A scenario-based evaluation framework for renewable capacity scale and photovoltaic capacity ratio in a hydro–wind–photovoltaic–pumped-storage hybrid energy system

Renewable-capacity planning in hydro–wind–photovoltaic–pumped-storage hybrid energy systems is jointly affected by wind–photovoltaic output, inflow, renewable capacity, and photovoltaic capacity ratio, yet their coupled effects on operation and capacity preference remain unclear. This study develops a scenario-based resource–operation–capacity evaluation framework for a cascade hydropower–wind–photovoltaic–pumped-storage system. Five cumulative-probability levels of wind–photovoltaic joint output and five hydrological exceedance-frequency levels form 25 combined scenarios. Normalized historical recurrence weights are derived through similar-year intersections from 26 assigned historical years. In each scenario, 210 fixed combinations of renewable capacity and photovoltaic capacity ratio are evaluated using a medium- and short-term operational model solved by the non-dominated sorting genetic algorithm II (NSGA-II). A two-level decision method identifies the highest-scoring configuration in each scenario and then aggregates configuration scores across all scenarios using recurrence weights. Application to the clean-energy base in the middle reaches of the Yalong River Basin shows that inflow dominates operational-performance variation by affecting hydropower availability and the generation-plan level. Wind–photovoltaic resource conditions alter the balance between renewable output and flexible regulation, whereas the photovoltaic capacity ratio changes joint-output stability and the regulation required from cascade hydropower and pumped storage. Increasing renewable capacity raises transmitted energy, but excessive expansion increases curtailment and load-loss risks. The scenario-dependent highest-scoring configurations span 9000–12000 MW and photovoltaic capacity ratios of 35 %–55 %. Cross-scenario weighting identifies 9000 MW and a 40 % photovoltaic capacity ratio as the probability-weighted optimal configuration under the adopted settings. The framework links representative resource conditions, system operation, and capacity decisions for hydro-dominated renewable-energy planning.