University of California, Merced Merced, United States of America
In this work, we address the problem of accelerating complex power-grid simulation through machine learning (ML). Specifically, we develop a framework, Smart-PGsim, which generates multitask-learning (MTL) neural network (NN) models to predict the initial values of variables critical to the problem convergence. MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables.
We show that, to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model. Smart-PGsim then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. Smart-PGsim brings 2.60x speedup on average (up to 3.28x) computed over 10,000 problems, without losing solution optimality.