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Algoritmos Genéticos para Otimização de soluções integrando carregadores de veículos elétricos, auto-produção e armazenamento de energia
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Advisor(s)
Abstract(s)
É crescente a importância da energia renovável e da mobilidade elétrica, para enfrentar os
desafios ambientais e energéticos atuais. Acerca da mobilidade elétrica, o número crescente
de veículos elétricos a bateria e veículos elétricos híbridos plug-in, traz a necessidade de
mais infraestrutura de carregamento, destes tipos de veículos, nas cidades e nas estradas,
onde tais infraestrutas precisam estar conectadas à rede elétrica de distribuição, resultando
em restrições como disponibilidade de potência. Este trabalho faz uma abordagem acerca
do dimensionamento de uma estação de carregamentos rápidos de veículos elétricos, lidando
com esse desafio através da implementação de um sistema de armazenamento de energia
baseado em bateria BESS e considerando a inserção no sistema de uma fonte de energia
renovável (energia solar). Pretendemos minimizar os custos globais de energia, para evitar
atualizações futuras na infraestrutura e potencializar a integração de recursos de energia
renovável. A metodologia adotada baseia-se no Algoritmo BRKGA, um método aplicado
em problemas de otimização combinatória, para encontrar soluções ótimas que levem em
consideração a carga de carros elétricos, a geração de energia renovável local e o armazenamento
eficiente de energia com custos mínimos. Através de estudos de caso com diferentes
cenários, implementamos a metodologia proposta. Foram obtidos resultados para o dimensionamento
de uma estação de recarga com auto produção de energia de fonte renovável que
minimizam os custos de energia. Este trabalho, destaca a importância contínua da pesquisa
em Algoritmos Genéticos e seu papel relevante na resolução de problemas complexos relacionados
à energia e mobilidade elétrica, pavimentando o caminho para um futuro mais
sustentável e ecologicamente consciente.
The importance of renewable energy and electric mobility is growing to face current environmental and energy challenges. Regarding electric mobility, the growing number of battery electric vehicles and plug-in hybrid electric vehicles brings about the need for more charging infrastructure for these types of vehicles in cities and on roads, where such infrastructure needs to be connected to the electrical grid. distribution, resulting in restrictions such as power availability. This work takes an approach to the sizing of a fast charging station for electric vehicles, dealing with this challenge through the implementation of an energy storage system based on a BESS battery and considering the insertion into the system of a renewable energy source (energy solar). We aim to minimize global energy costs, to avoid future infrastructure upgrades and enhance the integration of renewable energy resources. The methodology adopted is based on the BRKGA Algorithm, a method applied to combinatorial optimization problems, to find optimal solutions that take into account the charging of electric cars, local renewable energy generation and efficient energy storage with minimum costs. Through case studies with different scenarios, we implemented the proposed methodology. Results were obtained for the design of a charging station with self-production of energy from renewable sources that minimize energy costs. This work highlights the continued importance of research in Genetic Algorithms and their relevant role in solving complex problems related to energy and electric mobility, paving the way for a more sustainable and ecologically conscious future.
The importance of renewable energy and electric mobility is growing to face current environmental and energy challenges. Regarding electric mobility, the growing number of battery electric vehicles and plug-in hybrid electric vehicles brings about the need for more charging infrastructure for these types of vehicles in cities and on roads, where such infrastructure needs to be connected to the electrical grid. distribution, resulting in restrictions such as power availability. This work takes an approach to the sizing of a fast charging station for electric vehicles, dealing with this challenge through the implementation of an energy storage system based on a BESS battery and considering the insertion into the system of a renewable energy source (energy solar). We aim to minimize global energy costs, to avoid future infrastructure upgrades and enhance the integration of renewable energy resources. The methodology adopted is based on the BRKGA Algorithm, a method applied to combinatorial optimization problems, to find optimal solutions that take into account the charging of electric cars, local renewable energy generation and efficient energy storage with minimum costs. Through case studies with different scenarios, we implemented the proposed methodology. Results were obtained for the design of a charging station with self-production of energy from renewable sources that minimize energy costs. This work highlights the continued importance of research in Genetic Algorithms and their relevant role in solving complex problems related to energy and electric mobility, paving the way for a more sustainable and ecologically conscious future.
Description
Keywords
Veículos elétricos Energias renováveis Algoritmos genéticos Sistemas de armazenamento de energia em baterias Carregamento Electric vehicles Renewable energies Genetic algorithms Energy storage in bateries Charging