An MILP Approach for Modeling and Analyzing the BESS for Smoothing Renewable Fluctuations Considering BESS Capacity Attenuation in the Bulk Power System With High Inverter-Based Resource Penetration

Analyzing the effectiveness of the battery energy storage system (BESS) in smoothing renewable fluctuations correctly is vital to the stable operation of the power system. Therefore, based on stochastic programming and attention-based time series forecasting with learnable and interpretable basis (BasisFormer), we propose a mixed integer linear programming (MILP) approach for modeling the BESS and analyzing the effectiveness of the BESS in smoothing renewable fluctuations in the bulk power system with high inverter-based resources. The approach considers the attenuation of BESS capacity and the uncertainty of renewable generation. First, we present a scenario generation and reduction method based on Latin hypercube sampling (LHS) and the Kantorovich distance. Second, we propose a state of health (SoH) prediction method of the BESS based on BasisFormer. Third, we propose an MILP approach for modeling the BESS and analyzing the effectiveness of the BESS in smoothing renewable fluctuations. Finally, we verify the proposed approach through case studies. The experimental results show that the attenuation of BESS capacity is a crucial factor in correctly analyzing the effectiveness of the BESS in smoothing renewable fluctuations.