Power grids in today's developed societies are designed to meet consumer demands in a highly reliable manner. In order to guarantee reliability to consumers, the grid is required to be sized for infrequently occurring demand peaks. The cost of maintaining generation sources that make up the relatively unused capacity of the grid can be extremely high. In addition to high costs, environmental impacts of these sources are also of great concern. In order to serve highly fluctuating peak demands, energy sources such as coal, gas and bio-gas are commissioned by utilities. These sources have a high carbon footprint. In order to prevent wasting extensive amounts of money in maintaining infrequently used grid capacity and causing an adverse environmental impact, a comprehensive study on how elasticity of domestic appliances can be used to reduce the impact of these issues is made. A thorough analysis of appliances in four distinct regions is presented. Significant reduction of peak demands is shown quantitatively for all of the four regions. Based on these positive results, an elasticity based scheme that takes into account user discomfort is proposed for reducing monetary and environmental issues faced by today's utilities.

The problem of optimum restoration after occurrence the outages in a distribution network is an important issue in smart grids. In this kind of networks remote-controlled switches, alternative sources and grid-connected distributed generators (DG) are employed. Therefore, the reliability of the system (corresponding to the frequency of failures and the duration of interruptions) is improved by operating the switches to resupply a part of interrupted system during the repair time. To evaluate the reliability indices in smart grids, neglecting the restoration during the repair time causes the wrong assessment of the network. Thus, considering the rerouting the power during the interruptions seems necessary to calculate the reliability indices. The problem of restoration is formulated as a non-linear integer programming problem with the assistance of the network graph. The circuit graph method is also used to pre-evaluate the feasible interchanging operations to enhance the efficiency of the computations. The topological and operational constraints in this formulation can be found and resolved with the assistance of fundamental cut-set matrix. The optimum restoration schemes is obtained by considering the optimal islanded mode of operation of the DGs which implies maximizing the loading of DGs while not violating their generation capacities. The optimum restoration policies lead us to find the optimum number and location of those manual switches that should be upgraded to the remote-controlled switches. This is a multi-objective problem that contains the contribution of each restoration policy in the reliability improvement and the cost of those switching operations. The proposed algorithm is applied to different standard test systems and the results are compared to the results obtained from other methods and algorithms.

abstract: Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization. / Dissertation/Thesis / Ph.D. Electrical Engineering 2013

Electric vehicle charging stations (EVCS) play a vital role in providing charging support to EV users. In order to facilitate users in terms of charging speed and price, two different charging modes (L2 and L3) are currently available at public charging stations. L3 mode provides quick charging with higher power, whereas L2 mode offers moderate charging speed with low power. The integration of an EVCS into the power grid requires coordinated charging strategies in order to reduce the electricity bill for a profitable operation. However, the effective utilization of the multi-mode charging strategy to serve the maximum number of EVs for a small charging station with limited charging capacity and spots is an open issue. To this end, we propose a priority-based online charging scheme, namely PBOS, which is based on real-time information and does not depend on future knowledge. The objective is to serve as many vehicles as possible in a day while fulfilling their charging requirements under a multi-mode EVCS setting and reducing the charging costs by utilizing the time-of-use pricing based demand response strategy. Extensive simulation is done while considering two different demand response strategies under various settings. The results show that the proposed algorithm can increase profit for the EVCS by up to 48\% with a 22\% lower rejection rate. In addition, it can serve EVs with a low battery charge, known as state of charge (SOC), up to 11\% higher than most of the other schemes and can save up to 81.75 minutes to attain the same SOC when compared with other schemes. / Graduate

Due to the rapid growth of urban areas, sustainable urbanization is an inevitable task for city planners to address major challenges in resource management across different sectors. Sustainable approaches of energy production, distribution, and consumption must take the place of traditional methods to reduce the negative impacts of urbanization such as global warming and fast consumption of fossil fuels. In order to enable the transition of cities to sustainable ones, we need to have a precise understanding of the city dynamics. The prevalence of big data has highlighted the importance of data-driven analysis on different parts of the city including human movement, physical infrastructure, and economic activities. Sustainable urban mobility (SUM) is the problem domain that addresses the sustainability issues in urban areas with respect to city dynamics and people movements in the city. Hence, to realize an integrated solution for SUM, we need to study the problems that lie at the intersection of energy systems and mobility. For instance, electric vehicle invention is a promising shift toward smart cities, however, the impact of high adoption of electric vehicles on different units such as electricity grid should be precisely addressed. In this dissertation, we use data analytics methods in order to tackle major issues in SUM. We focus on mobility and energy issues of SUM by characterizing transportation networks and energy networks. Data-driven methods are proposed to characterize the energy systems as well as the city dynamics. Moreover, we propose anomaly detection algorithms for control and management purposes in smart grids and in cities. In terms of applications, we specifically investigate the use of electrical vehicles for personal use and also for public transportation (i.e. electric taxis). We provide a data-driven framework to propose optimal locations for charging and storage installation for electric vehicles. Furthermore, adoption of electric taxi fleet in dense urban areas is investigated using multiple data sources. / Ph. D.

Os smart grids são tidos como a nova geração dos sistemas elétricos de potência, combinando avanços em computação, sistemas distribuídos e inteligência artificial para prover maiores funcionalidades sobre acompanhamento em tempo real da demanda e do consumo de energia elétrica, gerenciamento em larga escala de geradores distribuídos, entre outras, a partir de um sistema de controle distribuído sobre a rede elétrica. Esta abordagem alteraria fundamentalmente a maneira como se dá o planejamento e a operação de sistemas de distribuição, e há grandes possibilidades de pesquisa e desenvolvimento possibilitada pela busca de implementação destas funcionalidades. Com esse cenário em vista, o presente trabalho utiliza uma abordagem a partir do uso de sistemas multiagentes para estudar o gerenciamento de sistemas de distribuição, do ponto de vista da reconfiguração da topologia da rede, simulando as características de um smart grid. Nesta dissertação, foi desenvolvido um sistema multiagente para simulação computacional de um sistema de distribuição elétrico do tipo smart grid, buscando executar a reconfiguração topológica do sistema a partir de dados de carga capturados de forma distribuída pelos agentes dispersos na rede elétrica. Espera-se que o desenrolar da pesquisa conduza à vários estudos sobre algoritmos e técnicas que melhor implementem tais funcionalidades a serem transpostas para um ambiente de produção. / Smart grids are taken as the new generation of electric power systems, combining advances in computing, distributed systems and artificial intelligence to provide more features on real-time monitoring of demand and consumption of electricity, managing large-scale distributed generators, among others, from a distributed control system on the grid. This approach fundamentally alter the way how is the planning and operation of distribution systems, and there are great possibilities for research and development offered in the quest to implement these features. With that environment, this text uses an approach through the use of multi-agent systems to study the management of the distribution system, from the reconfiguration of grid topology, simulating the characteristics of a smart grid. In this text, was developed a multiagent system to computational simulation of a distribution system like smart grid to topological reconfiguration, from datas collected for agents in electrical grid. It is expected that the conduct of research leads to several studies about better algorithms and techniques that would implement such functionality in a production environment.

A complexidade dos sistemas de distribuição é uma característica intrínseca dos mesmos, isso dificulta a aplicação de ferramentas convencionais para localização de faltas, cuja eficiência é determinante para melhoria dos índices de qualidade de energia e serviços prestados por concessionárias de energia. Este trabalho tem o objetivo de apresentar uma abordagem alternativa para a localização de faltas em alimentadores de distribuição aéreos com topologia radial. A proposta consiste no desenvolvimento de um algoritmo de auxílio à decisão para técnicas convencionais de localização de faltas, capaz de reduzir o problema da múltipla estimação. Esta proposta se dá num contexto onde é vislumbrado uma infraestrutura de Smart Grids consolidada. A ferramenta consiste em um pré-processamento de sinais analógicos e extração de parâmetros para posterior uso da técnica DAMICORE (do inglês Data Mining of Code Repositories) a fim de encontrar relações entre os eventos, agrupando aqueles que forem similares, de modo a auxiliar na localização de faltas. O sistema utilizado para a simulação de curtos-circuitos foi o alimentador-teste IEEE 34 barras em que os testes preliminares do algoritmo para este sistema resultaram em uma redução considerável para a múltipla estimação. / The complexity of distribution systems is an intrinsic characteristic from them, making it difficult to apply conventional fault locating methods, whose efficiency is decisive for the improvement of power quality and service indexes provided by the utilities. This work aims to present an alternative approach for fault location in aerial distribution feeders with radial topology. The tool consists of the development of a decision aid algorithm for conventional fault location techniques capable of reducing the multiple estimation problem. This proposal takes place in a context where a Smart Grids-based infrastructure is envisaged. This tool consists of a pre-processing of analog signals and feature extraction for later use of the DAMICORE (Data Mining of Code Repositories) technique to find connections between events, forming groups of similar events, and helping the Fault Location. The system used to simulate short circuits was the IEEE 34 Node Test Feeder and, for this system, the algorithm responded with a significant reduction of multiple estimations.

A complexidade dos sistemas de distribuição é uma característica intrínseca dos mesmos, isso dificulta a aplicação de ferramentas convencionais para localização de faltas, cuja eficiência é determinante para melhoria dos índices de qualidade de energia e serviços prestados por concessionárias de energia. Este trabalho tem o objetivo de apresentar uma abordagem alternativa para a localização de faltas em alimentadores de distribuição aéreos com topologia radial. A proposta consiste no desenvolvimento de um algoritmo de auxílio à decisão para técnicas convencionais de localização de faltas, capaz de reduzir o problema da múltipla estimação. Esta proposta se dá num contexto onde é vislumbrado uma infraestrutura de Smart Grids consolidada. A ferramenta consiste em um pré-processamento de sinais analógicos e extração de parâmetros para posterior uso da técnica DAMICORE (do inglês Data Mining of Code Repositories) a fim de encontrar relações entre os eventos, agrupando aqueles que forem similares, de modo a auxiliar na localização de faltas. O sistema utilizado para a simulação de curtos-circuitos foi o alimentador-teste IEEE 34 barras em que os testes preliminares do algoritmo para este sistema resultaram em uma redução considerável para a múltipla estimação. / The complexity of distribution systems is an intrinsic characteristic from them, making it difficult to apply conventional fault locating methods, whose efficiency is decisive for the improvement of power quality and service indexes provided by the utilities. This work aims to present an alternative approach for fault location in aerial distribution feeders with radial topology. The tool consists of the development of a decision aid algorithm for conventional fault location techniques capable of reducing the multiple estimation problem. This proposal takes place in a context where a Smart Grids-based infrastructure is envisaged. This tool consists of a pre-processing of analog signals and feature extraction for later use of the DAMICORE (Data Mining of Code Repositories) technique to find connections between events, forming groups of similar events, and helping the Fault Location. The system used to simulate short circuits was the IEEE 34 Node Test Feeder and, for this system, the algorithm responded with a significant reduction of multiple estimations.

Os smart grids são tidos como a nova geração dos sistemas elétricos de potência, combinando avanços em computação, sistemas distribuídos e inteligência artificial para prover maiores funcionalidades sobre acompanhamento em tempo real da demanda e do consumo de energia elétrica, gerenciamento em larga escala de geradores distribuídos, entre outras, a partir de um sistema de controle distribuído sobre a rede elétrica. Esta abordagem alteraria fundamentalmente a maneira como se dá o planejamento e a operação de sistemas de distribuição, e há grandes possibilidades de pesquisa e desenvolvimento possibilitada pela busca de implementação destas funcionalidades. Com esse cenário em vista, o presente trabalho utiliza uma abordagem a partir do uso de sistemas multiagentes para estudar o gerenciamento de sistemas de distribuição, do ponto de vista da reconfiguração da topologia da rede, simulando as características de um smart grid. Nesta dissertação, foi desenvolvido um sistema multiagente para simulação computacional de um sistema de distribuição elétrico do tipo smart grid, buscando executar a reconfiguração topológica do sistema a partir de dados de carga capturados de forma distribuída pelos agentes dispersos na rede elétrica. Espera-se que o desenrolar da pesquisa conduza à vários estudos sobre algoritmos e técnicas que melhor implementem tais funcionalidades a serem transpostas para um ambiente de produção. / Smart grids are taken as the new generation of electric power systems, combining advances in computing, distributed systems and artificial intelligence to provide more features on real-time monitoring of demand and consumption of electricity, managing large-scale distributed generators, among others, from a distributed control system on the grid. This approach fundamentally alter the way how is the planning and operation of distribution systems, and there are great possibilities for research and development offered in the quest to implement these features. With that environment, this text uses an approach through the use of multi-agent systems to study the management of the distribution system, from the reconfiguration of grid topology, simulating the characteristics of a smart grid. In this text, was developed a multiagent system to computational simulation of a distribution system like smart grid to topological reconfiguration, from datas collected for agents in electrical grid. It is expected that the conduct of research leads to several studies about better algorithms and techniques that would implement such functionality in a production environment.

L'architecture physique des réseaux électriques et les structures organisationnelles des industries électriques survenues à la suite des réformes ont principalement été conçues en fonction des caractéristiques relatives aux infrastructures de transport et aux parcs de production. Or, les nouveaux enjeux de transition énergétique, associés aux progrès réalisés dans les équipements de communication et d'automatisation, plaident pour une plus grande participation des activités décentralisées de production et des consommateurs finals. Nous constatons que les systèmes électriques sont à l'aube d'évolutions majeures qui partagent une double caractéristique. La première et que ces évolutions nécessiteront des apports en capitaux considérables pour adapter et moderniser les réseaux de distribution. La seconde est que l'activation de l'aval de la chaîne électrique libère des gains d'efficacité économique actuellement inexploités, mais est également porteuse de contraintes nouvelles.Partant de ce constat, l'objectif de cette thèse est double. Il s'agit dans un premier temps de proposer une analyse théorique des instruments de régulation qui encadrent et orientent les dépenses des opérateurs réseau. Nous cherchons en nous appuyant sur la littérature à caractériser les outils de régulation les mieux adaptés à l'investissement en smart technologies. Puisqu'il est nécessaire de confronter l'analyse théorique aux faits, nous entreprenons de recenser les gains clés d'efficacité économique escomptés de la généralisation des smart grids. Nous illustrons chacun de ces gains par une étude empirique qui nous permet de comparer les résultats issus de notre analyse théorique aux schémas de régulation existants et de formuler un certain nombre de recommandations.Le second objectif de la thèse se concentre sur les impacts de la diffusion de programmes de la gestion de la demande. Le raisonnement adopté s'articule autour de deux constats. D'importants bénéfices sont attendus des réductions substantielles des pointes de demande, réductions qui se traduisent par de moindres opportunités de profit pour les producteurs. Il s'agira de réaliser une estimation des gains et pertes que l'on peut attendre de la gestion de la demande. Pour cela, nous développons et utilisons un modèle d'optimisation dans lequel nous intégrons plusieurs pays interconnectés dotés de parcs de production différenciés.La thèse montre que les cadres de régulation dominants actuellement sont limités dans leur portée incitative pour favoriser un investissement efficace dans la technologie, ce qui est susceptible de retarder son introduction. La quantification des impacts de la gestion de la demande montre quant à elle que des efficacités significatives peuvent être activées via la généralisation de ces mesures. Toutefois, elles posent des problèmes nouveaux dans la rémunération de l'existant, l'adéquation future des capacités, et souligne l'antagonisme potentiel entre perte de revenus pour les unités de pointe les plus réactives et développement des énergies bas carbone.De toute évidence, les interrogations soulevées par le développement des smart grids nécessitent que soit mené un débat politique éclairé puisque l'industrie électrique est indispensable à nos sociétés. Parmi le nombre considérable d'éléments à aborder, arriveront en bonne place les questions relatives au financement des projets d'investissement et à l'inclusion des nouvelles sources de flexibilité induites par l'adoption de la technologie dans les marchés électriques libéralisés. / The physical architecture of electricity grids and the organizational structure of power systems implemented after the reforms have traditionally be achieved according to the characteristics of the transmission infrastructures and power mixes. However, the new challenges related to energy transition favor a greater participation of decentralized generation and final consumers to system exploitation and competitive markets. This latter participation is made possible thanks to recent innovations in the fields of communication and remote control technologies.Significant evolutions are expected in power industries that share common characteristics. First, these evolutions suppose massive capital investments to modernize and adapt current power distribution grids. Second, it is expected the activation of distribution grids and final consumers will unleash substantial unexploited economic efficiency gains as well as impose new constraints.Taking these simple facts as a starting point, the objective of the thesis is twofold. In the first place, we provide a theoretical analysis of the regulatory instruments that monitor the system operators' expenses. Relying on the literature, we aim at characterizing what regulatory tools and incitation are suitable for investing in smart grids technologies. Since it is necessary to compare theoretical formulation to facts, we use an empirical approach that allows us to designate key benefits pursued by the development of smart grids and to compare our theoretical results with practical regulatory applications. Our findings eventually allow us to formulate recommendations.In the second place, the thesis focuses on the impacts of demand-side management during peak periods. We structure our approach around two general observations. Large benefits should be generated in lowering substantially peak demand. However, such situation also creates losses of profit for generators. We provide an estimation of efficiency gains and revenue losses induced by peak shedding. To this end, we develop and use a linear optimization model and expand our analysis to interconnected countries endowed with differentiated generation means.The thesis shows dominant regulatory frameworks are unsuited to provide the necessary sets of incentive to efficiently develop smart technologies. This can cause delays in their integration to power grids. The quantitative evaluation of the impacts generated by demand-side management shows significant efficiency gains are achievable through final consumers' flexibility. However, such measures create new discrepancies regarding installed capacities profitability, future capacity adequacy, and highlight potential antagonism between missing money for flexible peak capacities and the development of low carbon energies.It is clear the issues raised by the development of smart grids call for informed public debate as power industries are essential to our societies. Among the considerable amount of elements to discuss, issues relative to financing the investment projects and the inclusion of the new sources of flexibility induced by the technology in competitive markets will be of priority.