DeepONet Based Uncertainty Quantification for Power System Dynamics with Stochastic Loads

This paper proposes a DeepONet-based uncertainty quantification framework for power system dynamics with stochastic loads modeled via the Ornstein-Uhlenbeck process. We model the stochastic power system using stochastic differential and algebraic equations (SDAE), which allow us to transport the stochasticity from the loads through the power system dynamics. Then, the proposed DeepONet-based framework learns a parametric surrogate for the infinite-dimensional solution operator of the SDAEs. This surrogate solution operator allows us to quantify uncertainty by mapping the stochastic response of the loads to statistical estimates of power system dynamic trajectories, which contain critical information for operators and planners. The effectiveness and scalability of our method are verified (i) using the IEEE 39-bus and the WECC 179-bus systems and (ii) by comparing it to Monte Carlo and other surrogate baselines.