Framework para a otimização do planejamento de operações e manutenções de parques eólicos offshore

• Ano de defesa: 2024
• Autor(a) principal: Cabral, Eric Lucas dos Santos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio Grande do Norte; Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA E ENGENHARIA DE PETRÓLEO
• Palavras-chave em Português: Parques Eólicos Offshore; Energia Eólica Offshore; Planejamento de parques eólicos; O&M; Inteligência Artificial; CNPQ::ENGENHARIAS::ENGENHARIA QUIMICA::TECNOLOGIA QUIMICA::PETROLEO E PETROQUIMICA
• Link de acesso: https://repositorio.ufrn.br/handle/123456789/60484

The development of offshore wind farms encounters substantial challenges in their operations and maintenances (O&M), including a pronounced reliance on adequate port infrastructure, regional regulatory frameworks, and technical requisites, as well as the maintenance of wind turbines, which can significantly impede the sustained growth of this sector due to the burdensome industrial costs involved. Despite the imposition of these challenges, the notable expansion of the offshore wind sector finds primary support from European and emerging nations as a strategic measure to uphold competitiveness in the production of durable goods and commodities. The aim of this study is to construct a model to optimize the planning of O&M for offshore wind farms. The research procedure comprised three stages: stage one – definition of the research problem; stage two – systematic literature review procedures; stage three – framework, model validation through focus groups, and presentation of findings. The model encompassed 21 factors across three dimensions: Strategic O&M Planning, Innovation and Technical Analysis, Operational Efficiency, and Safety. Building upon these considerations, the study advocates for the exploration of the feasibility of integrating hydrogen technology with offshore wind energy and the proposal of specific quantitative methodologies to address conflicting choices for O&M optimization. These approaches endeavor to propel the global energy transition by furnishing solutions that mitigate risks and uncertainties in routine decision-making processes.