# A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components

## TL;DR

This paper conducts a comprehensive year-long simulation comparing nonlinear mixed-integer MPC with traditional control for a complex thermal energy system, demonstrating MPC's efficiency and ability to discover new operational modes.

## Contribution

It introduces a whole-year simulation framework for nonlinear mixed-integer MPC applied to a complex thermal energy system, including a moving horizon estimation for improved accuracy.

## Key findings

- MPC significantly reduces yearly energy consumption.
- MPC maintains similar constraint satisfaction as conventional control.
- MPC autonomously identifies beneficial operation modes.

## Abstract

This work presents a whole-year simulation study on nonlinear mixed-integer Model Predictive Control (MPC) for a complex thermal energy supply system which consists of a heat pump, stratified water storages, free cooling facilities, and a large underground thermal storage. For solution of the arising Mixed-Integer Non-Linear Programs (MINLPs) we apply an existing general and optimal-control-suitable decomposition approach. To compensate deviation of forecast inputs from measured disturbances, we introduce a moving horizon estimation step within the MPC strategy. The MPC performance for this study, which consists of more than 50,000 real time suitable MINLP solutions, is compared to an elaborate conventional control strategy for the system. It is shown that MPC can significantly reduce the yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previously unknown, beneficial operation modes.