Fuzzy optimization-based sizing of a battery energy storage system for participating in ancillary services markets

Due to the accelerated advancements in battery manufacturing recently, battery energy storage systems (BESS) have become more economically viable than ever before for power grid applications. Given their fast response, BESS are known for their efficacy in providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. However, for an investor willing to provide AS to the electricity markets via BESS, properly sizing the storage system presents a challenge. In this paper, a BESS sizing optimization model is proposed. The model aims to identify the BESS optimal power and energy capacities that maximize the investor's long-term profitability. The optimization model considers the physical properties of BESS, such as degradation due to cycling. It also accounts for the uncertainties associated with the AS markets, mainly inaccurate AS price forecasts. To ensure computational tractability, the optimization is modeled as a fuzzy linear program (FLP). To verify its effectiveness, the proposed FLP-based optimization is compared with its deterministic counterpart. Simulation results using real data obtained from the ERCOT market demonstrates the effectiveness of the proposed FLP-based model, as compared to its deterministic counterpart, in identifying the optimal BESS size in a computationally tractable manner while capturing the market-related uncertainties.