Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-28T12:04:17Z No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 13228407 bytes, checksum: 1e92e8c2fa686ddcaea1c9ed0d33b278 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-28T12:16:14Z (GMT) No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 13228407 bytes, checksum: 1e92e8c2fa686ddcaea1c9ed0d33b278 (MD5) / Made available in DSpace on 2016-07-28T12:16:14Z (GMT). No. of bitstreams: 1 rafaelbrunodasilvabrandi.pdf: 13228407 bytes, checksum: 1e92e8c2fa686ddcaea1c9ed0d33b278 (MD5) Previous issue date: 2016-02-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / O Sistema Elétrico Brasileiro (SEB) apresenta características peculiares devido às grandes dimensões do país e pelo fato da geração elétrica ser proveniente predominantemente de usinas hidráulicas. Como as afluências a estas usinas possuem comportamento estocástico e grandes reservatórios proporcionam ao sistema a capacidade de uma regularização plurianual, a utilização dos recursos hidráulicos deve ser planejada de forma minuciosa em um horizonte de tamanho considerável. Assim, o planejamento da operação de médio prazo compreende um período de 5 a 10 anos com discretização mensal e é realizado por uma cadeia de modelos computacionais tal que o principal modelo desta cadeia é baseado na técnica da Programação Dinâmica Dual Estocástica (PDDE). O objetivo deste trabalho é obter avanços nas metodologias de programação dinâmica atualmente utilizadas. Partindo-se da utilização da inserção iterativa de cortes, implementa-se um modelo computacional para o planejamento da operação de médio prazo baseado na metodologia de Programação Dinâmica Estocástica (PDE) utilizando uma discretização mais eficiente do espaço de estados (PDEE). Além disso, a metodologia proposta de PDE possui um critério de convergência bem definido para o problema, de forma que a inclusão da medida de risco CVaR não altera o processo de avaliação da convergência de forma significante. Dado que a inclusão desta medida de risco à PDDE convencional dificulta a avaliação da convergência do processo pela dificuldade da estimação de um limite superior válido, o critério de convergência proposto na PDEE é, então, base para um novo critério de convergência para a PDDE tal que pode ser aplicado mesmo na consideração do CVaR e não aumenta o custo computacional envolvido. Adicionalmente, obtém-se um critério de convergência mais detalhado em que as séries utilizadas para amostras de afluência podem ser avaliadas individualmente tais que aquelas que, em certo momento, não contribuam de forma determinante para a convergência podem ser descartadas do processo, diminuindo o tempo computacional, ou ainda serem substituídas por novas séries dentro de uma reamostragem mais seletiva dos cenários utilizados na PDDE. As metodologias propostas foram aplicadas para o cálculo do planejamento de médio prazo do SIN baseando-se em subsistemas equivalentes de energia. Observa-se uma melhoria no algoritmo base utilizado para a PDE e que o critério proposto para convergência da PDDE possui validade mesmo quando CVaR é considerado na modelagem. / The Brazilian National Grid (BNG) presents peculiar characteristics due to its huge territory dimensions and hydro-generation predominancy. As the water inflows to these plants are stochastic and a pluriannual regularization for system storage capacity is provided, the use of hydro-generation must be planned in an accurate manner such that it considersalongplanningperiod. So, thelong-termoperationplanning(LTOP)problemis generallysolvedbyachainofcomputationalmodelsthatconsideraperiodof5to10years ahead such that the primary model of this chain is based on Stochastic Dual Dynamic Programming (SDDP) technique. The main contribution of this thesis is to propose some improvements in Stochastic Dynamic Programming techniques usually settled on solving LTOP problems. In the fashion of an iterative cut selection, it is firstly proposed a LTOP problem solution model that uses an ecient state space discretization for Stochastic Dynamic Programming (SDP), called ESDP. The proposed model of SDP has a welldefined convergence criterion such that including CVaR does not hinder convergence analysis. Due to the lack of good upper bound estimators in SDDP when including CVaR, additional issues are encountered on defining a convergence criterion. So, based on ESDP convergence analysis, a new criterion for SDDP convergence is proposed such that it can be used regardless of CVaR representation with no extra computational burden. Moreover, the proposed convergence criterion for SDDP has a more detailed description such that forward paths can be individually assessed and then be accordingly discarded for computational time reduction, or even define paths to be replaced in a more particular resampling scheme in SDDP. Based on aggregate reservoir representation, the proposed methodsofconvergenceofSDDPandtheESDPwereappliedonLTOPproblemsrelatedto BNG. Results show improvements in SDDP based technique and eectiveness of proposed convergence criterion for SDDP when CVaR is used.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-20T13:40:45Z No. of bitstreams: 1 wellingtoncarlosdaconceicao.pdf: 4259949 bytes, checksum: 52410bbb422df8d4e80e7f6956efc71e (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-21T13:12:55Z (GMT) No. of bitstreams: 1 wellingtoncarlosdaconceicao.pdf: 4259949 bytes, checksum: 52410bbb422df8d4e80e7f6956efc71e (MD5) / Made available in DSpace on 2017-03-21T13:12:55Z (GMT). No. of bitstreams: 1 wellingtoncarlosdaconceicao.pdf: 4259949 bytes, checksum: 52410bbb422df8d4e80e7f6956efc71e (MD5) Previous issue date: 2016-12-12 / O sistema de produção de energia elétrica brasileiro é um sistema hidrotérmico de grande porte com forte predominância de usinas hidrelétricas. O planejamento e operação do sistema é realizado considerando diversos fatores, tais como, estocasticidade das afluências, usinas hidrelétricas em cascata e acoplamento temporal da operação. A resolução deste tipodeproblemaéfeitaconsiderandodiversoshorizontesdeplanejamento. Oplanejamento da operação de médio prazo compreende um período de 5 anos de estudo, e este período é discretizado em base mensal. O presente trabalho apresenta uma metodologia alternativa para resolução do problema de planejamento da operação de médio prazo de sistemas hidrotérmicos utilizando a Programação Dinâmica Estocástica (PDE) com discretização dointercâmbiodeenergiaentreossubsistemas(PDE-INT).Alémdisso, utiliza-seatécnica de sistemas equivalentes de energia e o algoritmo de fechos convexos (convex hull) para obtenção da função de custo futuro a partir dos pontos obtidos pela PDE-INT. Nesta abordagem, para cálculo da política energética, os subsistemas são considerados isolados, e desta forma, as variáveis que compõem o espaço de estados que são discretizadas são a energia armazenada e o intercâmbio líquido entre os subsistemas. Inicialmente, para análise e avaliação da metodologia proposta na resolução do problema de planejamento hidrotérmico, criou-se um sistema tutorial, composto por dois subsistemas. Em seguida, a metodologia foi utilizada considerando todo o sistema elétrico brasileiro, representado por quatro subsistemas ou submercados. Os resultados mostraram que com a técnica de separação dos subsistemas há uma redução significativa no tempo computacional quando comparados com as técnicas tradicionais que utilizam programação dinâmica. Desta forma, a metodologia proposta pode ser utilizada para uma análise rápida e inicial do caso em estudo, servindo como base para estudos e refinamentos posteriores. / The Brazilian power production system is a large scale hydrothermal system with a strong predominance of hydroelectric power plants. The electric power system operation planning must take into consideration several factors, such as uncertainty of the water inflows, hydroelectric plants in cascade and temporal coupling. This problem is solved considering different planning horizon. The long-term operation planning problem is generally solved by a chain of computational models that consider a period of 5 years ahead with monthly discretization. This work presents an alternative strategy to solve hydrothermalsystemsoperationplanningbyStochasticDynamicProgramming(SDP)with discretization of energy interchange between subsystems (SDP-INT). Under the presented approach, the hydroelectric plants are grouped into energy equivalent subsystems and the expected operation cost functions are modeled by a piecewise linear approximation, by means of the convex hull algorithm. Also, under this methodology, the subsystems are solved isolated, but the net energy interchange (export – import) between subsystems is set as a state variable of the cost function, together with the energy storage Initially, for the analysis and evaluation of the proposed methodology applied on solving the hydrothermalplanningproblem, themethodologyisusedinatutorialsystem, composedof two subsystems. Next, a simulation with the whole Brazilian electrical system considering four subsystems is presented. The results have shown that this subsystems separation technique reduces significantly the computation time when compared with the traditional techniques, demonstrating the effectiveness of the proposed methodology. Thus, the proposed methodology can be used for a fast and initial analysis of the case study, serving as a basis for further studies.

Dans les régions semi-arides, les systèmes de production agricole dépendent fortement de l'irrigation et font face à des difficultés croissantes (épuisement des ressources naturelles, forte volatilité des prix du marché, hausse des coûts de l'énergie, incertitude sur les changements climatiques). Modéliser ces systèmes agricoles et la façon dont ils s'adaptent est important pour les décideurs politiques afin de mieux évaluer leur flexibilité et leur résilience. Pour comprendre la capacité des systèmes agricoles à s'adapter, il est essentiel de considérer l'ensemble du processus de décision : des décisions sur le long-terme à l'échelle de l'exploitation aux décisions de court-terme à l'échelle de la parcelle. Pour ce faire, cette thèse conçoit un système de production agricole adaptable dans un contexte de diminution de l'eau et de changement climatique. Elle fournit une méthodologie guidant l'acquisition de données, leur analyse et la conception de modèle. Elle présente le modèle de simulation NAMASTE représentant les décisions des agriculteurs, les interactions entre agriculteurs pour l'utilisation des ressources communes et met l'accent sur la rétroaction entre pratiques agricoles et évolution de la nappe phréatique. Le modèle a été initialement développé pour résoudre les problèmes critiques de baisse des eaux souterraines liés aux pratiques agricoles dans un bassin versant du sud-ouest de l'Inde. Sa structure, ses cadres conceptuels et ses formalismes peuvent être utilisés dans d'autres contextes agricoles. / In semi-arid regions, agricultural production systems depend greatly on irrigation and encounter increasing challenges (depletion of natural resources, high volatility in market prices, rise in energy costs, growing uncertainty about climate change). Modeling farming systems and how these systems change and adapt to these challenges is particularly interesting for policy makers to better assess their flexibility and resiliency. To understand the ability of farming systems to adapt, it is essential to consider the entire decision-making process: from long-term decisions at the farm scale to short-term decisions at the plot level. To this end, the thesis conceives a flexible and resilient agricultural production system under a context of water scarcity and climate change. It provides a step-by-step methodology that guides data acquisition and analysis and model design. It proposes a simulation model NAMASTE that simulates the farmers' decisions in different time and space scales, represents the interactions between farmers for resource uses and emphasizes the feedback and retroaction between farming practices and changes in the water table. The model was initially developed to address critical issues of groundwater depletion and farming practices in a watershed in southwestern India. Its structure, frameworks and formalisms can be used in other agricultural contexts.

Algumas abordagens para o problema de Planejamento Ótimo da Operação de Sistemas Hidrotérmicos (POOSH) utilizam modelos estocásticos para representar as vazões afluentes dos reservatórios do sistema. Essas abordagens utilizam, em geral, técnicas de Programação Dinâmica Estocástica (PDE) para resolver o POOSH. Por outro lado, muitos autores têm defendido o uso dos modelos determinísticos ou, particularmente, a Programação Dinâmica Determinística (PDD) por representar de forma individualizada a interação entre as usinas hidroelétricas do sistema. Nesse contexto, esta dissertação tem por objetivo comparar o desempenho da solução do POOSH obtida via PDD com a solução obtida pela PDE, que emprega um modelo Markoviano periódico, com distribuição condicional Log-Normal Truncada para representar as vazões. Além disso, é realizada a análise com abordagem bayesiana, no modelo de vazões, para estimação dos parâmetros e previsões das vazões afluentes. Comparamos as performances simulando a operação das usinas hidroelétricas de Furnas e Sobradinho, considerando séries de vazões geradas artificialmente / Some approaches for problem of Optimal Operation Planning of Hydrothermal Systems (OOPHS) use stochastic models to represent the inflows in the reservoirs that compose the system. These approaches typically use the Stochastic Dynamic Programming (SDP) to solve the OOPHS. On the other hand, many authors defend the use of deterministic models and, particularly, the Deterministic Dynamic Programming (DDP) since it individually represents the interaction between the hydroelectric plants. In this context, this dissertation aims to compare the performance of the OOPHS solution obtained via DDP with the one given by SDP, which employs a periodic Markovian model with conditional Truncated Log-Normal distribution to represent the inflows. Furthermore, it is performed a bayesian approach analysis, in the inflow model, for estimating the parameters and forecasting the inflows. We have compared the performances of the DDP and SDP solutions by simulating the hydroelectric plants of Furnas and Sobradinho, employing artificially generated series

Ce mémoire présente une étude comparative de quatre stratégies de gestion énergétique temps réel, appliquées d'une part à un véhicule hybride thermique-électrique, et d'autre part à un véhicule électrique à pile à combustible : contrôle basé sur des règles empirique (RBS), minimisation de la consommation équivalente (A-ECMS), loi de commande optimale (OCL) établie à partir d'une modélisation analytique du système et programmation dynamique stochastique (SDP) associée à une modélisation des cycles de conduite par chaîne de Markov. Le principe du minimum de Pontryaguin et la programmation dynamique, applicables hors ligne, sont mis en œuvre pour fournir des résultats de référence. Les problèmes d’implémentation numérique et de paramétrage des stratégies sont discutés. Une analyse statistique effectuée sur la base de cycles aléatoires générés par chaînes de Markov permet d’évaluer la robustesse des stratégies étudiées. Les résultats obtenus en simulation, puis sur un dispositif expérimental montrent que les méthodes les plus simples (RBS ou OCL) conduisent à des consommations élevées. SDP aboutit aux meilleures performances avec en moyenne la plus faible consommation de carburant dans les conditions réelles de conduite et un état énergétique final du système de stockage parfaitement maîtrisé. Les résultats d’A-ECMS sont comparables à ceux de SDP en moyenne, mais avec une plus grande dispersion, en particulier pour l'état de charge final. Afin d'améliorer les performances des méthode, des jeux de paramètres dédiés aux différents contextes de conduite sont considérés. / This thesis presents a comparative study between four recent real-time energy management strategies (EMS) applied to a hybrid electric vehicle and to a fuel cell vehicle applications: rule-based strategy (RBS), adaptive equivalent consumption minimization strategy (A-ECMS), optimal control law (OCL) and stochastic dynamic programming (SDP) associated to driving cycle modeling by Markov chains. Pontryagin’s minimum principle and dynamic programming are applied to off-line optimization to provide reference results. Implementation and parameters setting issues are discussed for each strategy and a genetic algorithm is employed for A-ECMS calibration.The EMS robustness is evaluated using different types of driving cycles and a statistical analysis is conducted using random cycles generated by Markov process. Simulation and experimental results lead to the following conclusions. The easiest methods to implement (RBS and OCL) give rather high fuel consumption. SDP has the best overall performance in real-world driving conditions. It achieves the minimum average fuel consumption while perfectly respecting the state-sustaining constraint. A-ECMS results are comparable to SDP’s when using parameters well-adjusted to the upcoming driving cycle, but lacks robustness. Using parameter sets adjusted to the type of driving conditions (urban, road and highway) did help to improve A-ECMS performances.

Cette thèse propose des stratégies de gestion de l'énergie conçues pour un bus urbain électrique hybride. Le système de commande hybride devrait créer une stratégie efficace de coordination du flux d’énergie entre le moteur thermique, la batterie, les moteurs électriques et hydrauliques. Tout d'abord, une approche basée sur la programmation dynamique déterministe (DDP) a été proposée : algorithme d'optimisation simultanée de la vitesse et de la puissance pour un trajet donné (limité par la distance parcourue et le temps de parcours). Cet algorithme s’avère être gourmand en temps de calcul, il n’a pas été donc possible de l’utiliser en temps réel. Pour remédier à cet inconvénient, une base de données de profils optimaux basée sur DP (OPD-DP) a été construite pour une application en temps réel. Ensuite, une technique de programmation dynamique stochastique (SDP) a été utilisée pour générer simultanément et d’une manière optimale un profil approprié de la vitesse du Bus ainsi que sa stratégie de partage de puissance correspondante. Cette approche prend en compte à la fois la nature stochastique du comportement de conduite et les conditions de circulations urbaines (soumises à de multiples aléas). Le problème d’optimisation énergétique formulé, en tant que problème intrinsèquement multi-objectif, a été transformé en plusieurs problèmes à objectif unique avec contraintes utilisant une méthode ε-constraint afin de déterminer un ensemble de solutions optimales (le front de Pareto).En milieu urbain, en raison des conditions de circulation, des feux de circulation, un bus rencontre fréquemment des situations Stop&Go. Cela se traduit par une consommation d'énergie accrue lors notamment des démarrages. En ce sens, une stratégie de régulation de vitesse adaptative adaptée avec Stop&Go (eACCwSG) apporte un avantage indéniable. L'algorithme lisse le profil de vitesse pendant les phases d'accélération et de freinage du Bus. Une autre caractéristique importante de cet algorithme est l’aspect sécurité, étant donné que l’ACCwSG permet de maintenir une distance de sécurité afin d’éviter les collisions et d’appliquer un freinage en douceur. Comme il a été mentionné précédemment, un freinage en douceur assure le confort des passagers. / This PhD thesis proposes Energy Management Strategies conceived for a hybrid electrical urban bus. The hybrid control system should create an efficient strategy of coordinating the flow of energy between the heat engine, battery, electrical and hydraulic motors. Firstly, a Deterministic Dynamic Programming (DDP) based approach has been proposed: simultaneous speed and powersplit optimization algorithm for a given trip (constrained by the traveled distance and time limit). This algorithm turned out to be highly time consuming so it cannot be used in real-time. To overcome this drawback, an Optimal Profiles Database based on DP (OPD-DP) has been constructed for real-time application. Afterwards, a Stochastic Dynamic Programming (SDP) technique is used to simultaneously generate an optimal speed profile and related powersplit strategy. This approach takes into account a stochastic nature of the driving behavior and urban conditions. The formulated energy optimization problem, being intrinsically multi-objective problem, has been transformed into several single-objective ones with constraints using an ε-constraint method to determine a set of optimal solutions (the Pareto Front).In urban environment, due to traffic conditions, traffic lights, a bus encounters frequent Stop&Go situations. This results in increased energy consumption during the starts. In this sense, a relevant Eco Adaptive Cruise Control with Stop&Go (eACCwSG) strategy brings the undeniable benefit. The algorithm smooths speed profile during acceleration and braking phases. One more important feature of this algorithm is the safety aspect, as eACCwSG permits to maintain a safety distance in order to avoid collision and apply a smooth braking. As it was mentioned before, smooth braking ensures passengers comfort.