Model Predictive Control of Thermo-Hydraulic Systems Using Primal Decomposition

TL;DR

This paper introduces an automated, scalable model predictive control framework for thermo-hydraulic systems, validated on underground heating models, emphasizing improved efficiency and scalability.

Contribution

It presents a novel automated framework with primal decomposition for scalable model predictive control of thermo-hydraulic systems.

Findings

Validated on underground heating system with varying states

Primal decomposition improves scalability

Framework automates model predictive controller generation

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.