Multi-Scale Scenario Generation for Large Hydro–Wind–Solar Systems Using Clustering and Vine Copulas

Large-scale hydro–wind–solar systems require scenario sets that represent cross-resource dependence without losing station-level and intra-day characteristics. A remaining challenge is to connect cluster-level joint modeling with station allocation under sum constraints and hourly reconstruction consistent with daily totals. This study develops an integrated framework: wind and photovoltaic plants are grouped by output correlation; season-specific C-vine copulas model daily wind–solar–runoff dependence; a centered log-ratio transformation and Gaussian R-vine copula allocate cluster totals to stations; and a stratified typical-day library reconstructs hourly profiles. The framework is evaluated using 872 observed days of hourly data from a grid-connected system in Southwest China comprising 53 wind plants, 47 photovoltaic plants, and one hydropower-station runoff series; 1000 joint scenarios are generated. Against independent marginal sampling, Spearman correlation-matrix errors fell from 0.419–0.432 to 0.318–0.395 (8.7–24.2%), and Kendall errors from 0.306–0.325 to 0.236–0.298 (8.3–22.8%). Compared with historical-share resampling, station allocations were more diverse but less faithful to historical share distributions; compared with a unified typical-curve library, intra-day autocorrelation errors decreased across all resource-season cases. The resulting multi-scale scenarios can serve as generation-side inputs for stochastic scheduling and operational risk analysis.