AI 中文总结
针对电力系统多目标运行需求，提出统一控制的深度强化学习智能体，兼顾热安全与关键模式阻尼，在扰动下实现更好的运行平衡与性能提升。
AI 中文摘要
持续的能源转型对电力系统的稳定运行提出了挑战，增加了不确定性下快速决策的需求。尽管强化学习已成为电力系统控制与运行的有前景框架，但现有应用通常聚焦单一运行准则，如热安全或小信号稳定性。然而电力系统运行本质上是多目标的，各目标间可能存在权衡。本文开发了一种统一控制的深度强化学习智能体，该智能体在随机负荷变化下维持热安全，同时引导系统向阻尼最关键模式的运行点移动。与仅考虑热安全的智能体和常规策略相比，所提智能体在考虑的运行目标间实现了更好的平衡，阻尼显著提升且热安全违规可忽略不计。最后，在小信号和大信号扰动下验证了提升关键阻尼的运行价值，阻尼更高的运行点能实现更快的振荡衰减并改善关键清除时间。
英文摘要
The ongoing energy transition challenges the stable operation of power systems and increases the need for rapid decision-making under uncertainty. While reinforcement learning has emerged as a promising framework for power system control and operation, existing applications typically focus on a single operational criterion, such as thermal security or small-signal stability. However, power system operation is inherently multi-objective and may involve trade-offs between objectives. This paper develops a unified-control deep reinforcement learning agent that maintains thermal security under stochastic load variations while steering the system toward operating points with improved damping of the most critical mode. Compared to a thermal-security-only agent and a business-as-usual policy, the proposed agent achieves a better balance among the operational objectives considered, with notably improved damping and negligible thermal-security violations. Finally, the operational value of increased critical damping is demonstrated under small- and large-signal disturbances, where operating points with higher damping lead to faster oscillation decay and improved critical clearing times.
