发表机构
Auburn University; Florida State University; Oak Ridge National Laboratory（奥本大学; 佛罗里达州立大学; 橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出数据同化辅助的强化学习方法，利用集成分数滤波器校正负荷信息，在简化输电网络中显著降低负荷估计误差并提升控制性能。
AI 中文摘要
可靠的电网控制需要在输电约束、时变负荷、可再生能源波动性和不完善的负荷信息下进行序贯决策。我们研究负荷信息质量如何影响在一个受Grid2Op启发、基于直流潮流模型的定制化输电网络模拟器中的控制。该环境包括随机空间负荷、时间相关的类可再生能源发电、储能、线路保护、发电机调度和线路重连。集成分数滤波器（EnSF）在噪声负荷信息传递给控制器之前对其进行校正。一项启发式研究首先在固定反馈规则下隔离了这种校正的影响。然后训练近端策略优化（PPO）智能体用于序贯发电机和重连控制，冻结后，在配对的随机场景中使用前向、EnSF校正和真实负荷信息进行评估。EnSF持续降低负荷估计误差。在启发式控制下，校正延长了生存时间并减少了警告和过载暴露。在冻结的PPO策略下，它增加了累计回报并使性能更接近真实基准，当负荷不确定性被放大到超出名义训练分布时优势更大。在这两个实验中，EnSF降低了负荷估计误差并改善了简化输电网络中的最终控制性能。
英文摘要
Reliable power grid control requires sequential decisions under transmission constraints, time-varying demand, renewable variability, and imperfect load information. We investigate how load information quality affects control in a customized transmission network simulator inspired by Grid2Op and based on a DC power flow model. The environment includes stochastic spatial loads, temporally correlated renewable-like generation, energy storage, line protection, generator dispatch, and line reconnection. An Ensemble Score Filter (EnSF) corrects noisy load information before it is passed to the controller. A heuristic study first isolates the effect of this correction under a fixed feedback rule. Proximal policy optimization (PPO) agents are then trained for sequential generator and reconnection control, frozen, and evaluated with Forward, EnSF-corrected, and Truth load information on paired stochastic scenarios. EnSF consistently lowers load estimation error. Under heuristic control, the correction extends survival and reduces warning and overload exposure. With frozen PPO policies, it increases cumulative return and keeps performance closer to the Truth benchmark, with a larger advantage when load uncertainty is amplified beyond the nominal training distribution. In both experiments, EnSF reduces load-estimation error and improves the resulting control performance in the simplified transmission network.
