Learning to Solve Receding Horizon MINLP With Guaranteed Hard Constraints Using Neural Networks

Abstract

This letter proposes a learning method for real-time solution of receding-horizon mixed-integer nonlinear programs (RH MINLPs) in dynamic energy supply systems. Conventional MINLP solvers incur high computational costs. Although machine learning (ML) approximations can reduce the cost, existing approaches struggle to satisfy temporally coupled hard constraints solely through the forward pass, as required in industrial applications. To address this issue, we propose a two-stage architecture. First, RH MINLP is reformulated via backward recursion to decouple temporal dependencies into local allocation tasks. Second, a specialized neural network (NN) architecture embeds structural correction mechanisms into differentiable layers, thereby satisfying hard constraints during the forward pass without the need for external solvers. The proposed NN comprises a scalable binary-constraint layer and a continuous-allocation layer. A case study on a district cooling system demonstrates that the proposed method enables large-scale end-to-end training and generates feasible operational plans in real time.