Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition
For engineers designing control systems for heating and cooling, this provides a scalable MPC generation method, though it is an incremental improvement over existing decomposition techniques.
This work presents an automated framework for generating model predictive controllers for thermo-hydraulic systems, using primal decomposition to ensure scalability. Validation on an underground heating system shows the decomposition's advantage in handling varying numbers of states.
Decarbonizing the global energy supply requires more efficient heating and cooling systems. Model predictive control enhances the operation of cooling and heating systems but depends on accurate system models, often based on control volumes. We present an automated framework including time discretization to generate model predictive controllers for such models. To ensure scalability, a primal decomposition exploiting the model structure is applied. The approach is validated on an underground heating system with varying numbers of states, demonstrating the primal decomposition's advantage regarding scalability.