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:不断增长的可再生能源对传统的电力时间安排构成重大挑战,运营商很难获得准确的可再生能源发电的日常预测,从而要求未来的排期系统作出与超短期预测相一致的实时时间安排决定。由于计算速度的限制,传统的优化方法无法解决这个问题。最近加固学习(RL)的发展显示了解决这一挑战的潜力。然而,现有的RL方法在制约因素复杂性、算法性能和环境忠诚方面不够充分。我们首先根据最新的强化学习算法和真正的电网环境提出系统解决方案。拟议方法使得能够规划和更及时地调整发电机的时间,包括单位承诺和经济发送,从而提高电网接纳更多可再生能源的能力。经过良好训练的排期代理器大大减少了可再生能源限制和装载,这是传统时间安排依赖不准确的日头预报引起的问题。高频控制决策利用了现有单位的灵活性,减少了电网对硬件转换的依赖,节省了投资和经济发送电网的更短时间分辨率调整,从而增强了电网接纳更多可再生能源的能力。这表现了可持续发电系统的实验性学习成果。</s>