Ferramenta de Auxílio na Formação de Estratégias de Oferta em Leilões de Longo Prazo de Energia Elétrica

• Ano de defesa: 2012
• Autor(a) principal: Santos, Sergio Augusto Trovão
• Orientador(a): SAAVEDRA MENDEZ, Osvaldo Ronald
• Banca de defesa: Casas, Vicente Leonardo Paucar
• 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: Engenharia
• País: BR
• Palavras-chave em Português: Mercados de Energia Elétrica; Comercialização de Energia Elétrica; Leilões de Energia; Estratégias de Oferta; Gerenciamento de Riscos; Otimização Numérica; Inteligência Computacional, Computação Evolutiva e Inteligência Coletiva
• Palavras-chave em Inglês: Electricity Markets; Electrical Energy Trade; Electricity Auctions; Bidding Strategy; Risk Manager, Numerical Optimization; Intelligence Computation; Evolutionary Computation; Swarm Intelligence
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tedebc.ufma.br:8080/jspui/handle/tede/489

This work provides a framework to obtain the optimal bidding strategy for a GENCO in long-term electricity auction. The tool is based on intelligent techniques for optimizing the proposed Utility Function. The goal is to find the optimal strategy that maximizes the expected payoff of GENCO and simultaneously minimize the risks. The risks are modeled by two classical metrics: the Variance (Portfolio Theory) and Value at Risk (VaR). The proposed methodology is applied to auctions for long-term forward contracts, such that used in the Brazilian power system for buying and selling energy in the regulated market. The Bidding Strategy is formed through a Supply Curve which relates the optimal amount of energy to different offer prices. Thus, it allows the GENCO define the best bid (offer) for a given offer price. The proposed approach is validated for three test cases: First, concerning the variation of generation and price of energy scenarios for evaluation of the bidding strategy and the GENCOS risk perception; The second, consider a cascade hydro-term system for evaluation of MRE; and The third, considers the northeastern Brazilian subsystem where the supply curve is formed for the CHESF company's power plants portfolio. The results show how the offer may be changed according the variation of the spot prices and physical generation and demonstrate the efficacy of meta-heuristics proposed to optimize the supply model.