To take full advantage of the complementary characteristics of various renewable energy sources, hybrid generation systems (HGSs) are used to accommodate the increased variability and uncertainty. In southwest China, there are many small cascade hydropower stations (CHSs) and PV power stations, which have spatial and temporal correlation characteristics and complementary characteristics. Pumped-storage units are considered as ideal large-scale energy storage elements for HGSs due to their fast response and long life. The purpose of this study is to increase the system reliability and water power utilization rate and maximize the economic benefits of a cascade hydro-PV-pumped storage (CH-PV-PS) generation system. Considering the reliability, economy, and water power utilization rate of the system, the CH-PV-PS system model with multiple objectives and multiple constraints is established. Then, a multi-objective stochastic numerical P system (MOSNP) is proposed. The external storage set and correction method in the MOSNP algorithm are introduced to ensure the diversity of the solution and improve the efficiency of the algorithm. The CH-PV-PS system is introduced in Sichuan Province, Southwest China. The simulation results show that (1) the MOSNP method can obtain robust and effective optimization results for the hybrid system; (2) the use of pumped storage units has increased the daily economy by 1018 CNY, and the total fluctuation of CHSs has been reduced by 29.3%, which makes the hybrid system safer and more economical; and (3) the uncertainty of PV and runoff will lead to frequent dispatching of CHSs, thus reducing the economic benefits of the system.

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High photovoltaic penetration in a power system has significantly challenged its safety and economic operation. To use the complementary characteristics of various renewable energy sources (RESs) fully, a novel hierarchical scheduling control (HSC) method is presented to accommodate the variability and uncertainty of a cascade hydro-PV-pumped storage (CH-PV-PS) generation system. Considering the optimization functions and execution requirements of the CH-PV-PS system, the HSC method is divided into two layers: the dynamic optimization layer and the static optimization layer. The static optimization layer focuses on the economy of the CH-PV-PS system, and the dynamic optimization layer focuses on the safety of the CH-PV-PS system. In the first layer, that is, the static optimization layer, the objectives of the day-ahead and hour-ahead schedules are established, and a heuristic algorithm is combined with a linear programming algorithm to optimize the energy allocation. Considering the uncertainty of the PV power output and hour-ahead load, a real-time schedule is established in the second layer; that is, in the second layer, the dynamic optimization layer, real-time scheduling and prediction of active output are established. Model predictive control methods are introduced to correct for prediction bias at different time scales in order to fully utilize the control capability of hydropower generation. A CH-PV-PS real-world system in Southwest China is chosen as a case study. In the three scenarios, where only PV fluctuations are considered, the simulation results reveal that, compared with the traditional open-loop optimized and hierarchical open-loop optimization methods, the HSC method reduces the average relative deviation of PV and increases the system economics. After a large amount of RESs are connected to the power grid, the HSC method provides a solution for improving the consumption of RESs.