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>

This research proposes, implements and analyses a novel framework for multiobjective optimisation through evolutionary computing aimed at, but not restricted to, real-world problems in the engineering design domain. Evolutionary algorithms have been used to tackle a variety of non-linear multiobjective optimisation problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the number of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimising evolutionary algorithm framework, incorporating a genetic algorithm, that uses self-adaptive mutation and crossover in an attempt to avoid such problems, and which has been benchmarked against both standard optimisation test problems in the literature and a real-world airfoil optimisation case. For this last case, the minimisation of drag and maximisation of lift coefficients of a well documented standard airfoil, the framework is integrated with a freeform deformation tool to manage the changes to the section geometry, and XFoil, a tool which evaluates the airfoil in terms of its aerodynamic efficiency. The performance of the framework on this problem is compared with those of two other heuristic MOO algorithms known to perform well, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this framework achieves better or at least no worse convergence. The framework of this research is then considered as a candidate for smart (electricity) grid optimisation. Power networks can be improved in both technical and economical terms by the inclusion of distributed generation which may include renewable energy sources. The essential problem in national power networks is that of power flow and in particular, optimal power flow calculations of alternating (or possibly, direct) current. The aims of this work are to propose and investigate a method to assist in the determination of the composition of optimal or high-performing power networks in terms of the type, number and location of the distributed generators, and to analyse the multi-dimensional results of the evolutionary computation component in order to reveal relationships between the network design vector elements and to identify possible further methods of improving models in future work. The results indicate that the method used is a feasible one for the achievement of these goals, and also for determining optimal flow capacities of transmission lines connecting the bus bars in the network.

Les réseaux de capteurs ont connu un grand essor ces dix dernières années. Ils interviennent dans tous les domaines de notre vie quotidienne et la rendent plus aisée. Malgré ce grand succès des réseaux de capteurs, plusieurs problèmes restent encore ouverts. La capacité énergétique et la fragilité du canal radio des réseaux de capteurs affectent gravement leurs performances. La communication coopérative représente une solution efficace pour lutter contre l'instabilité du canal radio et afin d'économiser plus d'énergie. Nous proposons dans ce manuscrit, d'utiliser la communication coopérative, en premier lieu, au niveau de la couche MAC afin de mettre en place un accès au canal coopératif et non égoïste. En second lieu, nous utilisons la communication coopérative au niveau de la couche réseau dans le but d'établir des chemins de routage plus stables et plus robustes. / Wireless sensor networks (WSN) have known a great development during the last decade. They intervene in all the domain of our everyday life to make it easier. Despite the success of WSN several problems have to be solved. The restricted energy capacity and the randomness of the wireless channel seriously affect the performances of the WSN. Cooperative communication represents an efficient solution to reduce the instability of the wireless channel and to optimize energy. In this thesis we propose to use cooperative communications at the MAC and network layer in order to set up a cooperative access to the channel and to establish more robust routing paths.

Este trabalho propõe o desenvolvimento de Algoritmos Evolutivos (AEs) para estimação dos parâmetros que modelam sinais elétricos (frequência, fase e amplitude) em tempo-real. A abordagem proposta deve ser robusta a ruídos e harmônicos em sinais distorcidos, por exemplo devido à presença de faltas na rede elétrica. AEs mostram vantagens para lidar com tais tipos de sinais. Por outro lado, esses algoritmos quando implementados em software não possibilitam respostas em tempo-real para uso da estimação como relé de frequência ou Unidade de Medição Fasorial. O desenvolvimento em FPGA apresentado nesse trabalho torna possível paralelizar o cálculo da estimação em hardware, viabilizando AEs para análise de sinal elétrico em tempo real. Além disso, mostra-se que AEs multiobjetivos podem extrair informações não evidentes das três fases do sistema e estimar os parâmetros adequadamente mesmo em casos em que as estimativas por fase divirjam entre si. Em outras palavras, as duas principais contribuições computacionais são: a paralelização do AE em hardware por meio de seu desenvolvimento em um circuito de FPGA otimizado a nível de operações lógicas básicas e a modelagem multiobjetiva do problema possibilitando análises dos sinais de cada fase, tanto independentemente quanto de forma agregada. Resultados experimentais mostram superioridade do método proposto em relação ao estimador baseado em transformada de Fourier para determinação de frequência e fase / This work proposes the development of Evolutionary Algorithms (EAs) for the estimation of the basic parameters from electrical signals (frequency, phase and amplitude) in real time. The proposed approach must be robust to noise and harmonics in signals distorted, for example, due to the presence of faults in the electrical network. EAs show advantages for dealing with these types of signals. On the other hand, these algorithms when implemented in software cant produce real-time responses in order to use their estimations as frequency relay or Phasor Measurement Unit. The approach developed on FPGA proposed in this work parallelizes in hardware the process of estimation, enabling analyses of electrical signals in real time. Furthermore, it is shown that multi-objective EAs can extract non-evident information from the three phases of the system and properly estimate parameters even when the phase estimates diverge from each other. This research proposes: the parallelization of an EA in hardware through its design on FPGA circuit optimized at level of basic logic operations and the modeling of the problem enabling multi-objective analyses of the signals from each phase in both independent and aggregate ways. Experimental results show the superiority of the proposed method compared to an estimator based on Fourier transform for determining frequency and phase

Muitos problemas de otimização multi-objetivo utilizam os algoritmos evolutivos para encontrar as melhores soluções. Muitos desses algoritmos empregam as fronteiras de Pareto como estratégia para obter tais soluções. Entretando, conforme relatado na literatura, há a limitação da fronteira para problemas com até três objetivos, podendo tornar seu emprego insatisfatório para os problemas com quatro ou mais objetivos. Além disso, as propostas apresentadas muitas vezes eliminam o emprego dos algoritmos evolutivos, os quais utilizam tais fronteiras. Entretanto, as características dos algoritmos evolutivos os qualificam para ser empregados em problemas de otimização, como já vem sendo difundido pela literatura, evitando eliminá-lo por causa da limitação das fronteiras de Pareto. Assim sendo, neste trabalho se buscou eliminar as fronteiras de Pareto e para isso utilizou a lógica Fuzzy, mantendo-se assim o emprego dos algoritmos evolutivos. O problema escolhido para investigar essa substituição foi o problema de predição de estrutura terciária de proteínas, pois além de se encontrar em aberto é de suma relevância para a área de bioinformática. / Several multi-objective optimization problems utilize evolutionary algorithms to find the best solution. Some of these algoritms make use of the Pareto front as a strategy to find these solutions. However, according to the literature, the Pareto front limitation for problems with up to three objectives can make its employment unsatisfactory in problems with four or more objectives. Moreover, many authors, in most cases, propose to remove the evolutionay algorithms because of Pareto front limitation. Nevertheless, characteristics of evolutionay algorithms qualify them to be employed in optimization problems, as it has being spread out by literature, preventing to eliminate it because the Pareto front elimination. Thus being, this work investigated to remove the Pareto front and for this utilized the Fuzzy logic, remaining itself thus the employ of evolutionary algorithms. The choice problem to investigate this remove was the protein tertiary structure prediction, because it is a open problem and extremely relevance to bioinformatic area.

This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this type of problem, there is no unique solution. However, out of many available options, the Decision Maker eventually has to choose only one solution. With this problem as the motivation, the current study develops a compromise ranking algorithm, namely a trade-off ranking method. The trade-off ranking method able to give a trade-off solution with the least compromise compared to other choices as the best solution. The properties of the algorithm are studied in the thesis on several test cases. The proposed method is compared against several multi-criteria decision making methods with ranking based on the distance measure, which are the TOPSIS, relative distance and VIKOR. The sensitivity analysis and uncertainty test are carried out to examine the methods robustness. A critical criteria analysis is also done to test for the most critical criterion in a multi-criteria problem. The decision making method is considered further in a fuzzy environment problem where the fuzzy trade-off ranking is developed and compared against existing fuzzy decision making methods.

A demanda criada pelos estudos biológicos resultou para predição da estrutura terciária de proteínas ser uma alternativa, uma vez que menos de 1% das sequências conhecidas possuem sua estrutura terciária determinada experimentalmente. As predições Ab initio foca nas funções baseadas da física, a qual se trata apenas das informações providas pela sequência primária. Por consequência, um espaço de busca com muitos mínimos locais ótimos deve ser pesquisado. Este cenário complexo evidencia uma carência de algoritmos eficientes para este espaço, tornando-se assim o principal obstáculo para este tipo de predição. A optimização Multi-Objetiva, principalmente os Algoritmos Evolutivos, vem sendo aplicados na predição da estrutura terciária já que na mesma se envolve um compromisso entre os objetivos. Este trabalho apresenta o framework ProtPred-PEO-GROMACS, ou simplesmente 3PG, que não somente faz predições com a mesma acurácia encontrada na literatura, mas também, permite investigar a predição por meio da manipulação de combinações de objetivos, tanto no aspecto energético quanto no estrutural. Além disso, o 3PG facilita a implementação de novas opções, métodos de análises e também novos algoritmos evolutivos. A fim de salientar a capacidade do 3PG, foi então discorrida uma comparação entre os algoritmos NSGA-II e SPEA2 aplicados na predição Ab initio da estrutura terciária de proteínas em seis combinações de objetivos. Ademais, o uso da técnica de refinamento por Dinâmica Molecular é avaliado. Os resultados foram adequados quando comparado com outras técnicas de predições: Algoritmos Evolutivo Multi-Objetivo, Replica Exchange Molecular Dynamics, PEP-FOLD e Folding@Home. / The demand created by biological studies resulted the structure prediction as an alternative, since less than 1% of the known protein primary sequences have their 3D structure experimentally determined. Ab initio predictions focus on physics-based functions, which regard only information about the primary sequence. As a consequence, a search space with several local optima must be sampled, leading to insucient sampling of this space, which is the main hindrance towards better predictions. Multi-Objective Optimization approaches, particularly the Evolutionary Algorithms, have been applied in protein structure prediction as it involves a compromise among conicting objectives. In this paper we present the ProtPred-PEO-GROMACS framework, or 3PG, which can not only make protein structure predictions with the same accuracy standards as those found in the literature, but also allows the study of protein structures by handling several energetic and structural objective combinations. Moreover, the 3PG framework facilitates the fast implementation of new objective options, method analysis and even new evolutionary algorithms. In this study, we perform a comparison between the NSGA-II and SPEA2 algorithms applied on six dierent combinations of objectives to the protein structure. Besides, the use of Molecular Dynamics simulations as a renement technique is assessed. The results were suitable when comparated with other prediction methodologies, such as: Multi-Objective Evolutionary Algorithms, Replica Exchange Molecular Dynamics, PEP-FOLD and Folding@Home.

O problema de Planejamento da Expansão de Sistemas de Distribuição (PESD) visa determinar diretrizes para a expansão da rede considerando a crescente demanda dos consumidores. Nesse contexto, as empresas distribuidoras de energia elétrica têm o papel de propor ações no sistema de distribuição com o intuito de adequar o fornecimento da energia aos padrões exigidos pelos órgãos reguladores. Tradicionalmente considera-se apenas a minimização do custo global de investimento de planos de expansão, negligenciando-se questões de confiabilidade e robustez do sistema. Como consequência, os planos de expansão obtidos levam o sistema de distribuição a configurações que são vulneráveis a elevados cortes de carga na ocorrência de contingências na rede. Este trabalho busca a elaboração de uma metodologia para inserir questões de confiabilidade e risco ao problema PESD tradicional, com o intuito de escolher planos de expansão que maximizem a robustez da rede e, consequentemente, atenuar os danos causados pelas contingências no sistema. Formulou-se um modelo multiobjetivo do problema PESD em que se minimizam dois objetivos: o custo global (que incorpora custo de investimento, custo de manutenção, custo de operação e custo de produção de energia) e o risco de implantação de planos de expansão. Para ambos os objetivos, são formulados modelos lineares inteiros mistos que são resolvidos utilizando o solver CPLEX através do software GAMS. Para administrar a busca por soluções ótimas, optou-se por programar em linguagem C++ dois Algoritmos Evolutivos: Non-dominated Sorting Genetic Algorithm-2 (NSGA2) e Strength Pareto Evolutionary Algorithm-2 (SPEA2). Esses algoritmos mostraram-se eficazes nessa busca, o que foi constatado através de simulações do planejamento da expansão de dois sistemas testes adaptados da literatura. O conjunto de soluções encontradas nas simulações contém planos de expansão com diferentes níveis de custo global e de risco de implantação, destacando a diversidade das soluções propostas. Algumas dessas topologias são ilustradas para se evidenciar suas diferenças. / The Distribution System Expansion Planning (DSEP) problem aims to determine guidelines to expand the network considering the growing demand of customers. In this context, the distribution companies have to propose actions for improvements in the distribution system in order to adjust the supply of energy to the standards required by regulators. Traditionally minimizing the global cost of expansion plans is the only goal that is considered, thus reliability and robustness issues are neglected. As a result, the optimal expansion plans lead the distribution system to configurations that are vulnerable to high load shedding under the occurrence of contingencies in the network. This work aims to develop a methodology to insert reliability and risk issues to the traditional DSEP problem in order to maximize the robustness of the network and hence mitigate the system damages caused by contingencies. We formulated a multi-objective model of the problem that compromises two objectives: minimization of the global cost (that comprises investment cost, maintenance cost, operational cost, and production cost) and minimization of the deployment risk of expansion plans. For both objectives, we formulated mixed integer linear models which are solved using CPLEX accessed through GAMS. To manage the search for optimal solutions, we chose to implement in C++ language two Evolutionary Algorithms (EAs): Non-dominated Sorting Genetic Algorithm-2 (NSGA2) and Strength Pareto Evolutionary Algorithm-2 (SPEA2). The effectiveness of both algorithms was verified through simulations of the expansion planning of two test systems, adapted from the literature. The set of solutions that has been found contains expansion plans with different levels of global cost and deployment risk. Some of these topologies are depicted to show this diversity of the proposed solutions.

O problema de Planejamento da Expansão de Sistemas de Distribuição (PESD) visa determinar diretrizes para a expansão da rede considerando a crescente demanda dos consumidores. Nesse contexto, as empresas distribuidoras de energia elétrica têm o papel de propor ações no sistema de distribuição com o intuito de adequar o fornecimento da energia aos padrões exigidos pelos órgãos reguladores. Tradicionalmente considera-se apenas a minimização do custo global de investimento de planos de expansão, negligenciando-se questões de confiabilidade e robustez do sistema. Como consequência, os planos de expansão obtidos levam o sistema de distribuição a configurações que são vulneráveis a elevados cortes de carga na ocorrência de contingências na rede. Este trabalho busca a elaboração de uma metodologia para inserir questões de confiabilidade e risco ao problema PESD tradicional, com o intuito de escolher planos de expansão que maximizem a robustez da rede e, consequentemente, atenuar os danos causados pelas contingências no sistema. Formulou-se um modelo multiobjetivo do problema PESD em que se minimizam dois objetivos: o custo global (que incorpora custo de investimento, custo de manutenção, custo de operação e custo de produção de energia) e o risco de implantação de planos de expansão. Para ambos os objetivos, são formulados modelos lineares inteiros mistos que são resolvidos utilizando o solver CPLEX através do software GAMS. Para administrar a busca por soluções ótimas, optou-se por programar em linguagem C++ dois Algoritmos Evolutivos: Non-dominated Sorting Genetic Algorithm-2 (NSGA2) e Strength Pareto Evolutionary Algorithm-2 (SPEA2). Esses algoritmos mostraram-se eficazes nessa busca, o que foi constatado através de simulações do planejamento da expansão de dois sistemas testes adaptados da literatura. O conjunto de soluções encontradas nas simulações contém planos de expansão com diferentes níveis de custo global e de risco de implantação, destacando a diversidade das soluções propostas. Algumas dessas topologias são ilustradas para se evidenciar suas diferenças. / The Distribution System Expansion Planning (DSEP) problem aims to determine guidelines to expand the network considering the growing demand of customers. In this context, the distribution companies have to propose actions for improvements in the distribution system in order to adjust the supply of energy to the standards required by regulators. Traditionally minimizing the global cost of expansion plans is the only goal that is considered, thus reliability and robustness issues are neglected. As a result, the optimal expansion plans lead the distribution system to configurations that are vulnerable to high load shedding under the occurrence of contingencies in the network. This work aims to develop a methodology to insert reliability and risk issues to the traditional DSEP problem in order to maximize the robustness of the network and hence mitigate the system damages caused by contingencies. We formulated a multi-objective model of the problem that compromises two objectives: minimization of the global cost (that comprises investment cost, maintenance cost, operational cost, and production cost) and minimization of the deployment risk of expansion plans. For both objectives, we formulated mixed integer linear models which are solved using CPLEX accessed through GAMS. To manage the search for optimal solutions, we chose to implement in C++ language two Evolutionary Algorithms (EAs): Non-dominated Sorting Genetic Algorithm-2 (NSGA2) and Strength Pareto Evolutionary Algorithm-2 (SPEA2). The effectiveness of both algorithms was verified through simulations of the expansion planning of two test systems, adapted from the literature. The set of solutions that has been found contains expansion plans with different levels of global cost and deployment risk. Some of these topologies are depicted to show this diversity of the proposed solutions.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A teoria dos jogos à um ramo da matemÃtica dedicado à anÃlise das interaÃÃes entre elementos concorrentes, que se encontram em situaÃÃo de conflito, e à formulaÃÃo de estratÃgias de decisÃo. O potencial de aplicaÃÃo desta teoria em sistema de comunicaÃÃo mÃvel à considerado, jà que em alguns dos problemas podem ser identificados elementos em situaÃÃo de conflito. Dois problemas sÃo aqui abordados, a saber, o controle de potencia de transmissÃo e a equalizaÃÃo adaptativa de canal. Ambos estÃo relacionados à interferÃncia, que à um dos mais importantes fatores limitantes do desempenho de sistemas de telefonia celular. O controle de potÃncia de transmissÃo consiste em um procedimento de gerenciamento da interferÃncia de mÃltiplo acesso. Uma nova abordagem deste problema, via teoria dos jogos à considerada nesta dissertaÃÃo, resultando em uma nova deduÃÃo do algoritmo clÃssico de controle de potÃncia DPC (do inglÃs Distributed Power Control). Um novo algoritmo denominado GT-DPC ( do inglÃs Game â Theoretic Distributed Power Control), à desenvolvido e se revela uma forma geral do algoritmo DPC. O algoritmo GT-DPC se mostra mais eficiente em termos de energia do que os algritmos convencionais para serviÃos de qualidade flexÃvel( melhor esforÃo), isto Ã, para um mesmo nÃvel de potÃncia de transmissÃo mais altas do que o DPC. AlÃm disso, este algoritmo permite o gerenciamento dos recursos de potÃncia em cenÃrios de coexistÃncia de serviÃos com diferentes caracterÃsticas. Neste caso, o Lgoritmo à denominado GT-MSDPC ( do inglÃs Game-Theoretic Multi- Service Distributed Power Control).O desempenho dos algoritmos propostos para sistemas de serviÃos à avaliado atravÃs de simulaÃÃes computacionais que emulam os sistemas celulares TDMA( do inglÃs Time Division Multiple Acess) e CDMA(do inglÃs Code Division Multiple Acess). A aplicaÃÃo da teoria dos jogos à equalizaÃÃo adaptativa de canal, que à o procedimento de combate à interferÃncia entre sÃmbolos, està relacionada a situaÃÃes de pior caso. O filtro H( filtro robusto) à derivado atravÃs da aplicaÃÃo de conceitos da teoria dos jogos. AlÃm disso, suas interrelaÃÃes com o filtro de Kalman(RLS) sÃo apresentadas. Por meio de simulaÃÃes computacionais que emulam o sitema de telefonia celular GSM(do inglÃs Global System for Mobile Communications), ambos os fltros tÃm se desempenho como equalizador adaptativo de canal avaliado em dois diferentes cenÃrios. No primeiro deles, diferentes velocidades sÃo atribuÃdas ao usuÃrio, e os resultados mostram que o Rls e o equalizador H apresentam desempenhos comparÃveis. No segundo, considera-se a presenÃa de ruÃdo impulsivo, que pode ser uma consequÃncia do assincronismo de interferÃncia d mÃltiplo acesso, ou que pode ter fontes externas ao sistema de comunicaÃÃo, como a igniÃÃo de motores, llinhas de transmissÃo de energia, fornos de microondas, entre outros. Neste segundo cenÃrio, a robustez do equalizador H fica demonstrda, assim como a degradaÃÃo do desempenho do RLS. Um equalizador hÃbrido RLS-H à proposto, com a obtenÃÃo de ganhos expressivos com respeito ao equalizador RLS convencional. / Game theory is a branch of the Mathematics concerned with the analysis of interactions between competing elements, which are found in conflicting situations, and concerned with the formulation of decision strtegies. This theory is potentially applicable to communications systems problems, since elements in conflicting situations can be identified in some of such problems.Two problems are here considered: the transmit power control and the adaptive channel equalization. Both problems are related to interference, which is one of the most important limiting factors for the cellular system perfomance. Transmit power control consist of a procedure for multi-acess interference management. A new game theoretical approach to power control problem is considered, resulting in a new way to deduce the classical power control algorithm DPC(Distributed Power Con trol). A new algorithm, denoted GT-DPC(Game-theoretic Distributed Power Control), is developed and can be seen as a general form of DPC algorithms for best effort services, since for a unique transmit power level it provides data rates higher than DPC. Furthermore, it allows the power resource management in the presence of services, since for a unique transmit power level it provides data rates higher than DPC. Furthermore, it allows the power resource management in the presence of services with different characteristics. In this case, the algorithm is denoted GT-MSDPC(Game-Theoretic Multi-Service Distributed Power Control). The perfomace of the proposed algorithms for single-service and multi-service systems is demostrated through computational experiments whch simulate TDMA(Time Division Multiple Acess) and CDMA(Code Division Multiple Acess) cellular Systems. The game theory application to adaptive equalization, which is the procedure to combat the intersymbol interference, is related to worst case situations. The H filter(robust filter) is deduced by applying game-theoretic concepts. Furthermore, their relations with the Kalman filter are presented. Through computational experiments wihch simulate GSM(Global System for Mobile Communications) cellular system, both filters have their perfomance as adaptive channel equalizers valued in two different scenarios. In the first one,different speeds are attributed to the user, and results show that both RLS and H equalizer present similar perfomances. In the secon scenario, impulsive noise is considered. Impulsive noise may have external sources, as motors ignition, energy transmission lines or microwaves ovens. In this scenario, the H equalizer robustness is demontrated, so as the RLS perfomance degradation. A hybrid RLS-H equalizer is proposed, obtaining expressive gains with respect to conventional RLS equalizer.