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.

Autorentext

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.