# Distributionally Robust Safety Filter for Learning-Based Control in Active Distribution Systems

arXiv: https://arxiv.org/abs/2307.16351 · HTML: https://mlsanity.com/paper/2307.16351/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems](https://mlsanity.com/paper/2003.03681/) (2020)
- [On Distributionally Robust Chance Constrained Programs with Wasserstein Distance](https://mlsanity.com/paper/1806.07418/) (2018)
- [Wasserstein Distributionally Robust Look-Ahead Economic Dispatch](https://mlsanity.com/paper/2003.04874/) (2020)

## Abstract (arXiv)

Operational constraint violations may occur when deep reinforcement learning (DRL) agents interact with real-world active distribution systems to learn their optimal policies during training. This letter presents a universal distributionally robust safety filter (DRSF) using which any DRL agent can reduce the constraint violations of distribution systems significantly during training while maintaining near-optimal solutions. The DRSF is formulated as a distributionally robust optimization problem with chance constraints of operational limits. This problem aims to compute near-optimal actions that are minimally modified from the optimal actions of DRL-based Volt/VAr control by leveraging the distribution system model, thereby providing constraint satisfaction guarantee with a probability level under the model uncertainty. The performance of the proposed DRSF is verified using the IEEE 33-bus and 123-bus systems.