Stress-Aware Hierarchical Model Predictive Control for Lead-Acid/LiFePO4 Hybrid Energy Storage Systems Using Measured PV-Load Data

Photovoltaic-integrated smart grids require energy storage systems that mitigate net load fluctuations without imposing excessive operating burden on battery subsystems. This study formulates a stress-prioritized hierarchical MPC-based control allocation strategy for a lead-acid/LiFePO4 hybrid energy storage system. Unlike grid-smoothing-oriented controllers, the proposed method explicitly incorporates lead-acid operating stress into the control objective and evaluation framework. The upper layer schedules the lead-acid battery using a low-frequency net load component while penalizing power magnitude, ramping, throughput, and state-of-charge deviation. The lower layer controls the LiFePO4 battery to compensate residual net load variations and reduce the burden imposed on the lead-acid subsystem. The method was evaluated using 696 hourly samples of measured photovoltaic generation and load demand data from the Naju Sports Park smart-grid site. Compared with the lead-acid-only MPC case, the proposed strategy reduced lead-acid throughput and equivalent full cycles by 66.5%, ramp burden by 76.4%, high-power operation time by 78.8%, and high-power operation energy by 81.0%. Compared with rule-based hybrid control, it reduced lead-acid throughput and equivalent full cycles by 38.4% while accepting a 3.3% increase in grid power standard deviation. These results indicate that the proposed strategy provides a practical stress-prioritized operating framework for lead-acid/LiFePO4 hybrid energy storage systems.