Técnicas de inteligência artificial aplicadas na análise de mercados elétricos com inserção de geração eólica e de sistemas de armazenamento de energia nas redes elétricas de potência.

• Ano de defesa: 2017
• Autor(a) principal: SARAIVA, Felipe Oliveira Silva
• Orientador(a): PAUCAR, Vicente Leonardo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Preços marginais locacionais; técnicas de inteligência artificial; geração eólica; sistemas de armazenamento de energia; sistemas elétricos de potência
• Palavras-chave em Inglês: Locational marginal prices; artificial intelligence techniques; wind farms; energy storage systems; electric power systems
• Área do conhecimento CNPq: Sistemas Elétricos de Potência
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/1774

The locational marginal prices (LMPs) are essential financial guidelines for the electricity industry, which orientates most of the projects and deliberations in electrical market environments. In current scenario of the electricity markets, wind power plants and energy storage systems have been revealing itself as feasible and relevant electrical energy supply alternatives. In this work a generic methodology based on artificial intelligence (AI) techniques is formulated and applied to the calculation and decomposition of LMPs of electric power systems (EPS) with the insertion of energy storage systems and wind farms. In the proposed AI-based methodology the optimal power flow (OPF) model, on which the calculation and decomposition of LMP is based, considers the wind behavior profile volatility, the risks of wind power levels previously scheduled, and the energy storage systems operative peculiarities. The proposed AI-based methodology takes into account the mathematical and computational models of the particle swarm optimization (PSO) algorithm. This proposal was properly implemented and applied for the computation and decomposition of LMPs of test systems and considering different operative scenarios involving conventional power plants, wind farms, and energy storage systems.