JavaScript is Disabled
Your browser's JavaScript functionality is disabled. It has to be enabled to use this function of ConfTool.
Here you can find information on how to enable JavaScript
If you have any problems, please contact the organizers at hicl@tuhh.de.

Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Daily Overview

Session

T-C-04: Logistics Management & Operations 5: Waterway and Freight Corridor Analytics
Location: A-0.19

Presentation 1

Use of AIS Data to improve Operation and Maintenance Processes for Offshore Wind Farms

Jürgen Weigell¹, Carlos Jahn²

1: Harz University of Applied Sciences, Germany; 2: Hamburg University of Technology, Germany

Purpose:

Offshore wind energy is a key component in achieving the decarbonization goals set by the German government. These goals are 40 GW of installed offshore wind energy capacity by 2035 and 70 GW by 2045 in German waters. In order to reach these ambitious goals, the levelized cost of energy must be reduced. Logistics and thus the associated costs represent a major challenge especially during the O&M phase due to the distances and harsh wind and wave conditions.

The Operation & Maintenance (O&M) phase accounts for about 25% of the levelized cost of energy and is one of the levers to reduce the costs of offshore wind. Standard O&M agreements are set up for an initial period of 5 to 10 years during which the original equipment manufacturer (OEM) is responsible for O&M. After this period the owner of an offshore wind farm has to decide whether to extend this contract, do the O&M itself or contract another company to do the O&M.

For external third-party providers of O&M it is thus difficult to decide what prices they should offer for the new contract since the real O&M processes are only known to the OEM. A method to mitigate this disadvantage by analyzing historical AIS (Automatic Identification System) data will be proposed in this paper.

Methodology:

In this paper a method was developed to analyze historical AIS data and weather data to identify O&M processes of an offshore wind farm including the movements of the vessels and the duration of the visits to each turbine using Python. AIS data transmits every vessel’s position, speed, and course over ground at certain intervals based on said speed, which can allow the routes of O&M vessels to be traced.

Findings:

The findings are that this method can be used to analyze the O&M processes ex post and thus can give external entities valuable insights into the inner workings of O&M processes in offshore wind farms. It is possible to see which vessels were used and for how long they were at the wind turbines and thus determine the O&M processes.

Originality:

The use of AIS data is a new field in offshore wind logistics and has mainly been used to identify collision risks in the past but not to identify O&M processes and thus provide a basis for improving them. The paper is original because it analyzes the AIS data and uses it as a basis for future business decisions.