In the vast expanses of China’s deserts and Gobi regions, a new energy revolution is taking shape. Large-scale wind farms are springing up, harnessing the power of the wind to drive the nation’s transition to cleaner energy. However, integrating these intermittent power sources into the grid presents significant challenges, particularly when paired with limited thermal power and energy storage capacities. A recent study published in the *Journal of Shanghai Jiao Tong University* offers a promising solution to these complexities, with implications that could resonate across the global energy sector.
The research, led by Yinguo Yang from the Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., and a team from Tsinghua University, introduces a novel approach to scheduling and operating wind-thermal-storage energy bases. The method aims to address the constrained flexibility of these systems, which often struggle with the variability of wind power.
“Our method focuses on two main stages: day-ahead scheduling and real-time dispatch,” explains Yinguo Yang. “In the day-ahead stage, we determine the startup and shutdown plans for thermal units and their adjustable output ranges based on a rough prediction of wind power. This sets the stage for the real-time dispatch, where we use a quantile-based rule to generate dispatch strategies according to the current wind power output.”
The innovation lies in the use of a wind power accommodation interval, constructed based on the adjustable range of thermal power output and the operational constraints of energy storage. This interval allows for a more flexible and responsive system, capable of accommodating the uncertainties inherent in wind power generation.
One of the most compelling aspects of this research is its potential to reduce reliance on high-precision forecasts, which can be costly and unreliable. “Our dispatch strategies, generated by the quantile rule, inherently satisfy system operational constraints,” says Yang. “This means we can eliminate the need for high-precision forecasts, making the system more robust and adaptable.”
The study’s findings are particularly relevant for the energy sector, as they demonstrate that the proposed method outperforms rolling optimization methods when the three-step prediction error exceeds 10%. This could have significant commercial implications, as it suggests that improving the accuracy of day-ahead or intraday short-term forecasts could enhance the performance of operational scheduling.
The research not only provides a practical solution to the challenges of integrating wind power into the grid but also offers a valuable reference for the operation of large-scale new energy bases. As the world continues to transition towards cleaner energy sources, the insights gained from this study could play a crucial role in shaping the future of energy systems.
In an era where the need for sustainable and reliable energy is more pressing than ever, this research offers a beacon of hope. By addressing the complexities of wind-thermal-storage systems, it paves the way for a more flexible, efficient, and resilient energy future.