Stochastic Model Predictive Control under AC Power-Flow Constraints Using Generative Learning
TL;DR
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.
Abstract
This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes reliable feasibility difficult to achieve because one dispatch trajectory must simultaneously satisfy nonlinear constraints across all scenarios and time periods. To address this limitation, a conditional stochastic neural generator (CSNG) is developed to produce multiple candidate dispatch trajectories for each uncertainty instance, enabling the recovery of a feasible and economical solution through candidate selection. A feasibility-aware self-supervised distribution-shaping scheme is further introduced to promote constraint satisfaction, candidate diversity, and operating economy without requiring computationally expensive SMPC solution labels, while mitigating candidate collapse in generative ACOPF learning. To support efficient end-to-end training, a constraint-aware differentiable architecture is introduced. It employs a projection mechanism to exactly enforce box and ramping constraints while preserving informative gradients near active bounds, together with a differentiable equality-completion surrogate for efficient AC power-flow reconstruction. Case studies on the IEEE 14- and 118-bus systems demonstrate $100\%$ feasibility, optimality gaps below $2\%$, and computational efficiency suitable for intraday dispatch. The implementation is publicly available at https://github.com/JieZhu6/Generative_SMPC.