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Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo

Author

Listed:
  • Gregor Thiele

    (Fraunhofer Institute IPK for Production Systems and Design Technology, 10587 Berlin, Germany)

  • Theresa Johanni

    (Faculty for Electrical Engineering and Computer Science, Technical University of Berlin, 10623 Berlin, Germany)

  • David Sommer

    (Weierstrass Institute for Applied Analysis and Stochastics, 10117 Berlin, Germany)

  • Jörg Krüger

    (Institute for Machine Tools and Factory Management, Technical University of Berlin, 10587 Berlin, Germany)

Abstract

The operation of industrial supply technology is a broad field for optimization. Industrial cooling plants are often (a) composed of several components, (b) linked using network technology, (c) physically interconnected, and (d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization. An example containing a cooling tower, water circulations, and chillers entails a non-linear optimization problem with five dimensions. The decomposition of such a system allows the modeling of separate subsystems which can be structured according to the physical topology. An established method for energy performance indicators (EnPI) helps to formulate an optimization problem in a coherent way. The novel optimization algorithm OptTopo strives for efficient set-points by traversing a graph representation of the overall system. The advantages are (a) the ability to combine models of several types (e.g., neural networks and polynomials) and (b) an constant runtime independent from the number of operation points requested because new optimization needs just to be performed in case of plant model changes. An experimental implementation of the algorithm is validated using a simscape simulation. For a batch of five requests, OptTopo needs 61 min while the solvers Cobyla, SDPEN, and COUENNE need 0.3 min, 1.4 min, and 3.1 min, respectively. OptTopo achieves an efficiency improvement similar to that of established solvers. This paper demonstrates the general feasibility of the concept and fortifies further improvements to reduce computing time.

Suggested Citation

  • Gregor Thiele & Theresa Johanni & David Sommer & Jörg Krüger, 2022. "Decomposition of a Cooling Plant for Energy Efficiency Optimization Using OptTopo," Energies, MDPI, vol. 15(22), pages 1-16, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8387-:d:968159
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    References listed on IDEAS

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    1. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    2. Asad, Hussain Syed & Yuen, Richard Kwok Kit & Huang, Gongsheng, 2017. "Multiplexed real-time optimization of HVAC systems with enhanced control stability," Applied Energy, Elsevier, vol. 187(C), pages 640-651.
    3. Xuetao Wang & Qianchuan Zhao & Yifan Wang, 2020. "A Distributed Optimization Method for Energy Saving of Parallel-Connected Pumps in HVAC Systems," Energies, MDPI, vol. 13(15), pages 1-24, July.
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