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Increasing certainty in offshore wind energy

Name Affiliation Phone Number Email Address
David Standingford Zenotech Ltd +447870628916 david.standingford@zenotech.com
Industrial Sectors:

Renewable energy


Horns Rev Wind Turbine ArrayWind turbine array wake analysis leads to uncertainty reduction & better wind farm layouts & control strategies.This (i) reduces costs, thus enabling a displacement of fossil fuels thereby (ii) cutting carbon emissions & (iii) reducing dependence on insecure imports.Offshore wind farm design based on improved wake modelling to reduce wake losses will result in at least 0.2% reduction in the Levelised Cost of Energy (DECC).Uncertainty in the pre-construction energy predictions for offshore wind farms can be reduced with accurate CFD-based wake analysis.

2.1 Process Inputs

ZCFD Numerical Simulation of Turbine Array The use of numerical simulation techniques for a-priori design of wind turbine array layout and control strategies is highly desirable – if the simulation results are accurate. Underlying models for the use of CFD in aerospace, automotive and civil engineering have been well validated.  This is not the case for wind energy. Turbulence parameters for turbine wakes have been lifted directly from standard aerospace-scale models without great consideration of the tuning that has been applied  - or in some cases the formal bounds of applicability. The industry is now inserting LIDAR and other direct measurement systems into large wind farm arrays to provide in-situ and in-service data feeds, but integration with design simulation models is virtually non existent. The performance of the overall array is assessed in terms of the Levelised Cost of Energy – a formal framework from DECC. LCoE includes operational, capital and risk of the array over its life. This provides a solid model for assessing the impact of uncertainty reduction. The model explicitly includes the cost of financing as a function of output power uncertainty over the lifetime of the turbine array. The physical parameters to be included as input to the simulaiton proces include location, layout, type and control laws for the turbines.  More detail is included in the location & wind / sea characteristics; turbine model & definition – blade / section types, power curve & control laws; layout; simulation parameters (turbulence model, boundary conditions – atmospheric profiles). These parameters are all subject to uncertainty on input. The qualitative characteristic definition of many of these input uncertainties is not mature.

2.2 Propagation

The cost of energy to the consumer is directly related to the uncertainty in the array performance model, as uncertainty drives the cost of bank financing to develop the array.  The levels of uncertainty are referred to as the P50 / P90 ratio (see https://financere.nrel.gov/finance/content/p50-p90-exceedance-probabilities-demystified) Inverse mapping will drive the business case for investment in the reduction in overall uncertainty by further developing (say) the understanding of flow physics, structural performance, control law strategy. A combination of raw analysis and surrogate modelling will likely be required.  The cost of a given simualtion may rise to 1 billion cells for detailed wake analysis or circa 20 million with advanced techniques. 200 million cells = 1 hour (£100) on a modern GPU-based hardware.  72 wind directions @ 10 speeds.  Add robustness, layout, control, turbine characteristics.The design space is highly nonlinear.  Singularities and / or folds are un-characterised.  We expect that automated DOE plus Kriging with expected improvement-based refinement would be effective.Emergent functional interdependence likely when the updating framework is used: turbine blade performance will link to wake evolution;  wake evolution drives onset turbulence downstream turbine blades.

2.3 Interpretation and Communication of Results

The key decision that flows from UQ&M for turbine arrays is whether or not to invest in them.  The impact is felt across the renewables sector and is a key driver in the cost of energy for consumers. Clarity and aesthetics are desirable and systems that are easy-to-use tend to get used more!  Modern UX design and end-user engagement in solution development will be key to adoption and commercialisation.


Such a system is not currently in use.  The use case is the central driver for a recently approved project via Innovate UK: "Simulated Wake Effects Platform for Turbines (SWEPT2)".