Despacho econômico dinâmico sob incertezas com inclusão de fontes de energia renovável

• Autor(a) principal: CANTANHEDE, André Carlos dos Santos
• Data de Publicação: 2021
• 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/3481
• topic: despacho econômico dinâmico;; energia eólica;; QPSO;; PSO;; mercado elétrico; dynamic economic dispatch;; wind energy;; QPSO;; PSO;; electric market; Engenharia Elétrica

With the increase in renewable energy generation, several challenges arise for economic dispatch and changes in electricity markets. Dynamic economic dispatch is a complex optimization problem used to determine the generation schedule between the system's units, in a way that ensures the demand is met safely and reliably within a time horizon. The purpose of economic dispatch is to reduce marginal generation costs under restriction of operation, ramps and security of the units and the transmission system. With the addition of renewable energy sources and increasingly interconnected systems, economic dispatch problems have become increasingly complex. Thus, studies have analyzed new forms of optimization techniques for different forms of the problem, with the aim of improving the convergence point and computational time. This work aims to analyze the effects of the insertion of wind energy in dynamic economic dispatch. For this, the Quantum Particle Swarm Optimization (QPSO) meta heuristic was used and the cost of wind farms was calculated based on its analytical probabilistic production model. The proposed methodology was used in a system with 10 generators and a wind farm with meteorological data from Parnaíba-PI. For the study, different scenarios were analyzed in which the test system is linked to different wind farms, with generation concentrated in one bus and divided into two buses. In this study it is seen the influence of wind energy on the cost of operating the system. Furthermore, the uncertainty of wind energy generates costs linked to the forecast of expected generation of the wind farm combined with the reallocation of this load in the system. And finally the efficiency of the QPSO algorithm in the formulated DED problem.