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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.
UMA NOVA SOLUÇÃO PARA A OTIMIZAÇÃO DO DESPACHO ECONÔMICO E AMBIENTAL UTILIZANDO METAHEURÍSTICAS DA COMPUTAÇÃO BIO-INSPIRADA
(Universidade Federal do Pará, 2016) NASCIMENTO, Manoel Henrique Reis; NUNES, Marcus Vinícius Alves
Due to the significant industrial growth in Northern Brazil, especially at the Industrial Pole of Manaus (PIM), there has been an increased need for energy generation, which in this region is provided by thermoelectric plants (UTEs) in over 90% of its total. Thus, it became necessary to use computational tools that help specialists and operators make decisions about the optimal power dispatch, contemplating not only cost reduction but also reducing atmospheric pollution levels. This thesis presents a new solution proposal for the classic optimization problem of Economic Dispatch (ED) and Economic and Environmental Dispatch (EAD), using several Deterministic methods (Iteration Lambda, Quadratic Programming, Newton method) and Bio-Inspired Heuristic methods (Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, Grey Wolf Optimizer, and Artificial Bee Colony), as well as the Non-dominated Sorting Genetic Algorithm (NSGA-II and NSGA-III) for the EAD. The proposal includes shutting down generators with higher operating costs to reduce fuel costs and pollutant emissions like NOx and CO₂. The robustness of the methodology was validated with a set of ten generating units and three benchmark systems described in the literature, showing significant advantages of the new proposed solution.