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：可再生能源占比的持续增长给传统电力调度带来了严峻挑战。运营商难以获取准确的风光日前预测数据，从而要求未来调度系统能够根据超短期预测做出实时调度决策。受限于计算速度，传统的优化方法无法解决该问题。强化学习领域的最新进展展现出应对这一挑战的潜力，但现有方法在约束复杂度、算法性能及环境保真度方面仍存在不足。本文率先提出基于先进强化学习算法与实际电网环境的系统性解决方案。该方法能够实现发电机组规划与更精细时间尺度的调整（包括机组组合与经济调度），从而提升电网对可再生能源的消纳能力。训练完成的调度智能体显著降低了由传统调度依赖不准确日前预测所引发的弃风弃光与负荷削减问题。实验结果表明，高频控制决策充分利用现有机组调节灵活性，减少了电网对硬件改造的依赖，节约了投资与运行成本。本研究展现了强化学习在推动低碳智能电力系统发展中的潜力，标志着向可持续发电迈出了坚实一步。