SDR Deposit of the Week: Optimizing wind farms
Every year, more and more Stanford researchers use the Stanford Digital Repository (SDR) to share the work they have done in a way that goes beyond just publishing a paper -- they provide direct access to the actual data files so that others may also benefit from their efforts. Graduate student Michael Howland is one such forward-thinking Cardinal who recently deposited the data associated with his article "Wind farm power optimization through wake steering," out today in the Proceedings of the National Academy of Sciences.
Michael found the SDR through a Google search. He wanted to share these data because "open-access real wind farm SCADA [Supervisory control and data acquisition] data is uncommon and has the potential to aid in the development of state-of-the-art models and optimization tools." In addition, Michael said that "these data represent the first field experiment of wake steering with multiple wind turbines at a utility-scale wind farm." We are very excited to be hosting these important data in the Stanford Digital Repository!
Michael took a few minutes to tell us a bit more about his research and the challenges they face in this field.
Briefly describe the work represented by the deposit and why it is important.
The work represented by the deposit is a study of wind farm power optimization through wake steering. In utility-scale wind farms, aerodynamic wakes trailing wind turbines reduce the wind speed downwind. When wind directions are aligned with columns of wind turbines, these wakes result in a significant reduction in power production and therefore an efficiency degradation in the wind farm. In this study, we utilized a novel wake steering control scheme to operate wind turbines in a fashion to reduce these aerodynamic wake losses. The data represented in the deposit are Supervisory control and data acquisition (SCADA) data from an operational wind farm in Alberta, Canada for the historical baseline data and the wake steering experimental data. The wake steering resulted in an increase in power production and a decrease in the variability of the power with respect to the baseline operation.
What got you interested in wind farms?
In order to prevent globally averaged temperatures from rising 1.5 degrees Celsius above pre-industrial levels, renewable energy production must increase from contemporary rates of 20% to 67% by 2040. In order to achieve these large increases in low-carbon power production, wind farms must improve their efficiency and reduce their cost of electricity. As the cheapest form of electricity on the market, wind energy has the potential to transform the global energy picture. The continued research and development of this resource in fields such as fluid mechanics, optimization, machine learning, and economics will allow for this crucial energy transition.
What are some of the challenges that you continue to face in your research?
The major challenges faced in wind energy research continues to be the difficulty of field experiments with utility-scale wind turbines. Research of wind energy using computational models and wind tunnel experiments has reached a mature state with impressive optimization, control, and design capabilities. However, it has been a challenge to prove these research concepts in the field due to the high cost and potential risk of field experiment campaigns. Such challenges can be overcome by the continued development of academic-industry partnerships.
Why did you choose the SDR and how does it help support you in your work?
We chose the SDR to satisfy modern academic journals’ movement towards open-access datasets in an effort to increase repeatability and knowledge transfer.