Wind Due Diligence Services
Wind Due Diligence Services
Vaisala's due diligence services deliver accurate energy assessments to support profitable project investments in the wind sector.
Vaisala's Wind Due Diligence Services take into account all of the factors that affect production at a proposed location to fully understand the resource risk associated with a project over its lifetime. Using a combination of advanced NWP modeling and machine learning techniques, we examine the long-term wind resource, site terrain, on-site data, and turbine layout. The final report includes net production estimates both you and your financing institution can trust.
This analysis takes a deep look at a project location to better complete pre-construction due diligence, including a site visit and a comprehensive survey of the project location. Vaisala customizes each report to our clients' requirements, but typically creates a 40-year dataset of hourly values for each turbine in the project layout using our advanced NWP modeling platform. The client's observational data are quality-controlled by Vaisala and then used to statistically correct the model data to reduce bias and error.
Wake losses are calculated using our time-varying wake model and integrated into the analysis along with all other site-specific loss factors that affect project performance. The final report includes a full uncertainty analysis of the wind resource assessment and provides net probability of exceedance values.
The analysis is delivered as a Portable Document File (PDF) report with data files in an easily integrated, comma-separated value (CSV) format.
Our Wind Due Diligence Reports are produced by Vaisala 3TIER Services using its NWP modeling platform, which combines on-site observations with mesoscale and microscale weather simulation models. We primarily employ an NWP model called WRF (Weather Research and Forecasting). This model is widely supported and continually enhanced by the global atmospheric science and research community. Our in-house developed microscale model, TVM, allows us to calculate the effects of small-scale terrain, roughness, and blocking effects at a very fine resolution without the high computing costs of WRF. The output from this modeling system is a four-dimensional dataset of modeled historical weather for each met tower and wind turbine location.
Model output is statistically calibrated using quality-controlled observations from the site met towers in a process known as MOS (Model Output Statistics) correction. This technique is applied after conducting a detailed quality control analysis of the met tower observations, which incorporates information gathered during the project site visit. MOS then uses multi-linear regression equations to remove bias and adjust the variance of the raw model output. The MOS equation for each met tower is trained during the observational period of record and then applied over the entire time period of the NWP derived dataset.
MOS-corrected long-term wind speed data and the manufacturer specified power curve are used to compute long-term time series of power at each turbine location. Site-specific loss factors are then applied to derive the net power values. Finally, net probability of exceedance values are computed based on the results of the site-specific uncertainty analysis.
In order to perform the analysis, the client must provide all available met tower observations and the boundaries of the project area. If a turbine layout has been defined, then the specifications of the layout are required as well.