Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition

TL;DR

This work tackles the challenge of efficiently operating thermo-hydraulic heating/cooling systems modeled by control volumes, where scalable model predictive control (MPC) is essential for real-time performance. It introduces an automated framework with time discretization and employs primal decomposition to exploit system structure, improving scalability over conventional MPC. Through validation on an underground heating system with varying state counts, the primal-decomposition MPC demonstrates substantial speed advantages while maintaining competitive costs, and exhibits price-responsive control that can minimize operating expenses. The findings suggest that scalable PD-based MPC can enable practical, real-time optimization for decarbonized building energy systems using control-volume models.

Abstract

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.

Figures (2)

• Figure 4: Comparison of the Dymola (dashed lines) and the Matlab (dots) simulations for the system depicted in Figure \ref{['fig:CVpitch']}
• Figure 5: Results for the primal MPC for the system displayed in Figure \ref{['fig:CVpitch']}