The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

Pour résoudre des problèmes d'optimisation discret de type boîte noire, de nombreux algorithmes stochastiques tels que les algorithmes évolutionnaires ou les métaheuristiques existent et se révèlent particulièrement efficaces selon le problème à résoudre. En fonction des propriétés observées du problème, choisir l'algorithme le plus pertinent est un problème difficile. Dans le cadre original des environnements de calcul parallèle et distribué, nous proposons et analysons différentes stratégies adaptative de sélection d'algorithme d'optimisation. Ces stratégies de sélection reposent sur des méthodes d'apprentissage automatique par renforcement, issu du domaine de l'intelligence artificielle, et sur un partage d'information entre les noeuds de calcul. Nous comparons et analysons les stratégies de sélection dans différentes situations. Deux types d'environnement de calcul distribué synchrone sont abordés : le modèle en île et le modèle maître-esclave. Sur l'ensemble des noeuds de manière synchrone à chaque itération la stratégie de sélection adaptative choisit un algorithme selon l'état de la recherche de la solution. Dans une première partie, deux problèmes OneMax et NK, l'un unimodal et l'autre multimodal, sont utilisés comme banc d'essai de ces travaux. Ensuite, pour mieux saisir et améliorer la conception des stratégies de sélection adaptatives, nous proposons une modélisation du problème d'optimisation et de son opérateur de recherche locale. Dans cette modélisation, une caractéristique importante est le gain moyen d'un opérateur en fonction de la fitness de la solution candidate. Le modèle est utilisé dans le cadre synchrone du modèle maître-esclave. Une stratégie de sélection se décompose en trois composantes principales : l'agrégation des récompenses échangées, la technique d'apprentissage et la répartition des algorithmes sur les noeuds de calcul. Dans une dernière partie, nous étudions trois scénarios et nous donnons des clés de compréhension sur l'utilisation pertinente des stratégies de sélection adaptative par rapport aux stratégies naïves. Dans le cadre du modèle maître-esclave, nous étudions les différentes façons d'agréger les récompenses sur le noeud maître, la répartition des algorithmes d'optimisation sur les noeuds de calcul et le temps de communication. Cette thèse se termine par des perspectives pour le domaine de l'optimisation stochastique adaptative distribuée. / To solve discrete optimization problems of black box type, many stochastic algorithms such as evolutionary algorithms or metaheuristics exist and prove to be particularly effective according to the problem to be solved. Depending on the observed properties of the problem, choosing the most relevant algorithm is a difficult problem. In the original framework of parallel and distributed computing environments, we propose and analyze different adaptive optimization algorithm selection strategies. These selection strategies are based on reinforcement learning methods automatic, from the field of artificial intelligence, and on information sharing between computing nodes. We compare and analyze selection strategies in different situations. Two types of synchronous distributed computing environment are discussed : the island model and the master-slave model. On the set of nodes synchronously at each iteration, the adaptive selection strategy chooses an algorithm according to the state of the search for the solution. In the first part, two problems OneMax and NK, one unimodal and the other multimodal, are used as benchmarks for this work. Then, to better understand and improve the design of adaptive selection strategies, we propose a modeling of the optimization problem and its local search operator. In this modeling, an important characteristic is the average gain of an operator according to the fitness of the candidate solution. The model is used in the synchronous framework of the master-slave model. A selection strategy is broken down into three main components : the aggregation of the rewards exchanged, the learning scheme and the distribution of the algorithms on the computing nodes. In the final part, we study three scenarios, and we give keys to understanding the relevant use of adaptive selection strategies over naïve strategies. In the framework of the master-slave model, we study the different ways of aggregating the rewards on the master node, the distribution of the optimization algorithms of the nodes of computation and the time of communication. This thesis ends with perspectives in the field of distributed adaptive stochastic optimization.