídricos disponíveis para geração de energia elétrica. Posteriormente, a rede neural com função de base radial (RBF) foi aplicada na interpolação das previsões feitas pela rede SONARX, uma vez que este tipo de rede é indicado para esta tarefa. Neste contexto, os centros das funções de base, da rede RBF, passam a ser definidos pela rede SONARX, mitigando o erro inerente a este tipo de modelagem baseada na técnica de MATQV. / This research work is related to the application of Artificial Neural Networks (ANN) in the obtaining of united operation policies in hydroelectric power plants (HP). The optimal behavior of the hydroelectric power plants depends as much on the relative position of each station throughout the cascade as on the relation between them. The main purpose of this work is to apply the self-organizing map network (SOM) of Kohonem with dynamic system models, in the learning of these relations, objectifying its usage to simulate the optimal operation of a real hydroelectric power system. This model is an unsupervised version of the NARX model; which has been called self-organized model or network (SONARX), capable of processing space-time patterns. This way, a nonlinear programming algorithm, especially developed to accomplish the hydrothermal power system deterministic operation, is used to optimize the operation of this group of hydropower plants, considering many distinct hydrological conditions, and aiming the minimized cost with the thermal complementation. After this, the results of this optimization are processed and used in the SONARX network training; it will captivate the relative behavior of each one of the (HP), working with future affluences that it will predict. In a general way, from the acquired results from a subsystem of the brazilian southeast system, we may conclude that the SONARX network managed to assimilate the optimal behavior of the system test operation. The tests showed that the neural predictor has a high tendency towards deterministic optimization results, optimizing the water resources use available for the electric energy generation. After all this, the radial basis function (RBF) network was applied on the interpolation of the predictions made by the SONARX network, once this kind of network is indicated to this task. On this context, the centers of the basis functions, of RBF network, are to be defined by the SONARX network, mitigating the unrelated error to this type of modeling based on MATQV technique.