  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.
