Digitising the wind key to decarbonisation goals

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Lidar led digital visualisation of the wind will help us meet ambitious targets for on and offshore wind, according to Peter Clive, Principal Wind Energy Consultant, Black & Veatch – Europe.

Peter Clive

Much has been written about the role of digitalisation in the delivery of the wind power capacity required to decarbonise our economies. In order to implement this fully we need to digitise the wind. This is being achieved by moving from met masts to lidar as the primary source of the wind data used in assessments on which the planning and operation of wind farms are based.

Data requirements and use cases associated with optimised performance of wind turbines and wind farm arrays have outgrown the capabilities of met masts. Wind is a time-varying three-dimensional vector field. We can no longer rely on simplifying this as a “wind speed” of the sort acquired by met masts when understanding and characterising complex interactions between our wind assets and the atmosphere.

Lidar acquires a richer dataset by analysing laser emissions backscattered by airborne particles advected by the wind. Lidar allows us to learn about much more than wind speed and direction in a single location where a met mast has been installed.

Managing complexity

With lidar we gain detailed insights into phenomena such as wakes and complex shear which can have a significant effect on the bottom line of a wind project throughout the asset’s lifecycle, from demonstrating project bankability pre-construction right through to optimised operations and maintenance. As wind turbines and arrays get bigger, the impact of these phenomena are becoming more important.

Here are some practical illustrations. Engineering approximations of wind conditions tend to assume that wind gradually increases with height. In the North Sea complex, intermittent – and crucially-  unanticipated wind shear phenomena associated with variations in atmospheric stability have been directly observed using lidar.

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These phenomena impose mechanical loads on the turbine blades that propagate through the rotor nacelle assembly and drive train. Thanks to lidar data we now understand some of the loads that had been observed on offshore wind farms which were previously unexplained.

Lidar has also helped reveal the importance of atmospheric stability on wake propagation. Wake losses downwind of a wind turbine have been seen to increase at night, compared to day-time operations, because more stable night-time atmospheres meant that wakes propagated further.

Reducing uncertainty

So across the board, from investors, developers, and turbine manufacturers, to owners and operators, the benefits of supporting the digital representation of wind with lidar lie in grappling with the inherent complexity of the problems we are trying to solve as we seek to develop profitable wind projects and operate those assets in the most cost-efficient manner. By helping manage complexity, digitising the wind reduces uncertainty and increases confidence in wind projects. Lidar methods help limit the scope for circumstances to arise that would be unforeseeable if we relied only on met masts. Lidars achieve this by enabling assessments that would be inconceivable if we limited ourselves to met mast functionality.

You need to use lidar to do more than emulate the capabilities of a met mast if the true benefits of digitalisation are to be achieved. Lidar allows you to map the vector field that represents wind conditions with a level of detail and degree of precision that allows you to test the fidelity of the most sophisticated wind simulations. To fully see the wind as a digital object that is compatible with other digital objects in your workflow requires data acquired by lidar to be combined with, for example, computational wind models. These can then be coupled to aeroelastic models, which themselves provide input to engineering models that represent the turbines, to generate predictions grounded in wind measurement. Uncertainty models allow us to propagate measurement uncertainties associated with the data through to the predictions. Lidar lets us close the loop.

Lidar data can also be combined with mid-fidelity wake models for validation, and to support wind farm control methods. With developments like these we are taking the steps necessary to move away from using lidar as a met mast surrogate and thinking not in terms of ‘what measurements am I limited to?’ but, ‘what do I need to measure to remove as much uncertainty as possible from my wind project?’

Uncertainty is removed because data-driven analysis replaces assumption. Using a met mast, or lidar as simply a surrogate met mast, leaves more and bigger gaps in the information upon which a project is based which have to be filled with assumptions, which introduces uncertainty. Lidar helps us fill these gaps and reduce the possibility of unpleasant surprises later in the project lifecycle when adverse wind conditions that could otherwise have been predicted and mitigated with a properly designed and executed lidar measurement campaign prior to construction are only discovered through their unforeseen consequences in terms of component failure and unscheduled downtime.

Total lifecycle benefits

Applying high-fidelity lidar data, combined with the types of modelling discussed previously, reduces project uncertainty, which has benefits throughout the project lifecycle. Turbine design, operation and maintenance can be informed by richer data sets that allow us to describe the operational conditions more effectively, which can enhance the quality of project specific performance forecasts and allow the development of more predictable operations and maintenance costs.

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Ultimately this all feeds into greater confidence in the quality of levelised cost of energy analysis which enhances bankability for developers, and gives owners and operators greater confidence when evaluating energy production – and ultimately – profitability.

This approach offers an alternative to managing the unplanned consequences of wind conditions that – although the ability to model and predict them is available – are not accounted for due to gaps in wind assessments that do not fully exploit the capabilities of lidar and the integration of the data it acquires into the digital workflow. Component or structural failures that could have been proactively mitigated during design or construction become instead the subject of reactive remedial work, which is rarely the most cost-effective approach to operations and maintenance.

This issue is especially pertinent to offshore assets, both fixed and floating, where inspection, repair and maintenance represent a significant programme cost and consideration. Unless site-specific wind data has been incorporated into a project’s early development, it is possible that the built assets may not be able to consistently achieve the performance forecast – because the ability of local conditions to hamper O&M activities has not been accounted for in sufficient detail. In the preconstruction phase floating lidar offers an effective way to gather detailed site-specific wind data.

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