Development of low-noise and cost-effective wind farm control technology

The project objectives are (1) to develop high-fidelity noise-chain models that can accurately predict noise from source to receiver through atmosphere and (2) to increase AEP or decrease LCOE by 5% by optimally operating wind farms within existing noise constraints.
The aim of DecoWind is to address a fundamental gap in our knowledge about noise from WTs and to use the acquired insight to increase Annual Energy Production (AEP) by 5% without increasing noise annoyance at receiver. Achieving this will reduce a main barrier to public acceptance of WTs and increase onshore deployment of wind energy. This aim is realized by (1) developing high-fidelity noise-chain models that can accurately predict atmospheric noise propagation from source to receiver; (2) integrating the developed noise-chain models into EMD’s WindPRO as well as Siemens Gamesa Renewable Energy (SGRE)’s siting tool; (3) updating recommendations for noise regulation; (4) developing WFOS for optimal AEP operations of WTs in WF and demonstration, and (5) commercialization. The time-to-market can be estimated as: the high-fidelity noise-chain prediction tools commercialized in a new release of the WindPRO software at the project completion; the new AEP optimized WFOS for SGRE to implement in an existing or new WF with SGRE WTs at the project completion. Based on the outcome of this pilot study, the product will be rolled out to more WFs in the SGRE fleet. The project will generate solid scientific evidence about noise propagation from WFs at greater distances and lower frequencies, more precise and field-tested noise-chain models from source to receiver, and new active WFOS using real time modelling based on current wind and weather conditions, all contributing to the goal of increasing AEP or lowering Levelized Cost Of Energy (LCOE) by 5% within existing noise constraints. The 5% AEP is ambitious but realistic, and is based on a Siemens-internal feasibility study using historical operational data for real WFs. Recommendations for updated WT noise regulation will be made based on (1) measured data in WP2, (2) existing data, (3) WT noise regulation in other countries, (4) socio-acoustic study, and (5) computed data using the advanced noise-chain models.