Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening
2026-08-31 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors introduce a method called Distributionally Robust Conformal Safety Screening (DR-CSS) to check if new voltage control policies for electric grids are safe before using them in real life. Their approach uses historical data and imperfect simulations to predict if the new control will cause voltage problems, even under changing conditions. DR-CSS creates safety margins around predictions and adjusts them for possible changes caused by deploying the new policy. Tested on standard grid models, their method successfully flags unsafe scenarios while reducing false alarms through adaptive safety intervals and gradual policy rollouts.
voltage controlactive distribution gridscontrol policysimulationconformal safety intervalsdistributional robustnessclosed-loop controlIEEE 33-bus systemIEEE 141-bus systemlearning-based control
Authors
Sarra Bouchkati, Petros Ellinas, Adriana Geisler, Steffen Kortmann, Johanna Vorwerk, Spyros Chatzivasiliadis, Andreas Ulbig
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.