Innovative models slash offshore wind energy costs by 9%

Offshore wind generators face larger wind speeds than onshore generators and face sturdy ocean currents, requiring extra sturdy designs and considerably larger capital prices. Whereas they generate extra power because of stronger winds, these elevated prices lead to the next levelized price of power (LCOE).

The HIPERWIND mission has developed new design simulation fashions that scale back the LCOE by as much as 9%, thus making offshore wind turbine building and operation less expensive and dependable.

This yr, wind energy reached a cumulative set up capability of 1TW. The capability is anticipated to develop as much as 10TW by 2050. On this scale, lowering prices by 9% is monumental.

“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,” says Challenge Coordinator Nikolay Dimitrov from DTU Wind.

“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.”

On the core of HIPERWIND is managing uncertainties. Uncertainties translate into larger security margins, including supplies to parts, shorter upkeep cycles, and will increase in the price of financing wind farms. Uncertainty administration is consequently a driver in lowering prices and threat—thereby bettering the manufacturing reliability and, in the end, the worth of offshore wind.

Sport changer

“HIPERWIND could be a game changer,” Clément Jacquet from EPRI Europe says.

“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%.”

EPRI assessed the affect of HIPERWIND applied sciences on LCOE, requiring each a holistic method and an in depth evaluation of offshore wind farm prices. This work resulted in a brand new, adaptable framework that EPRI will use in future tasks to enhance the financial effectivity of each onshore and offshore wind farms.

The mission used a real-world case examine involving the Teesside offshore wind farm off the coast of England, owned by mission accomplice EDF. Knowledge and fashions particular to the wind farm have been used to determine and quantify turbine tower and basis design uncertainties. The group then assessed whether or not the improved information might scale back prices if the wind farm have been rebuilt.

HIPERWIND thereby demonstrated that utilizing much less materials in turbine building can scale back upfront prices (capital expenditure), which make up about 30% of the general price of power. Further price reductions have been achieved by scheduling upkeep throughout low power value intervals, boosting each price financial savings and operational effectivity.

Exploitation

Leveraging the measured knowledge and superior physics-based and data-driven fashions, this uncertainty administration and discount philosophy was utilized all through the offshore wind turbine design modeling chain—and past.

IFP Energies Nouvelles (IFPEN) can be already making use of HIPERWIND outcomes, bettering chain modeling by precisely quantifying wind turbine fatigue hundreds.

“The project has produced some significant reliability design procedures that are market-ready and thereby go beyond the research domain,” explains Martin Guiton from IFPEN. “Taking uncertainties into account, we obtain a reduction of 21% of the mass of the wind turbine structure, which is a lot.”

Likewise, ETH Zurich is now utilizing these methodologies not simply to unravel wind-related issues but in addition earthquake-related issues, such because the seismic fragility of buildings in complicated environments and the design of high-rise buildings beneath random wind excitation.

“The project required us to develop a new methodology from scratch to handle uncertainties in high-dimensional inputs and responses,” says Senior Scientist Stefano Marelli, Chair of Danger, Security and Uncertainty Quantification at ETH Zürich. “Our work on surrogate modeling techniques, which accelerated algorithm development and enabled cross-partner collaboration, proved to be successful.”