Evaluation of Loading Bay Restrictions for the Installation of Offshore Wind Farms Using a Combination of Mixed-Integer Linear Programming and Model Predictive Control

Abstract

Featured Application: This article demonstrates a combination of Mixed-Integer LinearProgramming with methods usually applied for short-term control, namely the Model Predictive Control scheme, to achieve decision support for the scheduling of installation activities for offshore wind farms. The general approach applies to several areas of application, where time-dependent uncertainties complicate mid-to long-term planning.Abstract: The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency.

Demand-Driven Supply of Offshore Wind Turbine Components by Cascading Simulation and Optimization

“…They can inquire the external framework which of these to choose for the next iteration. In general, the model contains various functions to estimate the duration of operations given a weather forecast and the operations' weather limits as proposed in the literature [17].…”

“…Afterward, it SNE 32(3) -9/2022 iterates through the database, selecting each year Y s in the database and a viable number of historical years Y N ∈ {0, 1, 2, 5, 10, 20} and calculates the mean value and hourly standard deviation. The framework again estimates the duration of installation operations using the Markov-Chain-based approach described in [17] using these values as input. Finally, it calculates the Pearson-Correlation Coefficient between these sets and the last three months to decide for a constellation that matches the current data as good as possible.…”