Supply Chain Simulation of Offshore Wind Farm Monopile Installation

Abstract

A monopile installation project consists of the whole process of loading monopiles to vessels, transporting monopiles to the wind farm and drilling monopiles in the seabed. Each installation project has different characteristics as the weather at the installation location, the distances to the suppliers and the vessels that are available differ between projects. Different project characteristics require different choices to be made when planning a typical installation project in order to optimally plan or reduce the expected cost of a project. One of these choices is determining which supply chain configuration will be used to get the monopiles from the supplier to the offshore wind farm. The choice of configuration is important as it determines the type of vessels that are required and if intermediate storage ports need to be rented, which requires initial investments.

The purpose of the research study is to assess the performance of different supply chain configurations. The performance of these different configurations is assessed on the key performance indicators project cost, project duration, utilization of the vessels and the waiting on weather duration. In order to assess the effect of using different supply chain configurations on the key performance indicators, 6 different experiments were performed within a developed simulation model.

Planners can use the qualitative insights of this report as a starting point for the choice of the preferred supply chain configuration when planning an offshore wind farm installation project. This will enable planners to make educated choices in the planning phase and give these planners insight into the different factors that are important in the supply chain of offshore wind farm installation projects. From a theoretical point of view, this report contributes to the sparse literature on the supply chain and logistical decisions in the field of offshore wind farm installation. Next to these insights, the weather module used to generate synthetic weather data improves Markov models discussed in literature and provides a solution to the problem of persistence that is present in conventional Markov models.