Scholarlink

<p>Offshore wind farms are major contributors to sustainable energy generation. However, their installation is highly weather-dependent, making the planning of costly resources, like jack-up vessels or port spaces, challenging. While existing models support strategic and tactical planning, there is a lack of effective decision support at the operational level.</p><p>To close this gap, this book presents an innovative online scheduling methodology based on a Model Predictive Control scheme. This approach combines Mixed-Integer scheduling models with control theory and a novel probabilistic method for integrating forecast uncertainty into operational planning. The resulting decision support system doesn't only enable reactive and weather-informed operational planning but also supports tactical and strategic decisions based on historical data. Simulation studies demonstrate significant potential: a reduction in variable costs of up to 9% and clear advantages over existing robust or control-based approaches in terms of planning reliability, cost efficiency, and responsiveness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Online Decision Support for Offshore Wind Farm Installations

  • 书籍

  • 发表日期：

    2026年

查看全文
  • Daniel Rippel

摘要

Offshore wind farms are major contributors to sustainable energy generation. However, their installation is highly weather-dependent, making the planning of costly resources, like jack-up vessels or port spaces, challenging. While existing models support strategic and tactical planning, there is a lack of effective decision support at the operational level.

To close this gap, this book presents an innovative online scheduling methodology based on a Model Predictive Control scheme. This approach combines Mixed-Integer scheduling models with control theory and a novel probabilistic method for integrating forecast uncertainty into operational planning. The resulting decision support system doesn't only enable reactive and weather-informed operational planning but also supports tactical and strategic decisions based on historical data. Simulation studies demonstrate significant potential: a reduction in variable costs of up to 9% and clear advantages over existing robust or control-based approaches in terms of planning reliability, cost efficiency, and responsiveness.