Stochastic Model Predictive Control under AC Power-Flow Constraints Using Generative Learning
An end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control problems under nonlinear AC power-flow constraints is proposed, and a constraint-aware differentiable architecture is introduced.