There have been significant advances in communication technologies over the last decade, such as cellular networks, Wi-Fi, and optical communication. Not only does the technology impact peoples’ everyday lives, but it also helps cities prepare for power outages by collecting and exchanging data that facilitates real-time status monitoring of transmission and distribution lines. Smart grids, contrary to the traditional utility grids, allow bi-directional flow of electricity and information, such as grid status and customer requirements, among different parties in the grid. Thus, smart grids reduce the power losses and increase the efficiency of electricity generation and distribution, as they allow for the exchange of information between subsystems. However, smart grids is not resilient under extreme conditions, particularly when the utility grid is unavailable. With the increasing penetration of the renewable energy sources (RES) in smart grids, the uncertainty of the generated power from the distributed generators (DGs) has brought new challenges to smart grids in general and smart microgrids in particular. The rapid change of the weather conditions can directly affect the amount of the generated power from RES such as wind turbine and solar panels, and thus degrading the reliability and resiliency of the smart microgrids. Therefore, new strategies and technologies to improve power reliability,sustainability, and resiliency have emerged. To this end, in this thesis, we propose a novel framework to improve the smart microgrids reliability and resiliency under severe conditions. We study the transition to the grid-connected operational mode in smart microgrids,in the absence of the utility grid, as an example of emergency case that requires fast and accurate response. We perform a comparative study to accurately predict upcoming grid-connected events using machine learning techniques. We show that decision tree models achieve the best average prediction performance. The packets that carry the occurrence time of the next grid-connected transition are considered urgent packets. Hence, we per-form an extensive study of a smart data aggregation approach that considers the priority of the data. The received smart microgrids data is clustered based on the delay-sensitivity into three groups using k-means algorithm. Our delay-aware technique successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small-cell base stations. As a mitigation action of the utility grid unavailability, we use the electrical vehicles (EVs) batteries as mobile storage units to cover smart microgrids power needs until the utility grid recovery. We formulate a Mixed Integer Linear Programming (MILP) model to find the best set of electrical vehicles with the objective of minimum cost. The EVs participating in the emergency power supply process are selected based on the distance and throughput performance between the base station and the EVs

Game theory has its applications in various domains, but has only recently been applied to study open problems in smart microgrids. A simple microgrid system with a small wind farm, a storage facility and an aggregate load entity is studied here using a non-cooperative game-theoretic framework. The framework developed is used to study the behavior of rational market participants (players), namely wind power producer and energy storage. The framework is implemented to find the existence of any Nash equilibria and see if cooperation is a natural outcome of the game. If cooperation is not self-enforcing then usefulness of the framework to find the conditions for cooperation is presented. It must be noted that cooperation is not automatically guaranteed as the payoff of the energy storage operator is dependent on the strategy employed by the wind power producer. Similarly, the payoff for the wind power producer is highly intertwined with the strategy employed by the energy storage operator. Historical weather and market data is used to calculate expected payoffs for each possible combination of strategies. The results are presented in the form of payoff matrices and the best response algorithm and/or elimination of dominated strategies is used to find the Nash equilibrium. Sensitivity of the Nash equilibrium to various storage parameters like storage size, charging/discharging limits, charging/discharging efficiency, and other market parameters like energy imbalance penalties, efficiency of up/down regulation, and electricity market prices is studied and necessary conditions for cooperation are presented. / Master of Science

Made available in DSpace on 2017-07-10T17:11:49Z (GMT). No. of bitstreams: 1 2015 - Cristian Adolfo Alvez2.pdf: 870298 bytes, checksum: e905359b074a87b04a67ea5befb19f4c (MD5) Previous issue date: 2015-03-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The microgrids may be considered as small power systems that operate autonomously and automatically, using technologies linked to it known as distributed energy resources (DERs), being demand, one of this resources. These networks are characterized by intensive use of information, communication and automation technologies, allowing meet demand in form efficient and optimally. However, implement, expand and operate a microgrid brings various economic, technical and operational challenges that must be faced, being one of those challenges the selection and operation of DERs. Thus, this work presents an optimization model, with the objective of analyzing the impact of input parameters on the behavior of the variables involved in selection and operations of these resources. The uncertainties in demand and renewable resources were treated through a scenario tree while for risk estimation was used the value at risk (VaR). The mathematical formulation constitutes a mixed integer linear programming model that was implemented in GAMS language and solved by CPLEX solver. Through simulations was possible to observe the economic benefits that can be obtained through use of DERs, highlight the impact that can produce the intermittent nature of renewable resources on operating costs, and also evidence the importance to have information regards of risk in situations of uncertainty. The results of the simulations show the tool's features developed as an aid when decisions must be made regarding the deployment of DERs and the optimized energy management in a microgrid. As features to highlight, this model operates independently of the energy consumer profile and also allows to perform various analyzes with respect to the influence of the input parameters on the decision variables. / As microrredes podem ser consideradas como pequenos sistemas de potência que operam de maneira autônoma e automática, utilizando tecnologias, conectadas a elas, conhecidas como recursos energéticos distribuídos (REDs), sendo a própria demanda um desses recursos. Estas redes se caracterizam pelo uso intensivo de tecnologias de informação, comunicação e automação, permitindo atender a demanda de forma eficiente e otimizada. No entanto, implementar, expandir e operar uma microrrede traz consigo vários desafios econômicos, técnicos e operacionais que devem ser enfrentados, sendo um deles a seleção e operação dos REDs. Como consequência disso, neste trabalho apresenta-se um modelo de otimização para a seleção e operação de REDs, com o objetivo de analisar o impacto que provocam os diversos parâmetros de entrada no comportamento das variáveis envolvidas na seleção e modo de operação desses REDs. As incertezas na demanda e nos recursos renováveis foram tratadas através de uma arvore de cenários enquanto que para a estimação do risco se utilizou o valor em risco (VaR). A formulação matemática resultante se constitui em um modelo de programação linear inteira mista que foi implementado na linguagem GAMS e resolvido com o solver CPLEX. Através dos resultados de simulação foi possível observar os benefícios econômicos que podem obter-se mediante a utilização de REDs, assim como destacar o impacto que pode produzir a natureza intermitente dos recursos renováveis sobre os custos de operação e também evidenciar a importância de dispor informação do risco em situações de incerteza. Os resultados obtidos corroboram as funcionalidades da ferramenta desenvolvida como auxilio no momento de tomar decisões em relação à implantação de REDs e à gestão otimizada de energia em uma microrrede. Como característica a destacar do modelo, este opera independentemente do qual seja o tipo de demanda do consumidor e permite efetuar diversas análises a respeito da influencia dos parâmetros de entrada sobre as variáveis de decisão.