# Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening

arXiv: https://arxiv.org/abs/2608.30889 · HTML: https://mlsanity.com/paper/2608.30889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 15 of 15)

- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019)
- [Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods](https://mlsanity.com/paper/2202.11762/) (2022)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Conformal Off-Policy Prediction in Contextual Bandits](https://mlsanity.com/paper/2206.04405/) (2022)
- [Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopes](https://mlsanity.com/paper/2207.02715/) (2022)
- [Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networks](https://mlsanity.com/paper/2007.02991/) (2020)
- [Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems](https://mlsanity.com/paper/2003.03681/) (2020)
- [Neural Network Verification in Control](https://mlsanity.com/paper/2110.01388/) (2021)
- [Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets](https://mlsanity.com/paper/2403.20149/) (2024)
- [Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operators](https://mlsanity.com/paper/2410.24162/) (2024)
- [Conformal Off-Policy Prediction for Multi-Agent Systems](https://mlsanity.com/paper/2403.16871/) (2024)
- [Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk](https://mlsanity.com/paper/2308.07867/) (2023)
- [Hardware-in-the-Loop Co-Simulation Based Validation of Power System Control Applications](https://mlsanity.com/paper/1812.09598/) (2018)
- [Making Distribution State Estimation Practical: Challenges and Opportunities](https://mlsanity.com/paper/2311.07021/) (2023)

## Abstract (arXiv)

Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical grid. This assessment is difficult for two reasons. First, simulations cannot capture every disturbance, modeling error, and device interaction present in the real grid. Second, historical measurements reflect operation under existing control policies, whereas a new policy may drive the grid into different operating conditions. To address these challenges, we propose Distributionally Robust Conformal Safety Screening (DR-CSS), a policy-agnostic framework for pre-deployment, scenario-by-scenario screening of a new control policy using historical data and a nominal simulator. For each new scenario, the simulator predicts a future voltage trajectory for the whole grid; DR-CSS then constructs a conformal safety interval around this prediction using historical simulation-to-reality errors. The interval is further enlarged to account for closed-loop changes induced by the deployment of the new policy and its interactions with the remaining controllers. To the best of our knowledge, DR-CSS is the first framework in power systems to combine historical data from an existing control policy with an imperfect simulator for pre-deployment safety screening of a new policy. Experiments on the IEEE 33-bus and IEEE 141-bus systems evaluate the deployment of learning-based voltage control policies and show that DR-CSS identifies all unsafe test scenarios. To reduce unnecessary warnings on safe scenarios, we adapt the safety intervals to different operating conditions and gradually introduce new policies with recalibration after each stage. These extensions increase the informational value of the safety screening and support safer deployment decisions in active distribution grids.