IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v36y2011i11p6471-6478.html
   My bibliography  Save this article

A fundamental study on the optimal/near-optimal shape of a network for energy distribution

Author

Listed:
  • Xia, Liang
  • Chan, Ming-yin
  • Qu, Minglu
  • Xu, Xiangguo
  • Deng, Shiming

Abstract

Usually, a medium such as water and electrical current is transported through an energy distribution network for delivering energy. Driving the medium through the network will lead to power loss. In this paper, a fundamental study was conducted to find out the optimal/near-optimal shape of an energy distribution network for minimising the total power loss under the constraint of the network’s total flow volume. A general equation was developed for calculating the total power loss. A parameter was used to classify all media into three types. For Type I and II medium, it was found that a radial shape network was the optimal one. A global search method was proposed to find out the near-optimal shape of the network for Type III medium. A case study was carried out for finding out the optimal/near-optimal shape of the networks for electric current, refrigerant and water in laminar flow, respectively, with a supplier and six users. The power losses to drive these media through the networks with the optimal/near-optimal shape were compared to those through the networks with other shapes. The comparison results indicated the power losses could be reduced when using the optimal/near-optimal networks.

Suggested Citation

  • Xia, Liang & Chan, Ming-yin & Qu, Minglu & Xu, Xiangguo & Deng, Shiming, 2011. "A fundamental study on the optimal/near-optimal shape of a network for energy distribution," Energy, Elsevier, vol. 36(11), pages 6471-6478.
  • Handle: RePEc:eee:energy:v:36:y:2011:i:11:p:6471-6478
    DOI: 10.1016/j.energy.2011.09.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544211006177
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2011.09.020?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. Alandi, P. Planells & Alvarez, J.F. Ortega & Martin-Benito, J.M. Tarjuelo, 2007. "Optimization of irrigation water distribution networks, layout included," Agricultural Water Management, Elsevier, vol. 88(1-3), pages 110-118, March.
    2. Avella, Pasquale & Villacci, Domenico & Sforza, Antonio, 2005. "A Steiner arborescence model for the feeder reconfiguration in electric distribution networks," European Journal of Operational Research, Elsevier, vol. 164(2), pages 505-509, July.
    3. Lexin Li & R. Dennis Cook & Christopher J. Nachtsheim, 2005. "Model‐free variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 285-299, April.
    4. Jianwen Cai & Jianqing Fan & Runze Li & Haibo Zhou, 2005. "Variable selection for multivariate failure time data," Biometrika, Biometrika Trust, vol. 92(2), pages 303-316, June.
    5. Yildirim, Nurdan & Toksoy, Macit & Gokcen, Gulden, 2010. "Piping network design of geothermal district heating systems: Case study for a university campus," Energy, Elsevier, vol. 35(8), pages 3256-3262.
    Full references (including those not matched with items on IDEAS)

    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. Rossi, André & Aubry, Alexis & Jacomino, Mireille, 2012. "Connectivity-and-hop-constrained design of electricity distribution networks," European Journal of Operational Research, Elsevier, vol. 218(1), pages 48-57.
    2. 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.
    3. Heng-Hui Lue, 2015. "An Inverse-regression Method of Dependent Variable Transformation for Dimension Reduction with Non-linear Confounding," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 760-774, September.
    4. Guelpa, Elisa & Verda, Vittorio, 2019. "Compact physical model for simulation of thermal networks," Energy, Elsevier, vol. 175(C), pages 998-1008.
    5. Bilin Zeng & Xuerong Meggie Wen & Lixing Zhu, 2017. "A link-free sparse group variable selection method for single-index model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2388-2400, October.
    6. Joseph G. Ibrahim & Hongtu Zhu & Ramon I. Garcia & Ruixin Guo, 2011. "Fixed and Random Effects Selection in Mixed Effects Models," Biometrics, The International Biometric Society, vol. 67(2), pages 495-503, June.
    7. Zambom, Adriano Zanin & Akritas, Michael G., 2015. "Nonparametric significance testing and group variable selection," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 51-60.
    8. Luke A. Prendergast & Jodie A. Smith, 2010. "Influence Functions for Dimension Reduction Methods: An Example Influence Study of Principal Hessian Direction Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 588-611, December.
    9. Karla B. Freitas & Márcio S. Arantes & Claudio F. M. Toledo & Alexandre C. B. Delbem, 2020. "MIQP model and improvement heuristic for power loss minimization in distribution system with network reconfiguration," Journal of Heuristics, Springer, vol. 26(1), pages 59-81, February.
    10. Xin Cheng & Wenbin Lu & Mengling Liu, 2015. "Identification of homogeneous and heterogeneous variables in pooled cohort studies," Biometrics, The International Biometric Society, vol. 71(2), pages 397-403, June.
    11. Hür Bütün & Ivan Kantor & François Maréchal, 2019. "Incorporating Location Aspects in Process Integration Methodology," Energies, MDPI, vol. 12(17), pages 1-45, August.
    12. Xingwei Tong & Xin He & Liuquan Sun & Jianguo Sun, 2009. "Variable Selection for Panel Count Data via Non‐Concave Penalized Estimating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 620-635, December.
    13. Wang, Qin & Yin, Xiangrong, 2008. "A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4512-4520, May.
    14. Stegnar, Gašper & Staničić, D. & Česen, M. & Čižman, J. & Pestotnik, S. & Prestor, J. & Urbančič, A. & Merše, S., 2019. "A framework for assessing the technical and economic potential of shallow geothermal energy in individual and district heating systems: A case study of Slovenia," Energy, Elsevier, vol. 180(C), pages 405-420.
    15. Marco Pellegrini & Augusto Bianchini, 2018. "The Innovative Concept of Cold District Heating Networks: A Literature Review," Energies, MDPI, vol. 11(1), pages 1-16, January.
    16. Jing Qian & Seyedmehdi Payabvash & André Kemmling & Michael H. Lev & Lee H. Schwamm & Rebecca A. Betensky, 2014. "Variable selection and prediction using a nested, matched case-control study: Application to hospital acquired pneumonia in stroke patients," Biometrics, The International Biometric Society, vol. 70(1), pages 153-163, March.
    17. Liu, Jicai & Zhang, Riquan & Zhao, Weihua & Lv, Yazhao, 2015. "Variable selection in semiparametric hazard regression for multivariate survival data," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 26-40.
    18. Vizcaino González, José Federico & Lyra, Christiano & Usberti, Fábio Luiz, 2012. "A pseudo-polynomial algorithm for optimal capacitor placement on electric power distribution networks," European Journal of Operational Research, Elsevier, vol. 222(1), pages 149-156.
    19. Leeb, Hannes & Potscher, Benedikt M., 2008. "Sparse estimators and the oracle property, or the return of Hodges' estimator," Journal of Econometrics, Elsevier, vol. 142(1), pages 201-211, January.
    20. Kwang Woo Ahn & Anjishnu Banerjee & Natasha Sahr & Soyoung Kim, 2018. "Group and within-group variable selection for competing risks data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 407-424, July.

    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:36:y:2011:i:11:p:6471-6478. 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.