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.

翻译：可再生能源的日益增长给传统电力调度带来了重大挑战。运营商难以获得精确的日前可再生能源出力预测，因此需要未来的调度系统能够依据超短期预测做出实时调度决策。受限于计算速度，传统的基于优化的方法无法解决该问题。强化学习的最新发展已显示出解决这一挑战的潜力。然而，现有强化学习方法在约束复杂度、算法性能和环境保真度方面存在不足。我们首次提出了一种基于最先进强化学习算法和真实电网环境的系统性解决方案。所提方法能够实现发电机的规划与更精细时间尺度调节，包括机组组合和经济调度，从而提高电网接纳更多可再生能源的能力。训练有素的调度智能体显著减少了可再生能源弃电和负荷削减——这些问题源于传统调度对不准确日前预测的依赖。高频控制决策利用了现有机组的灵活性，降低了对电网硬件改造的依赖，并节省了投资与运行成本，实验结果验证了这一点。本研究展示了强化学习在促进低碳智能电力系统方面的潜力，是迈向可持续发电的坚实一步。