Behavior-Informed EV Flexibility Estimation and Availability-Aware Voltage Control

Using electric vehicles (EVs) for grid support requires reliable flexibility estimates and effective voltage control from limited online information. We propose a behavior-informed interacting multiple-model estimator (BIMM) and an availability-aware voltage-control strategy. The estimator combines predicted behavior-mode probabilities with station-level measurements to estimate aggregate upward and downward energy flexibility and its uncertainty. The controller combines behavior-derived availability priors with online measurements to update station-level reactive-power limits and allocate voltage support. In paired simulations, explicit behavior-prior pooling reduces the normalized continuous ranked probability score (nCRPS) for energy by 54.0% and 61.8% in IID and OOD, respectively, relative to the same-bank no-pooling reference. Relative to the standard interacting multiple-model estimator (Standard-IMM), the normalized root-mean-square error (NRMSE) for energy decreases by 23.36% and 39.48% in the batch-charging scenarios S1 and S2, respectively; energy losses in ordinary IID/OOD operation are higher than those of Standard-IMM. In ordinary control scenarios, mean action-time request-exceedance rates are lower than for fast-measurement-only voltage control (FastOnly). These results demonstrate the use of aggregate behavior information for probabilistic flexibility estimation and availability-aware reactive-power dispatch.