Battery Energy Storage Capacity Sizing from a System Operator Perspective Considering Time-Frequency Correlation between PV and Wind Power

We propose a battery energy storage system (BESS) sizing method that uses time-frequency correlation between photovoltaic (PV) and wind to mitigate net-load variability without oversizing. Using 2019~2023 hourly data from four renewable-rich regions in Korea, we apply FFT-based normalized co-spectrum analysis to identify complementary time scales. Short-time Fourier transform (STFT) is then employed to derive time-varying weights emphasizing anti-correlated, high-output periods. With these weights, we formulate an optimization model that jointly determines BESS power and energy capacities by minimizing weighted residual net-load variability with capacity-penalty terms, subject to operational constraints. Across four regions and seasons, the proposed method achieves net-load smoothing comparable to an unweighted baseline while reducing energy capacity by 45.44~75.55% and operating duration by 18.5~70.99%. Larger gains occur when PV-wind complementarity is strong near the diurnal band.