Alocação e dimensionamento ótimo de geração distribuída utilizando o fluxo de potência intervalar

• Autor(a) principal: Nogueira, Wallisson Calixto
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFG
• dARK ID: ark:/38995/001300000268m
• Texto Completo: http://repositorio.bc.ufg.br/tede/handle/tede/11853
• topic: Otimização; Incertezas; Fluxo de potência intervalar; Optimization; Uncertainty; Interval load flow; ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIA

Modern Power Systems must deal with high levels of uncertainty in their planning and operation, these uncertainties are mainly due to variations in loads and distributed generation introduced by new technologies. This scenario brings new challenges for system planners and operators who need new tools to carry out more assertive analysis of the state of the network. This work presents an optimization methodology capable of considering uncertainties in the problem of sizing and sitting distributed generation in the networks. The proposed methodology uses the interval power flow (ILF) in order to add uncertainties to the combinatorial optimization problem that is solved through the meta-heuristics Symbiotic Organism Search (SOS) and Particle Swarm Optimization (PSO) for performance comparison purposes. The addition of uncertainties by ILF is validated by the probabilistic power flow (PLF) solved by Monte Carlo Simulation (MCS). This methodology was implemented in Python®, and was applied in the IEEE 33-bus, IEEE 34-bus and IEEE 69-bus test networks where distributed generation sizing and sitting problems were solved in order to minimize technical losses and to improve the voltage levels of the network. For the addition of uncertainties, the results obtained from the proposed ILF in the tested networks are compatible with those obtained by the PLF, thus showing the robustness and applicability of the proposed method. For the solution of the optimization problem, the SOS meta-heuristic proved to be robust, since it was able to find the best solutions that present the lowest losses, keeping the voltage levels regulated to the predetermined levels. On the other hand, the PSO meta-heuristic presents less satisfactory results, because for all the systems tested, the solution has a lower quality than that found by SOS, thus showing that the PSO algorithm presents difficulties to escape the minimum locations found during the simulation.