Optimal Scheduling of Battery Energy Storage System Performing Stacked Services

Abstract

Grid-scale battery energy storage systems (BESSs) are at the forefront of technologies utilized to provide stability and flexibility to the power grid. As a result, BESSs generate significant revenue for their operators by participating in ancillary services such as the energy arbitrage and frequency regulation markets. Therefore, BESS operators can benefit from a model that allows them to optimize the bidding process for providing services while also optimizing scheduling in a way that best exploits each BESS cycle by simultaneously stacking various grid services. Estimating maximum BESS revenue is crucial to establishing financial sustainability for investors. In this paper, a BESS optimization model for multiple grid applications is proposed to estimate maximum daily revenue with an appropriate focus on maintaining the longevity of the BESS. The model aims to maximize the revenue generated by a BESS by allowing the system to participate in the energy arbitrage and frequency regulation markets at the same time. In this proposal, a new BESS scheduling method, tested using historical PJM market data, is used to improve revenue generation from providing ancillary services through effective and optimized bidding into the PJM regulation market based on buying/selling to/from the energy markets. The model is formulated as a mixed-integer linear programming (MILP).