Quantifying the benefits of wind farm control measures
Wind farms witness ‘wake effects’ (aerodynamic interactions), ‘terrain effects’ (the influence of physical surroundings), ‘blockage effects’ (flow deceleration due to the wind farm itself), and wind dynamic factors. Wind farm control (WFC) measures help mitigate these impacts by coordinating turbine operations; lessening power in some turbines, to gain more in others, for example. The EU-supported FarmConners(opens in new window) project hosted by the Technical University of Denmark(opens in new window), investigated the success of some of these measures, in terms of power and revenue generated. To accelerate adoption of the technology the team used modelling and fieldwork from complementary projects, to assess the opportunities and challenges, while identifying research and implementation gaps. This resulted in two validation platforms which benchmarked and showcased WFC’s potential. “We brought state-of-the-art techniques, cutting-edge projects and industrial specialists together to increase confidence in WFC models and strategies,” says project coordinator Gregor Giebel. Most of the data collected are now open access and the results of the platforms are available as Wind Energy Science open access articles(opens in new window). Additionally, most project activities and results have been included in the International Energy Agency Wind Task 44 wiki on Farm Flow Control(opens in new window).
Validation platforms
As well as mitigating effects of environmental interactions, WFC techniques such as wind power plant control can also improve services. These technologies promise an increase in power production and a decrease in structural loads. They also better integrate wind power into the electricity grid, reducing costs, while increasing market share. This benefits revenue management, especially important for the forthcoming flexible electricity markets(opens in new window). FarmConners built a benchmark(opens in new window) platform from publicly available simulations, measurements from wind tunnel experiments and field data from the Sole du Moulin-Vieux onshore wind farm(opens in new window) in France, to demonstrate WFC benefits, such as increased production.
Benchmarking yields wide range of data
“With 31 authors contributing to four blind tests(opens in new window), our benchmark is the first of its kind to bring a wide variety of data sets, control settings and modelling to assess wind farm flow control benefits,” explains scientific lead Tuhfe Göçmen. The team also developed a series of showcases(opens in new window) for various electricity market scenarios, outlining the potential added revenue generated by WFC technology. These were based on data from the offshore TotalControl Reference wind power plant(opens in new window), weather simulation data and estimated electricity prices for 2020 and 2030. “With nine authors, the benefits of flexible, market-driven control were assessed, and the potential for increasing income, instead of power, was demonstrated,” says Göçmen. The team also wrote a position paper(opens in new window) guided by the project results, which makes recommendations for the standardisation and certification of WFC. “Many of our project outcomes are either already used as best practice in the industry, such as our common taxonomy, or actually set standards for the industry and certification bodies,” adds Göçmen.
Making wind power ‘market fit’
A major barrier to wider integration of renewable energy sources is their variability. While regulation partially addresses this, wind farms need more market flexibility, especially within zero-subsidy schemes(opens in new window). WFCs offer wind power plant owners more control, allowing them to adapt to fluctuating market prices. “Scenarios could include boosting power, strategic power reduction with distributed set-points mitigating loads or simply longer participation in the market, thanks to extended lifetime,” concludes Giebel. During the project, FarmConners’ work was integrated into initiatives by project collaborators including Siemens Gamesa Wake Adapt(opens in new window) to help control wind flow, Vestas and Microsoft Azure for reinforcement machine learning(opens in new window) and WindESCo for Swarm(opens in new window), for cooperative turbine position adjustment.
