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Authors
Advisor(s)
Abstract(s)
A necessidade por energia elétrica é crescente e, como forma de suprir as projeções analisadas pelas entidades energéticas e garantir o cumprimento dos acordos climáticos está cada vez mais em voga a transição e a eficiência energética. Com isso, estudos relacionados a fluxo de energia tem se intensificado no planeamento de expansão, operação do sistema, otimização do sistema de energia e estabilidade em tempo real, emergências e investigação do sistema. Existem diversas formas para calcular o fluxo de potência que variam desde a escolha dos métodos matemáticos (Newton Raphson, desacoplamento rápido, linearizado, dentre outros) até a escolha da metodologia de otimização (enxame de partículas, algoritmo genético, dentre outros), com o intuito de encontrar a maneira mais eficiente de transmitir energia por longas distâncias, minimizando as perdas, garantindo a estabilidade da rede elétrica e que a eletricidade seja entregue nos níveis de tensão apropriados. Neste trabalho é realizada uma comparação entre as metodologias de otimização por enxame de partículas e otimização por algoritmo genético a partir dos dados obtidos do método de Newton-Raphson para reduzir as perdas ativas do fluxo de potência nos Sistemas IEEE-14 barras e IEEE-30 barras.
The necessity for electricity is growing and, as a way to meet the projections analyzed by energy entities and ensure compliance with climate agreements, transition and energy efficiency are increasingly in vogue. As a result, studies related to energy flow have intensified in expansion planning, system operation, energy system optimization and real-time stability, emergencies and system investigation. There are several ways to calculate the power flow, ranging from the choice of mathematical methods (Newton Raphson, fast decoupling, linearized, among others) to the choice of optimization methodology (particle swarm, genetic algorithm, among others), with in order to find the most efficient way to transmit energy over long distances, minimizing losses, ensuring the stability of the electrical grid and that electricity is delivered at the appropriate voltage levels. In this work, a comparison is made between the methodologies of particle swarm optimization and genetic algorithm optimization based on data obtained from the Newton-Raphson method to reduce active power flow losses in the IEEE-14 bus and IEEE-30 systems. bars.
The necessity for electricity is growing and, as a way to meet the projections analyzed by energy entities and ensure compliance with climate agreements, transition and energy efficiency are increasingly in vogue. As a result, studies related to energy flow have intensified in expansion planning, system operation, energy system optimization and real-time stability, emergencies and system investigation. There are several ways to calculate the power flow, ranging from the choice of mathematical methods (Newton Raphson, fast decoupling, linearized, among others) to the choice of optimization methodology (particle swarm, genetic algorithm, among others), with in order to find the most efficient way to transmit energy over long distances, minimizing losses, ensuring the stability of the electrical grid and that electricity is delivered at the appropriate voltage levels. In this work, a comparison is made between the methodologies of particle swarm optimization and genetic algorithm optimization based on data obtained from the Newton-Raphson method to reduce active power flow losses in the IEEE-14 bus and IEEE-30 systems. bars.
Description
Mestrado de dupla diplomação com o Instituto Federal de Educação, Ciência e Tecnologia da Bahia
Keywords
Fluxo de potência Perdas ativas Otimização por enxame de partículas Algoritmo genético