Despacho econômico e ambiental em sistemas de potência com inserção de geração eólica utilizando algoritmos quânticos

• Autor(a) principal: JESUS, Alan Penha de
• Data de Publicação: 2024
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/5385
• topic: despacho econômico e ambiental;; meta-heurísticas;; energia eólica;; otimização.; economic and environmental dispatch;; metaheuristic;; wind power;; optimization.; Sistemas Elétricos de Potência

This work addresses environmental economic dispatch as a multi-objective optimization problem. To solve this problem, the potential of quantum meta-heuristics was explored, including the Quantum Grey Wolf Optimizer (QGWO), the Quantum Particle Swarm Optimizer (QPSO), and the Quantum Flower Pollination Algorithm (QFPA), which are advanced methods inspired by natural processes to seek efficient solutions, aiming to compare the performance and suitability of these algorithms. The experiments were conducted on two test systems from the Institute of Electrical and Electronics Engineers (IEEE), one with six units and another with 14 generating units. Both systems were analyzed considering different load scenarios. Additionally, scenarios with the integration of wind energy, modeled by the Weibull distribution to capture the stochastic nature of the wind, were explored. The results showed that QFPA outperformed the other evaluated meta-heuristics, providing higher quality solutions based on the minimum, average, and standard deviations of the economic generation cost, with reductions of up to 5.6% for the minimum economic cost, and 32.9% for the minimum environmental cost, for the environmental economic dispatch problem solved for the test systems. This suggests that QFPA could be the preferred choice for solving similar challenges in electrical power systems, especially when considering the integration of renewable sources, such as wind energy, in the optimization process.