A Nested Cross Decomposition Algorithm for Power System Capacity Expansion with Multiscale Uncertainties

Modern electric power systems have witnessed rapidly increasing penetration of renewable energy, storage, electrical vehicles, and various demand response resources. The infrastructure planning is thus facing more challenges as a result the variability uncertainties arising from diverse new This study aims to develop multistage multiscale stochastic mixed integer programming (MM-SMIP) model capture both coarse-temporal-scale uncertainties, such investment cost long-run stochasticity, fine-temporal-scale hourly energy output electricity for system capacity expansion problem. To be applied real system, resulting will lead extremely large-scale problems, which suffer not only well-known curse dimensionality but also computational difficulties with vast number variables at each stage. In addressing associated MM-SMIP model, we propose nested cross decomposition algorithm that consists two layers decomposition—that is, Dantzig–Wolfe L-shaped decomposition. exhibits promising performance under our numerical especially amenable parallel computing, demonstrated through results.