The growing renewable energy sources have posed significant challenges to
traditional power scheduling. It is difficult for operators to obtain accurate
day-ahead forecasts of renewable generation, thereby requiring the future
scheduling system to make real-time scheduling decisions aligning with
ultra-short-term forecasts. Restricted by the computation speed, traditional
optimization-based methods can not solve this problem. Recent developments in
reinforcement learning (RL) have demonstrated the potential to solve this
challenge. However, the existing RL methods are inadequate in terms of
constraint complexity, algorithm performance, and environment fidelity. We are
the first to propose a systematic solution based on the state-of-the-art
reinforcement learning algorithm and the real power grid environment. The
proposed approach enables planning and finer time resolution adjustments of
power generators, including unit commitment and economic dispatch, thus
increasing the grid's ability to admit more renewable energy. The well-trained
scheduling agent significantly reduces renewable curtailment and load shedding,
which are issues arising from traditional scheduling's reliance on inaccurate
day-ahead forecasts. High-frequency control decisions exploit the existing
units' flexibility, reducing the power grid's dependence on hardware
transformations and saving investment and operating costs, as demonstrated in
experimental results. This research exhibits the potential of reinforcement
learning in promoting low-carbon and intelligent power systems and represents a
solid step toward sustainable electricity generation.

该研究利用最新的强化学习算法和真实的电力网络环境，提出了一种系统的解决方案来解决传统能源调度面临的问题，并为电力系统提供了一个可持续的低碳未来。