A Data-driven Nonlinear Recharge Controller for Energy Storage in Frequency Regulation

TL;DR

This paper introduces a data-driven nonlinear recharge controller for energy storage in frequency regulation, improving response accuracy and reducing deviations compared to traditional methods.

Contribution

It presents a novel nonlinear feedback controller designed via a best-hindsight optimization framework, adapting to storage state of charge and ratings.

Findings

Achieves smaller deviations in area control error.

Reduces state of charge distortion.

Outperforms benchmark controllers in simulations.

Abstract

Battery energy storage boosts up the response speed of power system frequency regulation, but must be recharged carefully to minimize the distortion to the frequency regulation response. This paper proposes a nonlinear feedback controller to optimize the recharge for storage resources in frequency regulation. This controller is designed using a data-driven best-hindsight optimization framework, the resulting nonlinear recharge controller's gain depends on the storage state of charge as well as its power and energy rating. The developed controller is compared with two benchmark automatic generation control designs, one is a proportional-integral-based control from PJM Interconnection, the other one is based on linear-quadratic regulator. Simulation results using real area control error data from PJM Interconnection show the proposed controller achieves smaller deviations in both the area control error and the storage state of charge compared to the two benchmark controllers under various storage configurations.