Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in this context might make conventional power dispatch strategies infeasible. Reinforcement-learning based (RL) controllers can address this challenge, however, cannot themselves provide safety guarantees, preventing their deployment in practice. To overcome this limitation, we propose a formally validated RL controller for economic dispatch. We extend conventional constraints by a time-dependent constraint encoding the islanding contingency. The contingency constraint is computed using set-based backwards reachability analysis and actions of the RL agent are verified through a safety layer. Unsafe actions are projected into the safe action space while leveraging constrained zonotope set representations for computational efficiency. The developed approach is demonstrated on a residential use case using real-world measurements.

翻译：未来电力系统将高度依赖具有高比例分散式可再生能源和储能系统的微电网。在此背景下，高度复杂性和不确定性可能使传统电力调度策略难以实施。基于强化学习（RL）的控制器能够应对这一挑战，但其本身无法提供安全性保证，阻碍了实际部署。为克服这一限制，我们提出一种经过形式化验证的强化学习控制器用于经济调度。我们通过编码孤岛应急事件的时间相关约束来扩展传统约束条件。该应急约束采用基于集合的反向可达性分析进行计算，并通过安全层对强化学习智能体的动作进行验证。不安全动作被投影至安全动作空间，同时利用约束零超多面体集合表示以提高计算效率。所开发的方法在使用实际测量数据的住宅应用案例中得到了验证。