Optimal dynamic thermal plant control: A study and benchmark

TL;DR

This paper explores the use of continuous optimization for district heating network control, achieving significant energy and cost savings, and demonstrates its practical applicability on a real Swiss network.

Contribution

It introduces a continuous optimization approach for DHN control, showing improved efficiency and cost savings over traditional methods, with practical implementation details.

Findings

Optimal control achieves ~8% energy savings.

Dynamic pricing reduces operational costs by ~12%.

Method runs in under 5 minutes on standard hardware.

Abstract

District heating networks play a vital role in thermal energy supply in many countries. Thus, it comes to no surprise that these has been a central role in improving energy efficiency for private and public energy suppliers alike around the globe. Many studies have previously investigated the potential of energy saving by low temperature operation of the DHN and the integration of renewable energies. Many other studies consider this problem in terms of mixed integer lin-ear programming. Here, we instead investigate the utilization of well-established continuous optimization methods to improve DHN operation efficiency. We demonstrate that optimal control is able to model low temperature operation of a DHN for savings of around 8%, but can even further improve its operation when considering dynamic energy pricing, reducing the cost of operation by roughly 12%. We demonstrate the applicability of this method in a realistic, openly available network in Switzerland (OpenDHN), with a total runtime of less than 5 minutes on a standard desktop com-puter per experiment.