In the heart of Vietnam’s burgeoning energy sector, a groundbreaking study is set to revolutionize how wind farms integrate with the national grid. Led by Thanh Hai Dinh, a researcher from Can Tho University, this innovative work focuses on short-term active power forecasting for wind farms using artificial neural networks (ANNs). The research, published in the CTU Journal of Innovation and Sustainable Development, translates to the Journal of Innovation and Sustainable Development at Can Tho University, holds significant implications for the stability and efficiency of Vietnam’s power grid.
As Vietnam continues to upgrade its 500 kV grid infrastructure, the concentration of wind farms in specific regions presents both opportunities and challenges. While these farms contribute to the country’s renewable energy goals, they can also cause significant power influxes, leading to grid overcurrent issues. This is where Dinh’s research comes into play.
“The National Load Dispatch Center currently regulates power generation based on forecasted data,” Dinh explains. “Accurate short-term power forecasting for wind farms is crucial to mitigate grid overcurrent and ensure stable power supply.”
Dinh’s model, developed on the Matlab platform, uses ANNs to predict active power in wind farms with remarkable accuracy. The model was tested using real data from the Ia Pết Đăk Đoa 1 wind farm in Gia Lai province, with forecasts given in 15-minute intervals for the next four hours. The results were impressive, demonstrating low errors and significant time savings in calculations.
The commercial impacts of this research are substantial. By providing more accurate and timely forecasts, wind farm operators can better manage their power output, reducing the risk of grid overcurrent and improving overall efficiency. This not only benefits the operators but also the national grid and consumers, who can expect a more stable and reliable power supply.
Moreover, this research paves the way for future developments in the field. As Dinh notes, “The superiority of the method in forecasting with low errors and saving calculation time opens up new possibilities for integrating renewable energy sources into the grid.”
The energy sector is on the cusp of a significant shift, and Dinh’s work is at the forefront of this change. By harnessing the power of ANNs, wind farms can become more predictable and reliable, contributing to a more sustainable and efficient energy future. As Vietnam continues to invest in its renewable energy infrastructure, research like Dinh’s will be instrumental in shaping the country’s energy landscape.