  In this work, we investigate a data-driven approach for obtaining a reduced
equivalent load model of distribution systems for electromechanical transient
stability analysis. The proposed reduced equivalent is a neuro-physical model
comprising of a traditional ZIP load model augmented with a neural network.
This neuro-physical model is trained through differentiable programming. We
discuss the formulation, modeling details, and training of the proposed model
set up as a differential parametric program. The performance and accuracy of
this neurophysical ZIP load model is presented on a medium-scale 350-bus
transmission-distribution network.
