A Data-Driven Real-Time Optimal Power Flow Algorithm Using Local Feedback

TL;DR

This paper introduces a data-driven, real-time optimal power flow algorithm leveraging local measurements and neural networks to efficiently manage distributed energy resources in power networks.

Contribution

It develops a novel neural network-based method for real-time, local feedback control of power flow, with theoretical guarantees and practical efficiency.

Findings

Achieves higher accuracy in tracking time-varying OPF solutions.

Faster computation compared to benchmark methods.

Demonstrates effectiveness on IEEE 37-bus test feeder.

Abstract

The increasing penetration of distributed energy resources (DERs) adds variability as well as fast control capabilities to power networks. Dispatching the DERs based on local information to provide real-time optimal network operation is the desideratum. In this paper, we propose a data-driven real-time algorithm that uses only the local measurements to solve time-varying AC optimal power flow (OPF). Specifically, we design a learnable function that takes the local feedback as input in the algorithm. The learnable function, under certain conditions, will result in a unique stationary point of the algorithm, which in turn transfers the OPF problems to be optimized over the parameters of the function. We then develop a stochastic primal-dual update to solve the variant of the OPF problems based on a deep neural network (DNN) parametrization of the learnable function, which is referred to as the training stage. We also design a gradient-free alternative to bypass the cumbersome gradient calculation of the nonlinear power flow model. The OPF solution-tracking error bound is established in the sense of universal approximation of DNN. Numerical results on the IEEE 37-bus test feeder show that the proposed method can track the time-varying OPF solutions with higher accuracy and faster computation compared to benchmark methods.