Data-driven capacity planning of highway multi-energy systems under photovoltaic uncertainty using CVAE-enhanced distributionally robust optimization
Shishan Dong
Capacity planning in highway multi-energy systems faces significant challenges due to the high uncertainty of renewable energy generation and the limited availability of historical data during the planning stage. This paper proposes an integrated framework that combines photovoltaic (PV) scenario generation using a Conditional Variational Autoencoder (CVAE) with a Wasserstein-type distributionally robust optimization (DRO) model for capacity planning under renewable generation uncertainty. The C