Short-term forecasting-based optimal scheduling of a pumped storage hydropower, wind, and photovoltaic complementary system

Introduction As clean energy penetration grows, short-term coordination of pumped storage hydropower plants (PSHPs), wind power and photovoltaic (PV) generation is increasingly important for secure and economical dispatch under renewable variability. Methods This study proposes a short-term forecasting-based scheduling and benefit allocation method for a PSHP-wind-PV complementary system. A hierarchical prediction and dynamic correction (HPDC) strategy improves 24-h forecasts of wind power, PV power and load. The corrected forecasts are embedded in a scheduling model with operational constraints, and a dynamic load fitness-based (DLF) method quantifies time-varying marginal contributions for benefit allocation. Results Compared with ARIMA, HPDC reduces the MAE by 6.2% for wind power, 16.5% for PV power and 9.4% for load demand. The scheduling results show that PSHP peak-valley regulation smooths net load and stabilizes thermal output, reducing thermal fuel costs by about 5%-10% in hydro-wind-PV operation. Discussion The proposed forecasting-scheduling-allocation framework improves short-term operational robustness and provides a practical basis for fair benefit sharing among entities in complementary renewable energy systems.