Towards Data-Driven End-of-Life Management: Identifying Data Requirements and Availabilities in the Wind Energy Sector

Wind energy represents a key pillar of the global sustainable energy transition, yet its environmental benefits are compromised by inadequate End-of-Life (EoL) management of wind turbines. Current practices suffer from fragmented and inconsistent data availability, hindering effective EoL decision-making. Recent developments such as Digital Product Passports (DPPs) have the potential to close this gap by providing standardized and interoperable data across product life cycles. However, the specific data requirements needed to support effective EoL management in the wind industry remain insufficiently defined. This paper addresses this gap by systematically identifying and structuring relevant data needs for EoL decision-making in the wind sector, leveraging the methodological framework of Catena-X. The research employs a mixed-method approach, beginning with a systematic analysis of both general and domain-specific regulatory frameworks. From this regulation-based identification process of the required data points, additional prerequisites are derived, and the preliminary results are validated through expert interviews with key stakeholders from the industry. The resulting data points are clustered thematically and formalized into a UML-based class model to illustrate their structural relationships. The study’s main contribution lies in the identification of a data set derived from regulatory and industry-specific requirements for EoL decisions related to wind assets. This data set forms the foundation for a standardized data model enabling the development and implementation of DPPs within the wind sector. By establishing minimum requirements for cross-company data exchange, this work takes a first step towards aligning the final phase of the wind turbine’s operational life with circular economy principles, helping to ensure that wind energy fulfills its potential as a truly sustainable energy solution.