Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening
eess.SY, cs.AI, cs.LG, cs.SY
Submitted: 2026-08-31
Updated: 2026-08-31
Comments: Sarra Bouchkati, Petros Ellinas, and Adriana Geisler contributed equally to this work
Code: https://github.com/sarraBou/DRCSS
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: 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.
Terminology
Abstract
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.
Sources
- Making Distribution State Estimation Practical: Challenges and Opportunities
- Learning Power Flow with Confidence: A Probabilistic Guarantee Framework for Voltage Risk
- Conformal Off-Policy Prediction in Contextual Bandits
- A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
- Conformal Prediction Under Covariate Shift
Related papers
- One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
- A Geometric Decision Procedure for STL Feasibility and Repair
- Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
- Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model
- Minimal Experiments for Robust Stabilization: Information, Spectral Geometry, and Duration
- Decentralized Power-Optimal Coordination for Spacecraft Swarms Using Time-Varying Magnetorquer Actuation