A framework for life-cycle profit-damage assessment of floating multi-energy islands with wind–wave dynamics integration
Year: 2026
Type: article
Abstract: This study proposes a comprehensive techno-economic assessment framework for optimal offshore energy island site selection, emphasizing both economic efficiency and structural resilience. By integrating the dynamic interaction of wind and wave conditions, the framework improves decision-making accuracy to ensure that selected locations are both cost-effective and environmentally robust. Additionally, a novel multi-energy island scenario is introduced, featuring detailed hydrogen production and storage modeling to enable efficient electricity-to-hydrogen conversion and flexible energy transport. To evaluate operational performance and life cycle sustainability, the study employs Kriging surrogate models, validated using OpenFAST simulations. These models predict turbine power output and fatigue damage across varying environmental conditions, including wind speed, wave height, and wave period. This integration significantly advances the optimization process by allowing long-term behavior prediction while minimizing computational costs. Furthermore, real historical environmental datasets and multiple floating offshore wind platforms are utilized for simulation benchmarks, enhancing the framework’s practical relevance. The results demonstrate substantial improvements in life cycle profit and structural reliability through optimal site selection. Ultimately, the framework provides a practical and scalable reference for planning offshore renewable energy systems, supporting the development of sustainable, resilient, and economically viable offshore energy islands in dynamic marine environments. • A novel techno-economic framework is developed for offshore energy island siting. • Wind-wave conditions are integrated to assess both economic and structural viability. • Power-to-hydrogen conversion and storage strategies are modeled and evaluated. • Kriging surrogate models validated by OpenFAST predict fatigue and power efficiently. • Real historical data from North Sea platforms supports lifecycle performance analysis.
Source: Renewable Energy
Authors: Miaorui Ma, Chengwei Lou, Lingte Chen, Jin Yang
Institutions: University of Glasgow, China Agricultural University
Language: English
Topic: Wave and Wind Energy Systems
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,460
OpenAlex ID: W7128497229