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.

The Author
Daniel Rippel is a research associate at BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen. He holds a Diploma degree in Computer Sciences from the University of Bremen, Germany. His research interests include modeling and simulation of logistic systems, the development of domain-specific modeling methods, as well as the application of prediction techniques from statistics and machine learning.

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.