Adaptive Real-Time Grid Operation via Online Feedback Optimization with Sensitivity Estimation

TL;DR

This paper introduces an online feedback optimization method with real-time sensitivity estimation for grid control, enabling adaptive, model-free, and high-speed operation that improves accuracy and robustness in dynamic power systems.

Contribution

It develops a recursive least-squares sensitivity estimator integrated into OFO, allowing model-free, real-time grid optimization with proven convergence.

Findings

Effective sensitivity learning during operation

Improved grid regulation accuracy

Validated on IEEE 123-bus test feeder

Abstract

In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input-output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a time-varying AC Optimal Power Flow (AC-OPF). Instead of relying on a full grid model, e.g., grid admittance matrix, OFO only requires the steady-state sensitivity relating a change in the controllable inputs, e.g., power injections set-points, to a change in the measured outputs, e.g., voltage magnitudes. Since an inaccurate sensitivity may lead to a model-mismatch and jeopardize the performance, we propose a recursive least-squares estimation that enables OFO to learn the sensitivity from measurements during real-time operation, turning OFO into a model-free approach. We analytically certify the convergence of the proposed OFO with sensitivity estimation, and validate its performance on a simulation using the IEEE 123-bus test feeder, and comparing it against a state-of-the-art OFO with constant sensitivity.