Optimization of offshore wind farm installation procedure with a targeted finish date

Abstract

Offshore Wind Farm (OWF) installation procedure is a complicated phase requiring excellent management of resources for timely completion of tasks. The wind farm installation period involves crucial stages like streamlining onshore logistics, transportation of components on vessels, installation of foundations, wind turbines, and cable laying, etc. With wind turbine size growing and OWFs moving into deeper waters, the complexity of the installation procedure is also intensifying correspondingly. Moreover, the installation process can experience uncertainty due to harsh weather conditions, possible equipment failures and component delivery delays during the build. Additionally, the resources used in the installation phase are needed for the subsequent projects and have limited flexibility with end dates. This finally results in a considerable ambiguity in end date and cost incurred during the project.
This graduation study looks at optimization of OWF installation procedure with a targeted completion date as a priority. In this thesis, an optimization approach is built around an ECN in-house software, developed for simulating various OWF installation strategies. Ultimately, the result of the dissertation is to have a method that provides added flexibility to simulate different OWF installation planning while still obtaining optimal installation costs. A concise literature review describes the significance of the current research and the potential that metaheuristic approaches bring to solve installation scheduling problems. Thus, the genetic algorithm is chosen as the optimization procedure to use for current work. The objective of the optimization procedure throughout the research is minimizing the total installation cost. The target end date in this study is implemented in the form of a constraint to steer the optimizer solution within the specified limit. A new methodology is proposed to generate an automated planning for the different installation procedures to facilitate the link between the optimizer and ECN tool. The project also considers uncertainty introduced due to weather and describes the considerations made to account for the same. The new approach shows the potential of introducing an optimization procedure in OWF installation logistics and ultimately assisting in lowering the overall project costs.