Peak operation optimization of cascade hydropower reservoirs and solar power plants considering output forecasting uncertainty

With the rapid development and utilization of hydropower and photovoltaic energy, the complementary operation of hydropower and solar power has become one of the research hotspots in the electric power system. However, the uncertainty of photovoltaic energy output significantly increases the difficulty of the complementary operation. This study focuses on the peak operation optimization of cascade hydropower reservoirs and solar power plants considering output forecasting uncertainty. Firstly, through principal component analysis and pattern clustering, the forecasting errors of historical dataset are used to generate typical solar output scenarios. Secondly, an efficient cooperation search algorithm is used to minimize the variance of the residual load series, taking into account the occurrence probabilities of solar output scenarios. Finally, the maximum value, peak-valley difference, standard deviation and load rate of the residual load are selected as the performance evaluation indexes. The peak load regulation ability of the proposed method is tested under different typical solar output scenarios. The results demonstrate that the proposed method can scientifically find the scheduling plan of hydropower-solar complementary system, reducing the photovoltaic energy integration risk.