Scheduling Strategy for Power Systems with Multiple Energy Storage Systems Considering Renewable Energy and Load Uncertainty

In modern power systems, the integration of renewable energy sources has introduced significant challenges due to their inherent variability and uncertainty, compounded by fluctuating load demands. This paper presents a comprehensive study on power system scheduling. A detailed power system model is constructed, incorporating thermal power, compressed air energy storage (CAES), electrochemical energy storage (ECES), and renewable energy sources. Mathematical models and constraints for each component are meticulously formulated. Through day-ahead scheduling analysis under various energy storage configurations, it is demonstrated that hybrid energy storage systems can effectively reduce renewable energy curtailment, avoid load shedding, and lower scheduling costs. For intra-day scheduling, a horizon-adaptive model predictive control (MPC) method is proposed, which dynamically adjusts prediction and control horizons based on the fluctuations of renewable energy and load. Compared with the fixed-horizon MPC, it better aligns with the day-ahead scheduling plan, significantly enhancing the system’s flexibility and robustness.