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In this work, we investigate a data-driven approach for obtaining a reducedequivalent load model of distribution systems for electromechanical transientstability analysis. The proposed reduced equivalent is a neuro-physical modelcomprising of a traditional ZIP load model augmented with a neural network.This neuro-physical model is trained through differentiable programming. Wediscuss the formulation, modeling details, and training of the proposed modelset up as a differential parametric program. The performance and accuracy ofthis neurophysical ZIP load model is presented on a medium-scale 350-bustransmission-distribution network.

Neuro-physical dynamic load modeling using differentiable parametric optimization