Optimal sizing of behind-the-meter BESS for providing stackable services

The behind-the-meter (BTM) battery energy storage system (BESS) is mainly utilized for providing load management. But the saved electricity bill hardly offsets the high upfront investment cost. The multi-revenue streams created by certain stackable services can offset the initial cost by reasonably designing the size and operation strategy of BESS. Therefore, to maximize the return rate on BESS investment, a two-stage optimal model for optimizing the power and energy capacity of a BTM BESS is proposed in this paper. The provided stackable services by BESS include energy arbitrage and frequency regulation. A hybrid algorithm combining the genetic algorithm and a mixed-integer linear programming model is employed to co-optimize the size and operation strategy of BESS. The real load data from the plastic manufacturing industry and the frequency regulation information from the PJM market are employed as databases to validate the availability and effectiveness of the proposed model and the hybrid algorithm.