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URI permanente para esta coleçãohttps://rigalileo.itegam.org.br/handle/123456789/175

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SOLUÇÃO PARA O DESPACHO ECONÔMICO AMBIENTAL DE UM SISTEMA DE GERAÇÃO TÉRMICA POR RECOZIMENTO SIMULADO
(Universidade Federal do Pará, 2018) BRITO JÚNIOR, Jorge de Almeida; NUNES, Marcus Vinícius Alves
In recent years, population and government concerns about environmental protection have increased, while the use of fossil fuels for electricity generation remains high due to their availability and the consolidated technology of thermal plants. In this context, it has become common to adopt methodologies to optimize thermoelectric plant operations, considering both fuel costs and pollutant emissions. This PhD thesis aims to apply multiobjective optimization to the environmental economic dispatch (DEA) of thermal plants using simulated annealing, comparing the obtained results with other metaheuristic techniques. This tool was applied with a fitness function involving two objectives — cost and emissions — to find the optimal solution, taking into account the shutdown of less efficient engines, thus ensuring the reduction of financial costs and pollution.
USO DE TÉCNICAS DE OTIMIZAÇÃO METAHEURISTICAS APLICADAS AO DESPACHO ECONÔMICO E AMBIENTAL DE USINAS TERMOELÉTRICAS E FOTOVOLTAICAS
(Universidade Federal do Pará, 2023) Santos, Eliton Smith dos; NUNES, Marcus Vinicius Alves
Economic Load Dispatch (ELD) aims at planning and operating Thermoelectric Power Plants (TPP) to meet energy demand at the lowest cost. However, traditional ELD does not consider environmental costs, which are crucial under current sustainability concerns. Thus, this thesis proposes the use of metaheuristic techniques — Ant Lion Optimizer (ALO), Dragonfly Algorithm (DA), and Differential Evolution (DE) — to optimize the Environmental Economic Dispatch (EED), integrating photovoltaic solar generation. The proposal includes an intelligent algorithm that adjusts TPP motor power based on demand, allowing the shutdown of less efficient motors and reducing emissions. Simulations performed in the MATLAB environment, with a hybrid model of six Generating Units (GU) and thirteen Solar Photovoltaic Plants (SPP), demonstrated that the DE technique yielded the best results, reducing fossil fuel consumption by 3.02% and atmospheric emissions by 1.42%.