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

arXiv 2608.30889 ↗First posted 31 Aug 2026eess.SY cs.AI cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 31 Aug 2026.

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15 of 28 references.

References · 15

  1. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  2. 2019Conformal Prediction Under Covariate Shiftcited by 511
  3. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 148
  4. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  5. 2022Conformal Off-Policy Prediction in Contextual Banditscited by 25
  6. 2022Open- and Closed-Loop Neural Network Verification using Polynomial Zonotopescited by 24
  7. 2020Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution Networkscited by 16
  8. 2021Neural Network Verification in Controlcited by 14
  9. 2020Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systemscited by 12
  10. 2024Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Marketscited by 8
  11. 2024Conformalized Prediction of Post-Fault Voltage Trajectories Using Pre-trained and Finetuned Attention-Driven Neural Operatorscited by 5
  12. 2024Conformal Off-Policy Prediction for Multi-Agent Systemscited by 3
3 more
  1. 2023Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Riskcited by 2
  2. 2018Hardware-in-the-Loop Co-Simulation Based Validation of Power System Control Applicationscited by 1
  3. 2023Making Distribution State Estimation Practical: Challenges and Opportunitiescited by 1

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