Reviewed by Lexie CornerDec 11 2024
New design simulation models developed by the HIPERWIND project can reduce the levelized cost of energy (LCOE) by up to 9 %, improving the reliability and cost-effectiveness of building and operating offshore wind turbines.
Breakdown of results. Image Credit: Nikolay Dimitrov.
Strong ocean currents and higher wind speeds compared to onshore wind turbines require more durable designs and significantly higher capital costs. While stronger winds lead to greater energy production, they also increase the LCOE due to these higher expenses.
The total installation capacity of wind energy reached 1TW this year, and by 2050, it is projected to grow to 10TW. A 9 % cost reduction would have a substantial impact on this scale.
HIPERWIND set out to achieve a significant reduction in the LCOE by understanding how to deal with uncertainties in the wind turbine design modeling chain.
Nikolay Dimitrov, Project Coordinator, Technical University of Denmark
Dimitrov added, “We examined how to quantify and identify various uncertainties, ranging from environmental conditions to loads and wind turbine reliability. With this information, we focused on reducing material use by better understanding model performance and reducing uncertainty. This approach helped minimize material use and lower energy costs. This methodology has demonstrated the feasibility of designing more efficient systems.”
HIPERWIND is built around the concept of uncertainty management. Uncertainties lead to bigger safety margins, more materials in components, shorter maintenance cycles, and higher financing costs for wind farms. Uncertainty management is thus a driver in lowering costs and risk, boosting output dependability and, eventually, the value of offshore wind.
Game Changer
HIPERWIND could be a game-changer. We delivered a significant reduction of the LCOE of up to 9% - and even 10% is achievable if we consider the most optimistic case we have. In the least optimistic case, the reduction will still be 5%.
Clément Jacquet, Senior Researcher, EPRI Europe
EPRI assessed the impact of HIPERWIND technology on LCOE, which required a comprehensive approach and a detailed analysis of offshore wind farm costs. This effort resulted in a new, customizable framework that EPRI will apply to future projects to enhance the economic efficiency of both onshore and offshore wind farms.
The Teesside offshore wind farm off the coast of England, owned by project partner EDF, served as the real-world case study for the project. Using wind farm-specific data and models, the team identified and quantified uncertainties related to turbine tower and foundation design. They then evaluated how the new insights could reduce the cost of rebuilding the wind farm.
HIPERWIND demonstrated that using less material in turbine construction can reduce upfront capital expenses, which make up about 30 % of the total cost of electricity. Further cost savings were achieved by scheduling maintenance during low-energy price periods, improving both cost efficiency and operational performance.
Exploitation
Using the measured data and advanced physics-based and data-driven models, a mindset focused on uncertainty control and reduction was applied throughout the offshore wind turbine design modeling process.
IFP Energies Nouvelles (IFPEN) is already leveraging HIPERWIND data to enhance chain modeling by precisely characterizing wind turbine fatigue loads.
The project has produced some significant reliability design procedures that are market-ready and, thereby, go beyond the research domain. Taking uncertainties into account, we obtain a reduction of 21% of the mass of the wind turbine structure, which is a lot.
Martin Guiton, Project Leader, IFP Energies Nouvelles
Similarly, ETH Zurich is applying these approaches to tackle not only wind-related challenges but also earthquake-related issues, such as the seismic fragility of buildings in complex environments and the design of high-rise buildings subjected to random wind excitation.
Senior Scientist Stefano Marelli, Chair of Risk, Safety, and Uncertainty Quantification at ETH Zürich, added, “The project required us to develop a new methodology from scratch to handle uncertainties in high-dimensional inputs and responses. Our work on surrogate modeling techniques, which accelerated algorithm development and enabled cross-partner collaboration, proved to be successful.”
Further Information
The HIPERWIND consortium had seven academic and industry partners: DTU Wind and Energy Systems, ETH Zürich, EDF, IFPEN, EPRI Europe, the University of Bergen, and DNV.
This 3.5-year project, spearheaded by DTU Wind and Energy Systems, was funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 101006689.