An analytical method for identifying power curtailment in hydro–wind–photovoltaic hybrid energy systems

Accurate estimation of power curtailment is essential for hydro-wind-photovoltaic hybrid energy systems. However, current numerical methods for estimating power curtailment require high temporal resolution data and suffer from heavy computational burdens and low interpretability. To address this issue, an analytical method is proposed to estimate power curtailment when the total power exceeds the load demand or the power transmission capacity by using two approaches. One is based on the idea of the soil moisture storage capacity curve in the Xinanjiang hydrological model, and the other uses linear regression depending on daily wind-photovoltaic power and load demand. The derived power curtailment function is then validated against the numerical method. Finally, sensitivity analyses are conducted to identify dominant parameters influencing power curtailment. A case study in the China's Yalong River basin indicates that, compared with the numerical method, the analytical method can accurately capture the power curtailment variations, achieving the average Nash-Sutcliffe efficiency above 0.83. Furthermore, with the power curtailment function, power generation calculation errors can be reduced by 16.2 million kWh (from 0.30 % to 0.17 %) in long-term operation, compared with the conventional method. In addition, sensitivity analysis results show that the average power curtailment is 7.1 % decrease per 1 % increase of the power transmission capacity (ranging from 3300 to 4620 MW). This study facilitates power curtailment estimation in HWPESs without high temporal resolution data.