IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v299y2021ics0306261921005997.html
   My bibliography  Save this article

Overall chilled water system energy consumption modeling and optimization

Author

Listed:
  • Trautman, Neal
  • Razban, Ali
  • Chen, Jie

Abstract

The emergence of increasingly affordable variable-speed drive technology has changed the approach used to control chilled water systems equipped with these drives. The purpose of this research was to develop an integrated chilled water modeling technique that can determine the optimal system setpoints and estimate the energy saving potential of chiller system. The chiller system equipped with Variable Frequency Drives (VFDs) on cooling tower fans and condenser water pumps. To accomplish the objective, physical component models of the centrifugal chiller, cooling tower and condenser water pump were established with the goal of incorporating the system’s condenser water flow rate and cooling tower fan speeds as optimization variables. Furthermore, a cooling load prediction algorithm was developed using a multiple non-linear regression model to approximate the building’s cooling load subject to a range of environmental conditions. The inputs and outputs of the individual component models were linked to estimate how adjusting the cooling tower fan and condenser water pump speed would influence the system’s comprehensive performance. The overall system model was then optimized using a generalized reduced gradient optimization algorithm to determine the potential energy savings through speed control with VFDs and to ascertain a control logic strategy for the building automation system to operate the heating and cooling system. A case-study was performed on a single chiller system at a museum and the model was calibrated according to logged data collected over four months. Results showed that for the system analyzed, the energy saving of optimizing the cooling tower fan system was found to be 12–15%, while the energy saving potential of optimizing the condenser water pump with the cooling tower fan was negligible. Additionally, comparing different cooling tower fan control strategies showed that a wet-bulb approach-based cooling tower control strategy was shown to have the highest correlation to the optimized fan speed with an R2 of 0.924.

Suggested Citation

  • Trautman, Neal & Razban, Ali & Chen, Jie, 2021. "Overall chilled water system energy consumption modeling and optimization," Applied Energy, Elsevier, vol. 299(C).
    • Handle: RePEc:eee:appene:v:299:y:2021:i:c:s0306261921005997
    DOI: 10.1016/j.apenergy.2021.117166
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921005997
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117166?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. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
    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. Wang, Peng & Sun, Junqing & Yoon, Sungmin & Zhao, Liang & Liang, Ruobing, 2024. "A global optimization method for data center air conditioning water systems based on predictive optimization control," Energy, Elsevier, vol. 295(C).
    2. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    3. Olszewski, Pawel, 2022. "Experimental analysis of ON/OFF and variable speed drive controlled industrial chiller towards energy efficient operation," Applied Energy, Elsevier, vol. 309(C).
    4. Lian, Kuang-Yow & Hong, Yong-Jie & Chang, Che-Wei & Su, Yu-Wei, 2022. "A novel data-driven optimal chiller loading regulator based on backward modeling approach," Applied Energy, Elsevier, vol. 327(C).
    5. Liu, Xuefeng & Xu, Jinman & Bi, Mengbo & Ma, Wenjing & Chen, Wencong & Zheng, Minglong, 2024. "Multivariate coupled full-case physical model of large chilled water systems and its application," Energy, Elsevier, vol. 298(C).
    6. Shunian Qiu & Zhenhai Li & Delong Wang & Zhengwei Li & Yinying Tao, 2022. "Active Optimization of Chilled Water Pump Running Number: Engineering Practice Validation," Sustainability, MDPI, vol. 15(1), pages 1-12, December.

    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. Guillaume Sagnol & Edouard Pauwels, 2019. "An unexpected connection between Bayes A-optimal designs and the group lasso," Statistical Papers, Springer, vol. 60(2), pages 565-584, April.
    2. Abdelfettah Laouzai & Rachid Ouafi, 2022. "A prediction model for atmospheric pollution reduction from urban traffic," Environment and Planning B, , vol. 49(2), pages 566-584, February.
    3. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    4. Francesco Rinaldi & Damiano Zeffiro, 2023. "Avoiding bad steps in Frank-Wolfe variants," Computational Optimization and Applications, Springer, vol. 84(1), pages 225-264, January.
    5. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    6. Friesz, Terry L. & Tourreilles, Francisco A. & Han, Anthony Fu-Wha, 1979. "Multi-Criteria Optimization Methods in Transport Project Evaluation: The Case of Rural Roads in Developing Countries," Transportation Research Forum Proceedings 1970s 318817, Transportation Research Forum.
    7. Fabiana R. Oliveira & Orizon P. Ferreira & Gilson N. Silva, 2019. "Newton’s method with feasible inexact projections for solving constrained generalized equations," Computational Optimization and Applications, Springer, vol. 72(1), pages 159-177, January.
    8. Ali Fattahi & Sriram Dasu & Reza Ahmadi, 2019. "Mass Customization and “Forecasting Options’ Penetration Rates Problem”," Operations Research, INFORMS, vol. 67(4), pages 1120-1134, July.
    9. Bo Jiang & Tianyi Lin & Shiqian Ma & Shuzhong Zhang, 2019. "Structured nonconvex and nonsmooth optimization: algorithms and iteration complexity analysis," Computational Optimization and Applications, Springer, vol. 72(1), pages 115-157, January.
    10. James Chok & Geoffrey M. Vasil, 2023. "Convex optimization over a probability simplex," Papers 2305.09046, arXiv.org.
    11. A. de Palma & Y. Nesterov, 2001. "Stationary Dynamic Solutions in Congested Transportation Networks: Summary and Perspectives," THEMA Working Papers 2001-19, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    12. D. J. White, 1993. "A parametric‐based heuristic program for the quadratic assignment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(4), pages 553-568, June.
    13. Kang, Seong-Cheol & Lee, Hoyoung, 2019. "Economic appraisal of implementing electric vehicle taxis in Seoul," Research in Transportation Economics, Elsevier, vol. 73(C), pages 45-52.
    14. Michael W. Levin & Melissa Duell & S. Travis Waller, 2020. "Arrival Time Reliability in Strategic User Equilibrium," Networks and Spatial Economics, Springer, vol. 20(3), pages 803-831, September.
    15. Yong Xia & Zi Xu, 2010. "An efficient Lagrangian smoothing heuristic for Max-Cut," Indian Journal of Pure and Applied Mathematics, Springer, vol. 41(5), pages 683-700, October.
    16. Arezou Rahimi & Luis A. Vale-Silva & Maria Fälth Savitski & Jovan Tanevski & Julio Saez-Rodriguez, 2024. "DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    17. P. B. Assunção & O. P. Ferreira & L. F. Prudente, 2021. "Conditional gradient method for multiobjective optimization," Computational Optimization and Applications, Springer, vol. 78(3), pages 741-768, April.
    18. Ya-Feng Liu & Xin Liu & Shiqian Ma, 2019. "On the Nonergodic Convergence Rate of an Inexact Augmented Lagrangian Framework for Composite Convex Programming," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 632-650, May.
    19. Waddell, Paul & Boeing, Geoff & Gardner, Max & Porter, Emily, 2018. "An Integrated Pipeline Architecture for Modeling Urban Land Use, Travel Demand, and Traffic Assignment," SocArXiv 74zaw, Center for Open Science.
    20. Butyn, Emerson & Karas, Elizabeth W. & de Oliveira, Welington, 2022. "A derivative-free trust-region algorithm with copula-based models for probability maximization problems," European Journal of Operational Research, Elsevier, vol. 298(1), pages 59-75.

    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:appene:v:299:y:2021:i:c:s0306261921005997. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.