Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement

Abstract

The transient performance 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 difficulty of accurately predicting net load, we propose an online optimization scheme that can adapt well to changes in an unknown and variable environment. To leverage the synergy between BESSs and slowramping Conventional Generators (CGs), we use 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 step size for compliance with the ramping needs upon detecting a potentially destabilizing event. The proposed scheme improves the transient behavior of the system by bridging the gap in AGC service and ensuring near-optimal operation. We demonstrate the effectiveness and scalability of the proposed scheme through comprehensive case studies.

Evaluation of technical and financial benefits of battery energy storage system control strategies

“…Prosumers may have contractual obligations towards operating energy storage systems with utilities or energy providers [15,16]. Some grid regulations may affect the battery charge control strategy, limiting the energy fed back to the grid or the instantaneous power injection and instantaneous ramp rate [17,18]. The charge control strategy must be compatible with the obligations arising from this contract, such as energy exports [19], participation in grid services [20], etc., to potentially benefit from any benefits or incentives [21].…”