4.7 Article

Temperature control of a low-temperature district heating network with Model Predictive Control and Mixed-Integer Quadratically Constrained Programming

Journal

ENERGY
Volume 224, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.120140

Keywords

Low-temperature district heating; Software in the loop; Heat pump; Mixed-integer quadratically-constrained; programming; Model predictive control

Funding

  1. BMWi (German Federal Ministry of Economic Affairs and Energy) [03EGB0010A]
  2. Helmholtz Association of German Research Centers

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Lowering the operating temperatures of district heating networks is crucial to reduce energy losses and utilize low-temperature heat sources, such as waste heat. This paper focuses on modeling, control, and optimization of a low-temperature district heating network, with a case study on waste heat from high-performance computers. The optimization model presented in the study demonstrates energy savings of 1.55%-5.49% by controlling the operation temperatures of heat pumps and thermal energy storages.
District heating networks transport thermal energy from one or more sources to a plurality of con-sumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losses and unlock the potential of low-temperature heat sources, such as waste heat. With an increasing share of uncontrolled heat sources in district heating networks, control strategies to co-ordinate energy supply and network operation become more important. This paper focuses on the modeling, control, and optimization of a low-temperature district heating network, presenting a case study with a high share of waste heat from high-performance computers. The network consists of heat pumps with temperature-dependent characteristics. In this paper, quadratic correlations are used to model temperature characteristics. Thus, a mixed-integer quadratically-constrained program is pre-sented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. The network operation is optimized for three sample days. The presented optimization model uses the flexibility of the thermal energy storages and thermal inertia of the network by controlling its flow and return temperatures. The results show savings of electrical energy consumption of 1.55%e5.49%, depending on heat and cool demand. (c) 2021 Elsevier Ltd. All rights reserved.

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