Optimal Sizing of Behind-the-Meter Battery Storage for Providing Profit-Oriented Stackable Services

This paper focuses on an advanced optimization method for optimizing the size of the behind-the-meter (BTM) battery energy storage system (BESS) that provides stackable services to improve return on investment. The grid frequency regulation service and two customer-side services, i.e., energy arbitrage and peak shaving, are selected as stackable services of BTM BESS. A two-stage stochastic programming model is proposed to handle uncertainty and achieve the most cost-effective BTM BESS size. The first stage obtains the optimal BTM BESS size with the maximum annual net income. The operating strategy of the BTM BESS is optimized in the second stage to maximize revenue while considering the BTM BESS degradation cost and the uncertainty of operating scenarios. A hybrid solution algorithm combining genetic algorithm and mixed-integer linear programming is employed to solve the two-stage stochastic programming model. A strategy based on a recorder and a filter is proposed to speed up the solution. In addition, a novel method for converting the secondlevel regulation signal into the equivalent minute-level signal is offered to reduce computational burden while maintaining high accuracy. Finally, the effectiveness of the proposed method is validated based on an industrial load and regulation information from the Pennsylvania-New Jersey-Maryland (PJM) market.