Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement

TL;DR

This paper introduces an online optimization method for allocating BESS ramping reserves to enhance AGC transient response, adapting to environmental changes and improving power system stability.

Contribution

It proposes a novel distributed optimization algorithm with adaptive learning rates for BESS reserve allocation in AGC systems, addressing environmental variability.

Findings

Improves AGC transient behavior significantly.

Demonstrates scalability through case studies.

Enhances ramping reserve utilization.

Abstract

The transient behavior of Automatic Generation Control (AGC) systems is a critical aspect of power system operation. Therefore, fully extracting the potential of Battery Energy Storage Systems (BESSs) for AGC enhancement is of paramount importance. In light of the challenges posed by diverse resource interconnections and the variability associated, we propose an online optimization scheme that can adapt to changes in an unknown and variable environment. To leverage the synergy between BESSs and Conventional Generators (CGs), we devise a variant of the Area Injection Error (AIE) as a measure to quantify the ramping needs. Based on this measure, we develop a distributed optimization algorithm with adaptive learning rates for the allocation of the ramping reserve. The algorithm restores a larger learning rate for compliance with the ramping needs upon detecting a potentially destabilizing event. We demonstrate the effectiveness and scalability of the proposed scheme through comprehensive case studies. It is shown that the proposed scheme can improve the transient behavior of the AGC system by bridging the gap in ramping capability.