Research on Reserve Capacity Optimization of Hydro-Wind-Solar Power Systems Based on Two-Stage Optimization

The increasing penetration of wind and photovoltaic power intensifies power fluctuations and raises the requirement for reserve capacity allocation in hydro-wind-solar (HWS) systems. To address this issue, this study proposes a two-stage optimization framework for coordinated reserve configuration. In the first stage, the entropy weight method is used to evaluate heterogeneous reserve resources according to unit capacity cost, response time, and carbon emission intensity, thereby determining their response priority and obtaining an initial reserve allocation. In the second stage, alternative preference coefficient ratios for economy, rapidity, and low-carbon performance are assessed, and the resulting allocation proportions are fed back to the first stage to form a closed-loop optimization process. To solve the model, an improved Osprey Optimization Algorithm incorporating a Lens Imaging Opposition-Based Learning mechanism is adopted. A case study based on the Wudongde regional grid shows that the 2:1:2 preference-ratio scenario provides the best overall trade-off among the tested cases, with a reserve cost of 18,640.38 CNY (Chinese Yuan), carbon emissions of 8718.30 kg CO₂, and a response time of 4336.7 s. Compared with representative benchmark models, the proposed method achieves lower carbon emissions and faster response while maintaining competitive economic performance. The results demonstrate that the proposed framework can improve reserve allocation quality and operational adaptability in HWS systems with high renewable penetration.