Periodic Reporting for period 3 - HIPERWIND (HIghly advanced Probabilistic design and Enhanced Reliability methods for high-value, cost-efficient offshore WIND)
Berichtszeitraum: 2023-06-01 bis 2024-09-30
Managing uncertainties is a key driver in reducing costs and improving the production, reliability, and thereby the value of offshore wind. Uncertainties translate into higher safety margins adding materials to components, shorter maintenance cycles, and increases in the cost of financing wind farms.
The HIPERWIND project aims at achieving a 9% reduction in the Levelized Cost of Energy of offshore wind farms, through advancements of basic wind energy science which will lead to reductions in risk and uncertainty. The outcome is cost efficient offshore wind through a reduction in unnecessary use of materials, less unscheduled maintenance, and optimized operating strategy tailored at delivering power with high market value.
The core challenge addressed in the project is the advancement of the entire modelling chain spanning basic atmospheric physics to advanced engineering design in order to lower uncertainty and risk for large offshore wind farms. The five specific objectives of the HIPERWIND project are to:
1) improve the accuracy and spatial resolution of met-ocean models;
2) develop novel load assessment methods tailored to the dynamics of large offshore fixed bottom and floating wind turbines;
3) develop an efficient reliability computation framework;
4) develop and validate the modelling framework for degradation of offshore wind turbine components due to loads and environment; and
5) prioritize concrete, quantified measures that result in LCOE reduction of at least 9% and market value improvement of 1% for offshore wind energy.
HIPERWIND employed multi-scale atmospheric flow and ocean modelling, creating a seamless connection between models of phenomena on mesoscale level and those on wind farm level, with the aim of reducing uncertainty in load predictions, and broadening the range of scenarios for which adequate load predictions are possible. Improved modelling of environmental conditions, improved load predictions, better reliability assessment and lower uncertainty, cost efficient design and operating strategies, and lower O&M costs are expected to yield a projected 9% decrease in the Levelized Cost of Energy (LCOE) and 1% increase in the market value of offshore wind by the conclusion of the project.
The project activities took place from December 2020 to September 2024, and concluded with full completion of the above scientific objectives. The methodologies and the tool for computing Levelized Cost of Energy (LCOE) developed as part of the project were applied on the Hiperwind use cases with a “before” and “after” calculation, in order to assess the impact of innovations developed in Hiperwind. The outcomes of these studies showed an overall LCOE reduction in the range of 5-10%, where the 10% is based on the calculations with present-day assumptions about interest rates and turbine size.
The outcomes of the project (outlined in the "Key Highlights" graphic) include advanced new scientific methods, software tools and models, which altogether led to significant cost reduction and improved approaches to offshore wind turbine design.
The primary conclusions from Hiperwind are:
- Practical examples of design under uncertainty were shown
- Along the way to delivering the design under uncertainty, a number of other useful scientific results were obtained, as well as model chain improvements and software tools
- We saw that understanding uncertainties lead to design improvements
- Integrated design approach is critical for the efficient design of offshore wind turbines
- A significant LCOE reduction was achieved - but besides the engineering, the cost of capital was a major external factor driving the LCOE.
The HIPERWIND project aims at achieving a 9% reduction in the Levelized Cost of Energy of offshore wind farms, through advancements of basic wind energy science which will lead to reductions in risk and uncertainty. The outcome is cost efficient offshore wind through a reduction in unnecessary use of materials, less unscheduled maintenance, and optimized operating strategy tailored at delivering power with high market value.
The core challenge addressed in the project is the advancement of the entire modelling chain spanning basic atmospheric physics to advanced engineering design in order to lower uncertainty and risk for large offshore wind farms. The five specific objectives of the HIPERWIND project are to:
1) improve the accuracy and spatial resolution of met-ocean models;
2) develop novel load assessment methods tailored to the dynamics of large offshore fixed bottom and floating wind turbines;
3) develop an efficient reliability computation framework;
4) develop and validate the modelling framework for degradation of offshore wind turbine components due to loads and environment; and
5) prioritize concrete, quantified measures that result in LCOE reduction of at least 9% and market value improvement of 1% for offshore wind energy.
HIPERWIND employed multi-scale atmospheric flow and ocean modelling, creating a seamless connection between models of phenomena on mesoscale level and those on wind farm level, with the aim of reducing uncertainty in load predictions, and broadening the range of scenarios for which adequate load predictions are possible. Improved modelling of environmental conditions, improved load predictions, better reliability assessment and lower uncertainty, cost efficient design and operating strategies, and lower O&M costs are expected to yield a projected 9% decrease in the Levelized Cost of Energy (LCOE) and 1% increase in the market value of offshore wind by the conclusion of the project.
The project activities took place from December 2020 to September 2024, and concluded with full completion of the above scientific objectives. The methodologies and the tool for computing Levelized Cost of Energy (LCOE) developed as part of the project were applied on the Hiperwind use cases with a “before” and “after” calculation, in order to assess the impact of innovations developed in Hiperwind. The outcomes of these studies showed an overall LCOE reduction in the range of 5-10%, where the 10% is based on the calculations with present-day assumptions about interest rates and turbine size.
The outcomes of the project (outlined in the "Key Highlights" graphic) include advanced new scientific methods, software tools and models, which altogether led to significant cost reduction and improved approaches to offshore wind turbine design.
The primary conclusions from Hiperwind are:
- Practical examples of design under uncertainty were shown
- Along the way to delivering the design under uncertainty, a number of other useful scientific results were obtained, as well as model chain improvements and software tools
- We saw that understanding uncertainties lead to design improvements
- Integrated design approach is critical for the efficient design of offshore wind turbines
- A significant LCOE reduction was achieved - but besides the engineering, the cost of capital was a major external factor driving the LCOE.
Reporting Period 3 was the final period in the project, and it led to the conclusion of all project activities. In RP3 the remaining open work packages - WP4, 5, 6 and 7 were concluded with all deliverables in place. The following technical deliverables were completed in RP3: D4.2 - D4.5 D5.3 D5.4 D6.3 D6.4.
Below is the complete list of technical deliverable:
D1.1 Supply of measurement data to the necessary parties from the appropriate platform
D1.2 Design brief for the use cases and models in engineering tools
D1.3 Baseline FLS and ULS simulation results from the use cases
D2.1 Atmospheric-wave multi-scale flow modelling that will resolve the flow fields from mesoscale to farm/turbine scale
D2.2 Realistic representation of nonlinear wave conditions applicable for offshore wind turbine design
D2.3 Environmental joint probability distributions and uncertainties
D3.1 Wind farm parameterization and turbulent wind box generation
D3.2 Turbine loading and wake model uncertainty
D3.3 Aero-servo-hydro-elastic model uncertainty
D4.1 Novel surrogate modelling approaches for wind turbine reliability assessment.
D4.2 Methods for efficient ULS reliability calculations and their impact on probabilistic design
D4.3 Methods for adaptive calculation of FLS loads and reliability, and their impact on probabilistic fatigue design
D4.4 Floating wind turbine structural design procedure including SLS
D4.5 Validation of the newly developed FLS and ULS distribution predictions and quantification of the resulting uncertainty reduction
D5.1 Offshore wind turbine drivetrain component degradation and lifing models
D5.2 Quantification of the impact of electrical events on drivetrain mechanical component degradation.
D5.3 Validation of component lifing and reliability models including grid events
D5.4 Development and implementation of probabilistic and uncertainty quantification methods for reliability sensitivity analysis
D6.1 Advanced O\&M model
D6.2 Quantification of the impacts of HIPERWIND on LCoE
D6.3 Quantification of the impacts of HIPERWIND on Market Value
All approved technical deliverables intended for public release have been made publicly available on the project website www.hiperwind.eu.
Below is the complete list of technical deliverable:
D1.1 Supply of measurement data to the necessary parties from the appropriate platform
D1.2 Design brief for the use cases and models in engineering tools
D1.3 Baseline FLS and ULS simulation results from the use cases
D2.1 Atmospheric-wave multi-scale flow modelling that will resolve the flow fields from mesoscale to farm/turbine scale
D2.2 Realistic representation of nonlinear wave conditions applicable for offshore wind turbine design
D2.3 Environmental joint probability distributions and uncertainties
D3.1 Wind farm parameterization and turbulent wind box generation
D3.2 Turbine loading and wake model uncertainty
D3.3 Aero-servo-hydro-elastic model uncertainty
D4.1 Novel surrogate modelling approaches for wind turbine reliability assessment.
D4.2 Methods for efficient ULS reliability calculations and their impact on probabilistic design
D4.3 Methods for adaptive calculation of FLS loads and reliability, and their impact on probabilistic fatigue design
D4.4 Floating wind turbine structural design procedure including SLS
D4.5 Validation of the newly developed FLS and ULS distribution predictions and quantification of the resulting uncertainty reduction
D5.1 Offshore wind turbine drivetrain component degradation and lifing models
D5.2 Quantification of the impact of electrical events on drivetrain mechanical component degradation.
D5.3 Validation of component lifing and reliability models including grid events
D5.4 Development and implementation of probabilistic and uncertainty quantification methods for reliability sensitivity analysis
D6.1 Advanced O\&M model
D6.2 Quantification of the impacts of HIPERWIND on LCoE
D6.3 Quantification of the impacts of HIPERWIND on Market Value
All approved technical deliverables intended for public release have been made publicly available on the project website www.hiperwind.eu.
The Hiperwind project has provided several types of exploitable results that have potential for directly impacting users. These are detailed in D7.9 (Exploitation Roadmap) and include:
- Aeroelastic code benchmarks;
- A range of software scripts and tools for enhanced, modular multi-scale CFD simulations;
- Methods to extract extreme transient wind events from wind time series and include the measured time series directly in wind turbine load simulations;
- An open-access tool for generation of synthetic turbulence fields
- Improved methods for load surrogate modeling that take wake effects into account;
- Improved model discrepancy assessment methods;
- A time series surrogate model capable of efficiently generating wind turbine load time series;
- A public wind turbine drive train description
- An integrated electrical-mechanical simulation framework;
- A modular, Python-based tool for calculation of Levelized Cost of Energy
The Hiperwind results have been disseminated in:
- 19 journal papers as of December 2024
- Over 35 conference presentations and posters
- Several webinars and collaborative workshops, and a final event
- More than 10 feature videos and interviews with project participants
- A LinkedIn page and project website featuring the project results and media
- Aeroelastic code benchmarks;
- A range of software scripts and tools for enhanced, modular multi-scale CFD simulations;
- Methods to extract extreme transient wind events from wind time series and include the measured time series directly in wind turbine load simulations;
- An open-access tool for generation of synthetic turbulence fields
- Improved methods for load surrogate modeling that take wake effects into account;
- Improved model discrepancy assessment methods;
- A time series surrogate model capable of efficiently generating wind turbine load time series;
- A public wind turbine drive train description
- An integrated electrical-mechanical simulation framework;
- A modular, Python-based tool for calculation of Levelized Cost of Energy
The Hiperwind results have been disseminated in:
- 19 journal papers as of December 2024
- Over 35 conference presentations and posters
- Several webinars and collaborative workshops, and a final event
- More than 10 feature videos and interviews with project participants
- A LinkedIn page and project website featuring the project results and media