Constraint-Aware Calibration of Power-Flow Surrogates: DG Hosting Capacity as a Downstream Application

Power-flow surrogates reduce the cost of repeated distribution-network studies, but low regression error does not guarantee reliable feasibility decisions near technical limits. We evaluate constraint-aware calibration for a continuous-output Base–Increment multilayer perceptron trained on OpenDSS simulations of a real unbalanced medium-voltage network. The surrogate predicts electrical variables, from which voltage, loss, and conductor-loading constraints are evaluated rather than learning feasibility or hosting capacity directly. Four one-sided empirical calibration strategies are compared: output-space, uniform constraint-space, constraint-specific, and joint-vector. Configurations are selected through repeated group-aware calibration partitions and frozen before evaluation on an independent TEST partition of 3240 operating points grouped into 108 base scenarios. DG hosting capacity provides an illustrative downstream application across 72 hourly conditions, with matching OpenDSS and surrogate constraints and search procedures. High global regression accuracy coexisted with optimistic feasibility decisions near operating limits. Calibration reduced false-feasible decisions, although stronger corrections also reduced the surrogate-accepted feasible region. In the HC illustration, the uncalibrated surrogate showed the lowest hourly error but more frequent overestimation. Surrogates substantially reduced online HC computation time; offline preparation and training were excluded. These results support evaluating surrogates through both regression accuracy and decision reliability.