Major cyber attacks against the cyber networks of organizations has become a common phenomenon nowadays. Cyber attacks are carried out both through the spread of malware and also through multi-stage attacks known as hacking. A cyber network can be represented directly as a simple directed or undirected network (graph) of nodes and arcs. It can also be represented by a transformed network such as the attack graph which uses information about network topology, attacker profile, and existing vulnerabilities to represent all the potential attack paths from readily accesible vulnerabilities to valuable target nodes. Then, interdicting or hardening a subset of arcs in the network naturally maps into deploying security countermeasures on the associated devices or connections. In this dissertation, we develop network interdiction models and algorithms to optimally select a subset of arcs which upon interdiction minimizes the spread of infection or minimizes the loss from multi-stage attacks. In particular, we define four novel network connectivity-based metrics and develop interdiction models to optimize the metrics. Direct network representation of the physical cyber network is used as the underlying network in this case. Two of the interdiction models prove to be very effective arc removal methods for minimizing the spread of infection. We also develop multi-level network interdiction models that remove a subset of arcs to minimize the loss from multi-stage attacks. Our models capture the defenderattacker interaction in terms of stackelberg zero-sum games considering the attacker both as a complete rational and bounded rational agents. Our novel solution algorithms based on constraint and column generation and enhanced by heuristic methods efficiently solve the difficult multi-level mixed-integer programs with integer variables in all levels in reasonable times.

This dissertation studies a framework in support biomass wood pellet supply chain. The worldwide wood pellet market is growing at a phenomenal rate. However, the economic sustainment of this business depends on how well the producers manage the uncertainty associated with biomass yield and quality. In the first part of the dissertation, we propose a two-stage stochastic programming model that optimizes different critical decisions (e.g., harvesting, storage, transportation, quality inspection, and production decisions) of a biomass-to-pellet supply system under biomass yield and quality uncertainty to economically produce pellets while accounting for the different pellet standards set forward by the U.S. and European markets. The study develops a hybrid algorithm that combines Sample Average Approximation with an enhanced Progressive Hedging algorithm. We propose two parallelization schemes to efficiently speed up the convergence of the overall algorithm. We use Mississippi as a testing ground to visualize and validate the algorithms performance. Experimental results indicate that the biomass-to-pellet supply system is sensitive to the biomass quality parameters (e.g., ash and moisture contents). In the second part of the dissertation, we propose a bi-level mixed-integer linear programming model that captures important features such as the hurricane’s degree, quality of damaged timbers, price-related issues, optimizes different critical decisions (e.g., purchasing, storage, and transportation decisions) of a post-hurricane damaged timber management problem. Lack of efficient tools to manage the wood market interactions in the post-hurricane situation increases timber salvage loss drastically. The overall goal is to provide an efficient decision-making tool for planning and recovering damaged timber to maximize its monetary value and mitigate its negative ecological impacts. Due to the complexity associated with solving the proposed model, we developed two exact solution methods, namely, the enhanced Benders decomposition and the Benders-based branch-and-cut algorithms, to efficiently solve the model in a reasonable time-frame. We use 15 coastal counties in southeast Mississippi to visualize and validate the algorithms' performance. Key managerial insights are drawn on the sensitivity of a number of critical parameters, such as selling/purchasing prices offered by the landowners/mills, quality-level, and deterioration rate of the damaged timbers on their economic recovery following a natural catastrophe.

Electrical distribution networks face many operational challenges as various renewable distributed generation (DG), such as solar photovoltaic (PV) systems and wind, become part of their structure. Unlike conventional distribution systems, where the only unpredictable aspect is the load level, the intermittent nature of DG poses additional uncertainty levels for distribution system operators (DSO). The voltage quality problem considers the most restrictive issue that hinders high DG integration into distribution grids. Voltage deviates from the nominal grid voltage limits due to the excess power from the DG. DSOs are accustomed to improving the voltage profile by optimal adjustments of the on-load tap changers, voltage regulator taps and capacitor banks. Nevertheless, due to the frequent variability of the output energy from DG, these devices may fail in doing the needful. Battery energy storage systems (BESS) and smart PV inverter functionalities are regarded as promising solutions to promote the seamless integration of renewable resources into distribution networks. BESS are utilized to store the surplus energy during the high penetration of renewable DG that causes high voltage levels and discharge the stored energy when the distribution grid is heavily loaded, which leads to the low voltage levels. Smart PV inverters regulate the network voltage by controlling the reactive power injection or absorption at the inverter end. This dissertation proposes a management strategy that coordinates BESS and smart PV inverter reactive power capability to improve voltage quality in the distribution systems with high PV and wind penetrations. The proposed management method is based on a bi-level optimization algorithm consisting of upper and lower optimization levels. The proposed method determines the optimal location, capacity, numbers and BESS charging and discharging rates to support the distribution system voltage and to ensure optimal deployment of BESS. Case studies are conducted to evaluate the proposed voltage control method. The large size PV system and wind turbine impacts are studied and simulated on the modified IEEE-34 bus test feeder. In addition, the proposed method is applied to the modified IEEE low voltage test feeder to investigate the effectiveness of installing residential rooftop PV systems on the distribution system's voltage. Experimental results show promising outcomes of the proposed method in controlling the distribution networks' voltage. In addition, a day-ahead forecast of PV power output is developed in this dissertation to assist the DSOs to accurately predict the future amounts of PV energy available and reinforcing the decision-making process of batteries operation. Hybrid forecasting models are proposed based on machine learning algorithms, which utilize support vector regression and backpropagation neural network, optimized with three metaheuristic optimization algorithms, namely Social Spider Optimization (SSO), Particle Swarm Optimization (PSO) and Cuckoo Search Optimization (CSO). These algorithms are used to improve the predictive efficacy of the selected algorithms, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. / Doctor of Philosophy / The need for more renewable energy has grown significantly, and many countries are embracing these technologies. However, the integration of distributed generation (DG), such as PV systems and wind turbines, poses several operational problems to the distribution system. The voltage problem represents the most significant issue that needs to be addressed. The traditional voltage control equipment may not cope with the rapid fluctuation and may impact their service life. The continuous developments in the battery energy storage systems (BESS) and the smart PV inverter technologies result in increasing the hosting capacity of DG. BESS can store the excess power from the distributed generators and supply this energy to the grid for different operational objectives. On the other hand, the advanced PV inverter's reactive power capability can be exploited from which the grid can attain many benefits. This dissertation aims at providing a reliable control method to the voltage profile in distribution networks embedded with high PV and wind energy by optimal coordination between the operation of the BESS and the smart PV inverter. In addition, the solar forecasting can mitigate the uncertainty associated with PV system generation. In this dissertation, the PV power forecasting application is applied in the distribution system to control the voltage. Through utilizing PV power forecasting, the decision-making for battery operation can be upheld and reinforced. The BESS can store the surplus energy from the PV system as needed and supply it back in low PV power incidents. Experimental results indicate that proper coordination between the BESS and smart PV inverter is beneficial for distribution system operation that can seamlessly integrate PV and wind energy.

Le système étudié durant cette thèse est un véhicule électrique hybride avec deux sources d’énergies (Pile à combustible et Super-capacité). L’objectif fixé est de minimiser la consommation du carburant tout en satisfaisant la demande instantanée en puissance sous des contraintes de puissance et de capacité et de stockage. Le problème a été modélisé sous la forme d’un problème d’optimisation globale. Nous avons développé de nouvelles méthodes heuristiques pour le résoudre et proposé le calcul d’une borne inférieure de consommation, en apportant de meilleurs résultats que ceux trouvés dans la littérature. En plus, une étude de robustesse a été réalisée afin de minimiser la consommation de pire-cas suite à une perturbation ou du fait d’incertitudes sur les données d’entrée, précisément sur la puissance demandée. Le but de cette étude est de prendre en compte les perturbations dès la construction des solutions afin d’éviter l’infaisabilité des solutions non robustes en situation perturbée. Les heuristiques de résolution du problème robuste modélisé sous la forme d’un problème de Minimax ont fourni des solutions moins sensibles aux perturbations que les solutions classiques. / The system studied in this thesis is a hybrid electrical vehicle with two energy sources (fuel cell system and super-capacitor). The first goal is to minimize the fuel consumption whilst satisfying the requested power for each instant, taking into account constraints on the availability and the state of charge of the storage element. The system was modeled as a global optimization problem. The heuristics developped for obtaining the best power split between the two sources and the lower bound consumption computation proposed provide better results than those found in the literature. The second goal of the thesis is the study of the robustness of the solutions in order to minimize the worst-case consumption when perturbation happens or uncertainty is added to the input data. In this study the uncertainty concerns the power required for traction. The objective is to maintain the feasibility of solutions and limit the worst consumption that can happen due to a demand fluctuation. Dedicated heuristics are proposed for solving the identified robust variant of the problem, modeled as a Minimax problem. The solutions provided are less sensitive to the perturbations than the previous ones.

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The introduction of communication technology into the electric power grid has made the grid more reliable. Power system operators gain visibility over the power system and are able to resolve operational issues remotely via Supervisory Control And Data Acquisition (SCADA) technology. This reduces outage periods. Nonetheless, the remote-control capability has rendered the power grid vulnerable to cyberattacks. In December 2015, over 200,000 people in Ukraine became victims of the first publicly reported cyberattack on the power grid. Consequently, cyber-physical security research for the power system as a critical infrastructure is in critical need. Research on cybersecurity for power grids has produced a diverse literature; the multi-faceted nature of the grid makes it vulnerable to different types of cyberattacks, such as direct power grid, supply chain and ransom attacks. The attacks may also target different levels of grid operation, such as the transmission system, distribution system, microgrids, and generation. As these levels are characterized by varying operational constraints, the literature may be categorized not only according to the type of attack it targets, but also according to the level of power system operation under consideration. It is noteworthy that cybersecurity research for the transmission system dominates the literature, although the distribution system is noted to have a larger attack surface. For the distribution system, a notable attack type is the so-called direct switching attack, in which an attacker aims to disrupt power supply by compromising switching devices that connect equipment such as generators, and power grid lines. To maximize the damage, this attack tends to be coordinated as the attacker optimally selects the nodes and switches to attack. This decision-making process is often a bi- or tri-level optimization problem which models the interaction between the attacker and the power system defender. It is necessary to detect attacks and establish coordination/correlation among them. Determining coordination is a necessary step to predict the targets of an attack before attack completion, and aids in the mitigation strategy that ensues. While the literature has addressed the direct switching attack on the distribution system in different ways, there are also shortcomings. These include: (i) techniques to establish coordination among attacks are centralized, making them prone to single-point failures; (ii) techniques to establish coordination among attacks leverage only power system models, ignoring the influence of communication network vulnerabilities and load criticality in the decisions of the attacker; (iii) attacker-defender optimization models assume specific knowledge of the attacker resources and constraints by the defender, a strong unrealistic assumption that reduces their usability; (iv) and, mitigation strategies tend to be static and one-sided, being implemented only at the physical level, or at the communication network level. In light of this, this dissertation culminates in major contributions concerning real-time decentralized correlation of detected direct switching attacks and hybrid mitigation for electric power distribution systems. Concerning this, four novel contributions are presented: (i) a framework for decentralized correlation of attacks and mitigation; (ii) an attacker-defender optimization model that accounts for power system laws, load criticality, and cyber vulnerabilities in the decision-making process of the attacker; (iii) a real-time learning-based mechanism for determining correlation among detected attacks and predicting attack targets, and which does not assume knowledge of the attacker's resources and constraints by the power system defender; (iv) a hybrid mitigation strategy optimized in real-time based on information learned from detected attacks, and which combines both physical level and communication network level mitigation. Since the execution of intrusion detection systems and mechanisms such as the ones proposed in this dissertation may deter attackers from directly attacking the power grid, attackers may perform a supply chain cyberattack to yield the same results. Although, supply chain cyberattacks have been acknowledged as potentially far-reaching, and compliance directives put forward for this, the detection of supply chain cyberattacks is in a nascent stage. Consequently, this dissertation also proposes a novel method for detecting supply chain cyberattacks. To the best of the knowledge of the author, this work is the first preliminary work on supply chain cyberattack detection. / Doctor of Philosophy / The electric power grid is the network that transports electricity from generation to consumers, such as homes and factories. The power grid today is highly remote-monitored and controlled. Should there be a fault on the grid, the human operator, often remotely located, may only need to resolve it by sending a control signal to telemetry points, called nodes, via a communication network. This significantly reduces outage periods and improves the reliability of the grid. Nonetheless, the high level connectivity also exposes the grid to cyberattacks. The cyber connectivity between the power grid and the human operator, like all communication networks, is vulnerable to cyberattacks that may allow attackers to gain control of the power grid. If and when successful, wide-spread and extended outages, equipment damage, etc. may ensue. Indeed, in December 2015, over 200,000 people in Ukraine became victims to the first publicly reported cyberattack on a power grid. As a critical infrastructure, cybersecurity for the power grid is, therefore, in critical need. Research on cybersecurity for power grids has produced a diverse literature; the multi-faceted nature of the grid makes it vulnerable to different types of cyberattacks, such as direct power grid, supply chain and ransom attacks. Notable is the so-called direct switching attack, in which an attacker aims to compromise the power grid communication network in order to toggle switches that connect equipment such as generators, and power grid lines. The aim is to disrupt electricity service. To maximize the damage, this attack tends to be coordinated; the attacker optimally selects several grid elements to attack. Thus, it is necessary to both detect attacks and establish coordination among them. Determining coordination is a necessary step to predict the targets of an attack before attack completion. This aids the power grid owner to intercept and mitigate attacks. While the literature has addressed the direct switching attack in different ways, there are also shortcomings. Three outstanding ones are: (i) techniques to determine coordination among attacks and predict attack targets are centralized, making them prone to single-point failures; (ii) techniques to establish coordination among attacks leverage only power system physical laws, ignoring the influence of communication network vulnerabilities in the decisions of the attacker; (iii) and, studies on the interaction between the attacker and the defender (i.e., power grid owner) assume specific knowledge of the attacker resources and constraints by the defender, a strong unrealistic assumption that reduces their usability. This research project addresses several of the shortcomings in the literature, particularly the aforementioned. The work focuses on the electric distribution system, which is the power grid that connects directly to consumers. Indeed, this choice is ideal, as the distribution system has a larger attack surface than other parts of the grid and is characterized by computing devices with more constrained computational capability. Thus, adaptability to simple computing devices is a priority. The contributions of this dissertation provide leverage to the power grid owner to intercept and mitigate attacks in a resilient manner. The original contributions of the work are: (i) a novel realistic model that shows the decision making process of the attacker and their interactions with the defender; (ii) a novel decentralized mechanism for predicting the targets of coordinated cyberattacks on the electric distribution grid in real-time and which is guided by the attack model, (iii) and a novel hybrid optimized mitigation strategy that provides security to the power grid at both the communication network level and the physical power grid level. Since the power grid is constructed with smart equipment from various vendors, attackers may launch effective attacks by compromising the devices deployed in the power grid through a compromised supply chain. By nature, such an attack is evasive to traditional intrusion detection systems and algorithms such as the aforementioned. Therefore, this work also provides a new method to defend the grid against supply chain attacks, resulting in a mechanism for its detection in a critical power system communication device.

Le management de la chaîne logistique concerne un large éventail d’activités. Nombreuses ceux qui ont un caractère incertain apportant souvent des conséquences inattendues. Malgré cela, l’incertitude est fréquemment non considérée dans la gestion de la chaîne logistique traditionnelle. En plus de l’incertitude, l’indisponibilité des ressources augmentera la complexité du problème. En prenons en compte les contraintes d’incertitude et de disponibilité nous étudions le management de la chaîne logistique selon différents aspects. Cette thèse représente une tentative de recherche afin d’aborder ce problème d’une façon systématique et complète et nous espérons que notre travail contribuera aux futurs travaux de recherche et sera utile aux gestionnaires de la chaîne logistique. Nous nous concentrons sur trois sources classiques de l’incertitude ; celle de la demande, celle la fabrication et celle liée à la distribution. Pour chaque source d’incertitude, nous analysons ses causes et ses impacts sur les performances de la chaîne logistique. L’incertitude est spécifiée dans des problèmes classiques concrets et des approches sont proposées pour les résoudre. Nous nous sommes également focalisés sur le problème bi-niveau de vendeur de journaux qui représente une chaîne logistique miniature, concerné par une double incertitude. Les méthodes utilisées offrent une bonne démonstration du traitement des variables incertaines dans les problèmes de décision / Supply chain management involves a wide range of activities. Among most of them, uncertainty exists inherently and always brings some consequence not expected. However, uncertainty is not considered much in conventional supply chain management. In the case where availability of resources is not what we expect, complexity of supply chain management increases. Taking constraints of uncertainty and availability into account, we aim to discuss supply chain management from different aspects. This thesis is an attempt of systematic and complete research from this point and we would like to offer some references to researchers and managers in supply chain. We focus on three classic sources of uncertainty: demand, manufacturing and distribution. For each source of uncertainty, we analyze its cause and its impact to the performance of the supply chain. Uncertainty is specified into concrete classic problem and an approach is proposed to solve it. Furthermore, bi-level newsboy problem as a miniature of supply chain, is focused under double uncertain environment. Treating uncertain variables is actually a treatment on operational level. The methods used offer good demonstration in treating uncertain variables in decision problems

Les problèmes d’optimisation combinatoire sont généralement réputés NP-difficiles, donc il n’y a pas d’algorithmes efficaces pour les résoudre. Afin de trouver des solutions optimales locales ou réalisables, on utilise souvent des heuristiques ou des algorithmes approchés. Les dernières décennies ont vu naitre des méthodes approchées connues sous le nom de métaheuristiques, et qui permettent de trouver une solution approchées. Cette thèse propose de résoudre des problèmes d’optimisation déterministe et stochastique à l’aide de métaheuristiques. Nous avons particulièrement étudié la méthode de voisinage variable connue sous le nom de VNS. Nous avons choisi cet algorithme pour résoudre nos problèmes d’optimisation dans la mesure où VNS permet de trouver des solutions de bonne qualité dans un temps CPU raisonnable. Le premier problème que nous avons étudié dans le cadre de cette thèse est le problème déterministe de largeur de bande de matrices creuses. Il s’agit d’un problème combinatoire difficile, notre VNS a permis de trouver des solutions comparables à celles de la littérature en termes de qualité des résultats mais avec temps de calcul plus compétitif. Nous nous sommes intéressés dans un deuxième temps aux problèmes de réseaux mobiles appelés OFDMA-TDMA. Nous avons étudié le problème d’affectation de ressources dans ce type de réseaux, nous avons proposé deux modèles : Le premier modèle est un modèle déterministe qui permet de maximiser la bande passante du canal pour un réseau OFDMA à débit monodirectionnel appelé Uplink sous contraintes d’énergie utilisée par les utilisateurs et des contraintes d’affectation de porteuses. Pour ce problème, VNS donne de très bons résultats et des bornes de bonne qualité. Le deuxième modèle est un problème stochastique de réseaux OFDMA d’affectation de ressources multi-cellules. Pour résoudre ce problème, on utilise le problème déterministe équivalent auquel on applique la méthode VNS qui dans ce cas permet de trouver des solutions avec un saut de dualité très faible. Les problèmes d’allocation de ressources aussi bien dans les réseaux OFDMA ou dans d’autres domaines peuvent aussi être modélisés sous forme de problèmes d’optimisation bi-niveaux appelés aussi problèmes d’optimisation hiérarchique. Le dernier problème étudié dans le cadre de cette thèse porte sur les problèmes bi-niveaux stochastiques. Pour résoudre le problème lié à l’incertitude dans ce problème, nous avons utilisé l’optimisation robuste plus précisément l’approche appelée « distributionnellement robuste ». Cette approche donne de très bons résultats légèrement conservateurs notamment lorsque le nombre de variables du leader est très supérieur à celui du suiveur. Nos expérimentations ont confirmé l’efficacité de nos méthodes pour l’ensemble des problèmes étudiés. / Combinatorial optimization problems are generally NP-hard problems, so they can only rely on heuristic or approximation algorithms to find a local optimum or a feasible solution. During the last decades, more general solving techniques have been proposed, namely metaheuristics which can be applied to many types of combinatorial optimization problems. This PhD thesis proposed to solve the deterministic and stochastic optimization problems with metaheuristics. We studied especially Variable Neighborhood Search (VNS) and choose this algorithm to solve our optimization problems since it is able to find satisfying approximated optimal solutions within a reasonable computation time. Our thesis starts with a relatively simple deterministic combinatorial optimization problem: Bandwidth Minimization Problem. The proposed VNS procedure offers an advantage in terms of CPU time compared to the literature. Then, we focus on resource allocation problems in OFDMA systems, and present two models. The first model aims at maximizing the total bandwidth channel capacity of an uplink OFDMA-TDMA network subject to user power and subcarrier assignment constraints while simultaneously scheduling users in time. For this problem, VNS gives tight bounds. The second model is stochastic resource allocation model for uplink wireless multi-cell OFDMA Networks. After transforming the original model into a deterministic one, the proposed VNS is applied on the deterministic model, and find near optimal solutions. Subsequently, several problems either in OFDMA systems or in many other topics in resource allocation can be modeled as hierarchy problems, e.g., bi-level optimization problems. Thus, we also study stochastic bi-level optimization problems, and use robust optimization framework to deal with uncertainty. The distributionally robust approach can obtain slight conservative solutions when the number of binary variables in the upper level is larger than the number of variables in the lower level. Our numerical results for all the problems studied in this thesis show the performance of our approaches.

Enhancing performance of power electronics converters has always been an interesting topic in the power electronics community. Over the years, researchers and engineers are developing new high performance component, novel converter topologies, smart control methods and optimal design procedures to improve the efficiency, power density, reliability and reducing the cost. Besides pursuing high performance, researchers and engineers are striving to modularize the power electronics converters, which provides redundancy, flexibility and standardization to the end users. The trend of modularization has been seen in photovoltaic inverters, telecommunication power supplies, and recently, HVDC applications. A systematic optimal design approach for modular power converters is developed in this dissertation. The converters are developed for aerospace applications where there are stringent requirement on converter form factor, loss dissipation, thermal management and electromagnetic interference (EMI) performance. This work proposed an optimal design approach to maximize the nominal power of the power converters considering all the constraints, which fully reveals the power processing potential. Specifically, this work studied three-phase active front-end converter, three-phase isolated ac/dc converter and inverter. The key models (with special attention paid to semiconductor switching loss model), detailed design procedures and key design considerations are elaborated. With the proposed design framework, influence of key design variables, e.g. converter topology, switching frequency, etc. is thoroughly studied. Besides optimal design procedure, control issues in paralleling modular converters are discussed. A master-slave control architecture is used. The slave controllers not only follow the command broadcasted by the master controller, but also synchronize the high frequency clock to the master controller. The control architecture eliminates the communication between the slave controllers but keeps paralleled modules well synchronized, enabling a fully modularized design. Furthermore, the implementation issues of modularity are discussed. Although modularizing converters under form factor constraints adds flexibility to the system, it limits the design space by forbidding oversized components. This work studies the influence of the form factor by exploring the maximal nominal power of a double-sized converter module and comparing it with that of two paralleled modules. The tradeoff between modularity and performance is revealed by this study. Another implementation issue is related to EMI. Scaling up system capacity by paralleling converter modules induces EMI issues in both signal level and system level. This work investigates the mechanisms and provides solutions to the EMI problems. / Ph. D. / As penetration of power electronics technologies in electric power delivery keeps increasing, performance of power electronics converters becomes a key factor in energy delivery efficacy and sustainability. Enhancing performance of power electronics converters reduces footprint, energy waste and delivery cost, and ultimately, promoting a sustainable energy use. Over the years, researchers and engineers are developing new technologies, including high performance component, novel converter topologies, smart control methods and optimal design procedures to improve the efficiency, power density, reliability and reducing the cost of power electronics converters. Besides pursuing high performance, researchers and engineers are striving to modularize the power electronics converters, enabling power electronics converters to be used in a “plug-and-play” fashion. Modularization provides redundancy, flexibility and standardization to the end users. The trend of modularization has been seen in applications that process electric power from several Watts to Megawatts. This dissertation discusses the design framework for incorporating modularization into existing converter design procedure, synergically achieving performance optimization and modularity. A systematic optimal design approach for modular power converters is developed in this dissertation. The converters are developed for aerospace applications where there is stringent v requirement on converter dimensions, loss dissipation, and thermal management. Besides, to ensure stable operation of the onboard power system, filters comprising of inductors and capacitors are necessary to reduce the electromagnetic interference (EMI). Owning to the considerable weight and size of the inductors and capacitors, filter design is one of the key component in converter design. This work proposed an optimal design approach that synergically optimizes performance and promotes modularity while complying with the entire aerospace requirement. Specifically, this work studied three-phase active front-end converter, three-phase isolated ac/dc converter and three-phase inverter. The key models, detailed design procedures and key design considerations are elaborated. Experimental results validate the design framework and key models, and demonstrates cutting-edge converter performance. To enable a fully modularized design, control of modular converters, with focus on synchronizing the modular converters, is discussed. This work proposed a communication structure that minimizes communication resources and achieves seamless synchronization among multiple modular converters that operate in parallel. The communication scheme is demonstrated by experiments. Besides, the implementation issues of modularity are discussed. Although modularizing converters under form factor constraints adds flexibility to the system, it limits the design space by forbidding oversized components. This work studies the impact of modularity by comparing performance of a double-sized converter module with two paralleled modules. The tradeoff between modularity and performance is revealed by this study.

Orientador: Rubén Augusto Romero Lázaro / Banca: Jose Roberto Sanches Mantovani / Banca: Anna Diva Plasencia Lotufo / Banca: Marcos Julio Rider Flores / Banca: Eduardo Nobuhiro Asada / Resumo: Nessa pesquisa tem-se por objetivo a análise teórica e a implementação computacional de duas propostas de solução ao problema de planejamento da expansão de sistemas de transmissão de energia elétrica considerando diferentes fatores relacionados com a confiabilidade do sistema e a adoção dos novos modelos de mercados elétricos. É importante notar, que no planejamento básico não são levados em conta esses importantes aspectos. Dessa forma, uma primeira aproximação considera um critério de confiabilidade para expandir o sistema, de forma que ele opere adequadamente no horizonte de planejamento satisfazendo um nível de confiabilidade pré-definido. O índice de confiabilidade utilizado para exigir esse nível de confiabilidade é o LOLE, que corresponde ao número médio de horas/dias em um período dado (normalmente um ano) no qual o pico da carga horária/diária do sistema possivelmente exceder'a a capacidade de geração disponível. O problema de planejamento considerando a confiabilidade é, portanto, formulado como um problema de otimização que minimiza o investimento sujeito ao critério de confiabilidade. O índice de confiabilidade para o sistema de transmissão é calculado para cada configuração, subtraindo o índice de confiabilidade do sistema de geração do sistema composto geração-transmissão (bulk power system ). Para calcular o índice no sistema composto geração transmissão, utiliza-se uma curva de duração de carga efetiva para este sistema. Esta curva acumulada de carga é obtida de um processo de convolução de outras duas curvas que representam a função de distribuição de probabilidade (FDP) das saídas aleatórias dos componentes do sistema e a curva de duração de carga, respectivamente. A avaliação de confiabilidade no sistema de geração é feita usando um método que calcula o índice de confiabilidade por meio dos momentos... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work aims to the theoretical analysis and computational implementation of two proposals for the transmission expansion planning problem considering several factors such as system reliability and new electricity market structures. It is important to observe, that the basic planning does not consider these issues. Therefore, one first approach considers a reliability criterion to expand the system, so that it operates in adequate conditions in the horizon planning while satisfying pre-defined limits in the reliability index. Transmission system reliability criterion regards to LOLE, which refers to the number of hours/days in a specified period of time (normally one year), in which the hourly/daily peak load possibly will exceed the available generation capacity. So, the planning problem considering reliability is formulated as an optimization problem that minimizes the investment subject to probabilistic reliability criterion. Reliability index for the transmission system is calculated for each configuration by subtraction of generation and bulk power reliability indexes. A composite power system effective load curve is used for reliability analysis of the bulk power system. This accumulate curve is obtained convolving two curves, one of them corresponding to a probability distribution function of the random outages of the system components, and the other one corresponding to the load duration curve. Reliability assessment in the generation system is done using a method that calculates the reliability index through the statistics moments of the frequency distribution of equivalents loads. This curve is obtained by convolving the generation units which are dispached in merit order. The proposed model is solved using the specialized genetic algorithm of Chu-Beasley (AGCB). Detailed results on two test systems are analyzed and discussed. A second approach to the transmission expansion... (Complete abstract click electronic access below) / Doutor