IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v137y2017icp888-897.html
   My bibliography  Save this article

Optimal operation of heat supply systems with piping network

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421730539X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.03.146?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    2. Sakawa, Masatoshi & Kato, Kosuke & Ushiro, Satoshi, 2002. "Operational planning of district heating and cooling plants through genetic algorithms for mixed 0-1 linear programming," European Journal of Operational Research, Elsevier, vol. 137(3), pages 677-687, March.
    3. Guelpa, Elisa & Toro, Claudia & Sciacovelli, Adriano & Melli, Roberto & Sciubba, Enrico & Verda, Vittorio, 2016. "Optimal operation of large district heating networks through fast fluid-dynamic simulation," Energy, Elsevier, vol. 102(C), pages 586-595.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Hai & Wang, Haiying & Haijian, Zhou & Zhu, Tong, 2017. "Optimization modeling for smart operation of multi-source district heating with distributed variable-speed pumps," Energy, Elsevier, vol. 138(C), pages 1247-1262.
    2. Stojiljković, Mirko M. & Ignjatović, Marko G. & Vučković, Goran D., 2015. "Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization," Energy, Elsevier, vol. 92(P3), pages 420-434.
    3. Andrea Menapace & Simone Santopietro & Rudy Gargano & Maurizio Righetti, 2021. "Stochastic Generation of District Heat Load," Energies, MDPI, vol. 14(17), pages 1-17, August.
    4. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Optimization design of BCHP system to maximize to save energy and reduce environmental impact," Energy, Elsevier, vol. 35(8), pages 3388-3398.
    5. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    6. Tian, Xueyu & You, Fengqi, 2019. "Carbon-neutral hybrid energy systems with deep water source cooling, biomass heating, and geothermal heat and power," Applied Energy, Elsevier, vol. 250(C), pages 413-432.
    7. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    8. Rostami, Borzou & Chassein, André & Hopf, Michael & Frey, Davide & Buchheim, Christoph & Malucelli, Federico & Goerigk, Marc, 2018. "The quadratic shortest path problem: complexity, approximability, and solution methods," European Journal of Operational Research, Elsevier, vol. 268(2), pages 473-485.
    9. Unai Aldasoro & María Merino & Gloria Pérez, 2019. "Time consistent expected mean-variance in multistage stochastic quadratic optimization: a model and a matheuristic," Annals of Operations Research, Springer, vol. 280(1), pages 151-187, September.
    10. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    11. Gupta, Renu & Bandopadhyaya, Lakshmisree & Puri, M. C., 1996. "Ranking in quadratic integer programming problems," European Journal of Operational Research, Elsevier, vol. 95(1), pages 231-236, November.
    12. Angel L. Cedeño & Reinier López Ahuar & José Rojas & Gonzalo Carvajal & César Silva & Juan C. Agüero, 2022. "Model Predictive Control for Photovoltaic Plants with Non-Ideal Energy Storage Using Mixed Integer Linear Programming," Energies, MDPI, vol. 15(17), pages 1-21, September.
    13. Osman, Hany & Demirli, Kudret, 2010. "A bilinear goal programming model and a modified Benders decomposition algorithm for supply chain reconfiguration and supplier selection," International Journal of Production Economics, Elsevier, vol. 124(1), pages 97-105, March.
    14. Guelpa, Elisa & Verda, Vittorio, 2019. "Compact physical model for simulation of thermal networks," Energy, Elsevier, vol. 175(C), pages 998-1008.
    15. Verbiest, Floor & Cornelissens, Trijntje & Springael, Johan, 2019. "A matheuristic approach for the design of multiproduct batch plants with parallel production lines," European Journal of Operational Research, Elsevier, vol. 273(3), pages 933-947.
    16. Fabio Furini & Emiliano Traversi, 2019. "Theoretical and computational study of several linearisation techniques for binary quadratic problems," Annals of Operations Research, Springer, vol. 279(1), pages 387-411, August.
    17. Biswas, Debajyoti & Alfandari, Laurent, 2022. "Designing an optimal sequence of non‐pharmaceutical interventions for controlling COVID-19," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1372-1391.
    18. Ricardo M. Lima & Ignacio E. Grossmann, 2017. "On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study," Computational Optimization and Applications, Springer, vol. 66(1), pages 1-37, January.
    19. Jih-Jeng Huang, 2016. "Resource decision making for vertical and horizontal integration problems in an enterprise," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(11), pages 1363-1372, November.
    20. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:137:y:2017:i:c:p:888-897. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.