High resolution wind farm simulations with CFD for Wind Resource Assessment and Forecasting

Vindressursanalytikere forsøker å redusere usikkerhetene i deres vindressursvurderinger. For å gjøre det bruker de avanserte numeriske strømningsberegninger (Computational Fluid Dynamics, CFD) som per dag bruker kunstige grensebetingelser. For å redusere usikkerheten ytterligere og å modellere turbulensen og vake effekter i en vindpark på best mulig måte, utvikles en statistisk-dynamisk nedskalering av numeriske værvarslingsmodeller. Disse numeriske værvarslingsmodellene kjøres vanligvis på et horisontalt oppløsning på flere kilometer, som er for grovt til å beskrive det detaljerte vindfeltet i en vindpark. Ved nedskalering av disse vindfeltene med en CFD-modell, kan vi oppnå en horisontal oppløsning på rundt 10 meter som gjør det mulig å ha flere gridpunkter over rotorområdet til en vindturbin. For å kunne gjøre realistiske vindprognoser rimelig raskt har man utviklet en statistisk tilnærming som gjør det mulig å simulere et fåtall værmønstre per år og å utvikle et årlig energiproduksjonsestimat basert på disse. I dette arbeidet vil egnede metoder for direkte dynamisk nedskalering bli utviklet og implementert i WindSim-programvaren. Nye metoder som mesoskala modellering og maskinlæring vil bli brukt og evaluert. Det endelige målet er å få en forbedret prognose for vindhastighet og turbulens i en vindpark.

Two novel meso-microscale coupling methodologies have been developed. The first approach consists in utilizing the average values of the mesoscale fields by wind directional sector. The second instead, extracts weather patterns by utilizing a fully automated clustering methodology. Both methodologies were validated with data obtained for commercial projects. Advantages and limitations depending on the site conditions were studied. Ways to tackle the current limitations of the methodologies were identified. This development allows WindSim to modernize its current meso-microscale coupling code. Furthermore, it enables potentially new services for customers that require to downscale mesoscale datasets. Currently the developed methods are being adapted for their application in other areas of wind energy like wind energy forecasting, wind turbine wake modelling and dynamic line rating.

Wind energy is a volatile energy source which is not always available when it is needed. Therefore, it is very important to have an accurate wind power forecast such that other energy sources can be used to balance the energy feed into the electrical grid. WindSim AS has developed forecasting systems based on Artificial Neural Networks (ANN) and based on Computational Fluid Dynamics (CFD). The ANN solutions are very efficient when the focus is only on predicting the wind speed at the turbine positions. But the power output of a wind farm depends also on the turbulence inside the wind farm and the wake effects between the turbines. To model the turbulence and wake effects inside a wind farm the best physical description is a dynamical downscaling of numerical weather prediction models. Those models are typically run on a horizontal grid of several kilometers which is too coarse to describe the detailed flow field inside a wind farm. Downscaling those predictions with a CFD model we can obtain horizontal resolutions of around 10 meters. To be able to make meaningful predictions the downscaling should be run as so called transient simulations which makes it possible to calculate the time dependent behavior of the wind field and thereby the turbulence. The purpose of this project is to establish an efficient meso-microscale coupling, answering the following questions: - Can the TKE profiles from the mesoscale be used or is there a better formulation how to calculate the TKE inlet profiles? - Can the turbulence prediction be improved by using more advanced turbulence models and can the wake prediction be improved by better models? - Is Large?Eddy Simulation (LES) superior to Reynolds averaged Navier-Stokes (RANS) simulation or can transient RANS simulation work well for industrial application? - What is the economic benefit of a meso-microscale coupling with regard to a better wind power forecast compared to the ANN methods when we consider the imbalance costs?