Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from [Formula: see text] of the global energy mix in 2018 to [Formula: see text] by 2050 to keep global temperatures from rising 1.5°C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to [Formula: see text] for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant ([Formula: see text]) power increases of 7-[Formula: see text] for wind speeds near the site average and wind directions which occur during less than [Formula: see text] of nocturnal operation and 28-[Formula: see text] for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to [Formula: see text] Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.

https://www.pnas.org/content/pnas/116/29/14495.full.pdf

Wind farm power optimization through wake steering.

A quantitative review of wind farm control with the objective of wind farm power maximization

The power generated by a wind turbine largely depends on the wind speed. During time periods with identical hub-height wind speeds but different shapes to the wind profile, a turbine will produce different amounts of power. This variability may be induced by atmospheric stability, which affects profiles of mean wind speed, direction and turbulence across the rotor disk. Our letter examines turbine power generation data, segregated by atmospheric stability, in order to investigate power performance dependences at a West Coast North American wind farm. The dependence of power on stability is clear, regardless of whether time periods are segregated by three-dimensional turbulence, turbulence intensity or wind shear. The power generated at a given wind speed is higher under stable conditions and lower under strongly convective conditions: average power output differences approach 15%. Wind energy resource assessment and day ahead power forecasting could benefit from increased accuracy if atmospheric stability impacts were measured and appropriately incorporated in power forecasts, e.g., through the generation of power curves based on a range of turbulence regimes.

/pdf/atmospheric-stability-affects-wind-turbine-power-collection-5g4jrjnn30.pdf

Atmospheric Stability Affects Wind Turbine Power Collection

https://repository.tudelft.nl/islandora/object/uuid%3A09a5185c-4d86-404f-92b4-9e365ce3cf32/datastream/OBJ/download

Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions

Abstract. Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).


/pdf/wind-farm-flow-control-prospects-and-challenges-1ewim7ay.pdf

Wind farm flow control: prospects and challenges

. Wake steering is a form of wind farm control in which turbines use
yaw offsets to affect wakes in order to yield an increase in total energy
production. In this first phase of a study of wake steering at a commercial
wind farm, two turbines implement a schedule of offsets. Results exploring
the observed performance of wake steering are presented and some
first lessons learned. For two closely spaced turbines, an approximate
14 % increase in energy was measured on the downstream turbine over a
10 ∘ sector, with a 4 % increase in energy production of the
combined upstream–downstream turbine pair. Finally, the influence of
atmospheric stability over the results is explored.

/pdf/initial-results-from-a-field-campaign-of-wake-steering-4d16ho6w2o.pdf

Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1

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Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1