In the quest to stabilize power grids and maximize renewable energy output, researchers have made a significant stride in optimizing wind farm operations. Chang Xu, a researcher from the School of Automation at Nanjing University of Science and Technology in China, has developed a novel strategy that promises to revolutionize how wind farms contribute to grid stability and economic efficiency.
Traditionally, wind farms operate in a mode called maximum power point tracking, where turbines are adjusted to capture the most power possible from the wind. However, as grids increasingly rely on renewable energy sources, the demand for frequency regulation has grown, pushing wind farms to adopt a different mode: deloading. This mode involves intentionally reducing the power output to help balance the grid, but it comes at a cost to the wind farm’s economic operation.
The challenge lies in accurately estimating the maximum power output of a wind farm, a task complicated by the wake effect. This phenomenon occurs when the wind turbines upstream disrupt the wind flow for those downstream, reducing their potential power generation. Existing deloading strategies often overestimate this maximum power, leading to excessive deloading and economic losses.
Xu’s research, published in the International Journal of Electrical Power & Energy Systems, addresses this issue head-on. “The key is to understand that the maximum power point for a wind turbine is not the same as the maximum power point for the entire wind farm due to the wake effect,” Xu explains. To tackle this, Xu and his team constructed a wind power analysis sensitivity model that considers the wake effect and the key parameters of wind turbines—rotor speed and pitch angle.
By analyzing the operating points of each wind turbine, they identified the configuration that maximizes the wind farm’s output, which is not the same as the traditional maximum power point. This insight led to the development of a new maximum power estimation method for wind farms and a precise deloading strategy that uses this estimated power as the baseline.
The implications for the energy sector are substantial. More precise deloading means wind farms can better meet grid demands without sacrificing economic efficiency. This could lead to significant cost savings for wind farm operators and contribute to more stable and reliable power grids.
“Our strategy not only improves the power generation efficiency of the wind farm but also ensures that the deloading demand of the grid is precisely met,” Xu says. The effectiveness of this method has been verified through simulations, paving the way for real-world applications.
As the energy sector continues to evolve, strategies like Xu’s could play a pivotal role in integrating more renewable energy sources into the grid. By optimizing wind farm operations, we can move closer to a future where renewable energy is not just a part of the solution but the solution itself. The research published in the International Journal of Electrical Power & Energy Systems, translated from English as the International Journal of Electrical Power and Energy Systems, marks a significant step forward in this journey.