水风光混合系统中现有水电站扩容优化的解析方法：以雅砻江流域为例

Abstract Hydropower can effectively integrate intermittent wind and photovoltaic (PV) power by forming a hydro-wind-PV hybrid energy system . With increases in wind and PV power plants, it is crucial to expand hydropower capacity. However, traditional numerical methods for capacity expansion require high temporal resolution of input data and complex simulation calculations. To address this issue, an analytical method is proposed to derive the optimal expanded size of hydropower plant , without high-resolution input data, facilitating implementation, and enabling sensitivity analysis. Firstly, hydropower generation and the wind-PV power curtailment rate were estimated using a polynomial function and a linear function, respectively, based on the expanded hydropower size and derived from historical operational data. Next, the capacity expansion function of the hydropower plant was derived based on the net present value method, accounting for total generation including hydropower and wind-PV power, minus wind-PV power curtailment. The solutions obtained from the analytical method were compared with those derived from the numerical method. Finally, the sensitivity of the capacity expansion function was assessed using the Taylor series . A case study in the Yalong River basin in China reveals that: (1) the R-squared value of the proposed functions in estimating hydropower generation and power curtailment exceeds 0.9 between analytical and numerical solutions; (2) based on analytical and numerical methods , the optimal expanded sizes of HP are 1250 MW and 1210 MW, respectively; (3) electricity price is the most sensitive parameter, contributing approximately 11 times higher than operation and maintenance costs , and 2 times higher than multi-year discount rate for the optimal expanded size of hydropower plant. The proposed analytical method can be an effective tool for the capacity expansion and sensitivity analysis of existing hydropower plants without the need for simulation calculation.