The high costs and high risks of transporting fuel to combat zones make fuel conservation a dire need for the US military. A towable hybrid electric system can help relieve these issues by replacing less fuel efficient standalone diesel generators to deliver power to company encampments. Currently, standalone generators are sized to meet peak demand, even though peak demand only occurs during short intervals each day. The average daily demand is much less, meaning generators will be running inefficiently most of the day. In this thesis, a simulation is created to help determine an optimal system design given a load profile, size and weight constraints, and relocation schedule. This simulation is validated using test data from an existing system. After validation, many hybrid energy components are considered for use in the simulation. The combination of components that yields the lowest fuel consumption is used for the optimal design of the system. After determining the optimal design, a few design parameters are varied to analyze their effect on fuel consumption. The model presented in this thesis agrees with the test data to 7% of the measured fuel consumption. Sixteen system configurations are run through the simulation and their results are compared. The most fuel efficient system is the system that uses a 3.8kW diesel engine generator with a 307.2V, maximum capacity LiFeMgPO? battery pack. This system is estimated to consume 21% less fuel than a stand-alone generator, and up to 28% less when solar power is available. / Master of Science

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-12-21T11:49:01Z No. of bitstreams: 1 Fausto Stefanello Tomazi_.pdf: 1429293 bytes, checksum: 4e85a45b348c5d3cbf6a7e9e13e1be3b (MD5) / Made available in DSpace on 2016-12-21T11:49:02Z (GMT). No. of bitstreams: 1 Fausto Stefanello Tomazi_.pdf: 1429293 bytes, checksum: 4e85a45b348c5d3cbf6a7e9e13e1be3b (MD5) Previous issue date: 2016-09-23 / Nenhuma / Os sistemas de potência desempenham um papel fundamental na economia de uma nação, fornecendo energia elétrica com qualidade e sem interrupções a população. Para que isto seja possível grandes investimentos no setor são aplicados para garantir o fornecimento. No entanto, qualquer equipamento está sujeito a falhas, e analisar o impacto que falhas em equipamento afetam o fornecimento é uma das tarefas executadas pelos centros de controle, chamada de Análise de Segurança. Desta forma, os centros de controle são responsáveis por realizar planos de contingência para que em caso de algum equipamento saia de operação o impacto sofrido pela rede seja o menor possível. Uma importante tarefa da Análise de Segurança é a Seleção de Contingências. Esta tarefa sendo encarregada de selecionar os equipamentos mais importantes do sistema para que a tarefa de Análise de Segurança possa criar planos de prevenção caso os respectivos equipamentos saiam de operação. Os grandes sistemas elétricos existentes hoje são compostos de milhares de equipamentos, e uma análise mais detalhada para cada equipamento é algo de difícil resolução, sendo neste cenário que a seleção de contingência ganha importância. A Seleção de Contingência é encarregada de buscar e classificar as restrições mais importantes da rede, porem para redes de grande porte com milhares de itens, analisar o impacto de cada item é uma tarefa que pode levar muito tempo, não permitindo que o cálculo seja efetuado durante a operação do sistema. Desta forma faz-se necessário executar a Seleção de Contingências de forma eficiente e eficaz. Este estudo propõe o desenvolvimento do algoritmo metaheurístico de Simulated Annealing a fim de que a seleção de contingência seja executada de forma que atenda todas as restrições de tempo impostas pelos centros de controle. Nos experimentos é possível verificar que após uma sintonia de parâmetros para a instancia do problema abordado, os resultados encontrados atende as restrições dos centros de controle e também é possível visualizar que os resultados são ligeiramente melhores que resultados de trabalhos encontrados na literatura, onde o mesmo problema é abordado pela metaheurística do Algoritmo Genético. / Power systems play a key role in a nation's economy by providing quality, uninterrupted power to the population. For this to be possible large investments in the sector are applied to guarantee the supply. However, any equipment is subject to failures, and analyzing the impact that equipment failures affect supply is one of the tasks performed by control centers, called Safety Analysis. In this way, the control centers are responsible for carrying out contingency plans so that in the event of any equipment leaving the operation the impact suffered by the network is as small as possible. An important task of Security Analysis is the Selection of Contingencies. This task is in charge of selecting the most important equipment in the system so that the Security Analysis task can create prevention plans if the respective equipment goes out of operation. The large electrical systems that exist today are made up of thousands of equipment, and a more detailed analysis for each equipment is difficult to solve, and in this scenario contingency selection is important. The Contingency Selection is responsible for searching and classifying the most important restrictions of the network, but for large networks with thousands of items, analyzing the impact of each item is a task that can take a long time, not allowing the calculation to be performed During system operation. In this way it is necessary to perform the Contingency Selection efficiently and effectively. This study proposes the development of the metaheuristic algorithm of Simulated Annealing in order that the contingency selection is performed in a way that meets all the time constraints imposed by the control centers. In the experiments it is possible to verify that after a tuning of parameters for the instance of the problem approached, the results found meets the control center constraints and it is also possible to visualize that the results are slightly better than results of works found in the literature, where the same Problem is addressed by the metaheuristic of the Genetic Algorithm.