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Optimal operation of heat supply systems with piping network

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  • Yokoyama, Ryohei
  • Kitano, Hiroyuki
  • Wakui, Tetsuya

Abstract

It is expected that energy saving may be attained by connecting heat source equipment and air conditioning equipment in multiple buildings with piping network and operating heat source equipment flexibly in consideration of heat demands required by air conditioning equipment. In this paper, an optimization method is proposed to operate such heat supply systems with piping network rationally. Mass flow rates and temperatures of water are adopted as basic variables to express heat flow rates as well as pressure and heat losses in piping segments. The discreteness for the selection of piping segments for water flow are also taken into account. To avoid treating the nonlinearity directly, mass flow rates are discretized, and the optimization problem is finally formulated as a mixed-integer linear programming one, and its suboptimal solution is derived efficiently by a two-stage approach. A case study is conducted for a heat supply system for space cooling and heating of an exhibition center with multiple buildings. Through the study, the validity and effectiveness of the proposed optimization method is shown in terms of solution optimality and computation time. In addition, it is shown how the primary energy consumption can be reduced using piping network.

Suggested Citation

  • Yokoyama, Ryohei & Kitano, Hiroyuki & Wakui, Tetsuya, 2017. "Optimal operation of heat supply systems with piping network," Energy, Elsevier, vol. 137(C), pages 888-897.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:888-897
    DOI: 10.1016/j.energy.2017.03.146
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    References listed on IDEAS

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    Cited by:

    1. Wang, Jiawei & Wang, Yi & Qiu, Dawei & Su, Hanguang & Strbac, Goran & Gao, Zhiwei, 2025. "Resilient energy management of a multi-energy building under low-temperature district heating: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 378(PA).
    2. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    3. Kuntuarova, Saltanat & Licklederer, Thomas & Huynh, Thanh & Zinsmeister, Daniel & Hamacher, Thomas & Perić, Vedran, 2024. "Design and simulation of district heating networks: A review of modeling approaches and tools," Energy, Elsevier, vol. 305(C).
    4. Muschick, D. & Zlabinger, S. & Moser, A. & Lichtenegger, K. & Gölles, M., 2022. "A multi-layer model of stratified thermal storage for MILP-based energy management systems," Applied Energy, Elsevier, vol. 314(C).

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