Long-term optimization scheduling of cascade hydropower stations with wind and photovoltaic energy integration

Abstract This study models temporal correlations of hydro-wind-solar resources using LHS-Cholesky decomposition, generating scenarios reduced via probabilistic distance for computational efficiency. A dual-objective stochastic optimization framework for the long-term multi-energy complementary system scheduling (LMCS) problem maximizes long-term power generation while guaranteeing minimum periodic output. The proposed orthogonal design and multi-population search framework-based evolutionary (OMPE) algorithm integrates orthogonal design, multi-population search, and constraint handling, achieving 5.63% and 4.41% improvements in total and minimum outputs versus benchmarks. Validation confirms orthogonal initialization, recombination operators, and dominance rules enhance performance, while scenario quantity regulates economic-risk tradeoffs.