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Economic dispatch of chiller plant by gradient method for saving energy

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  • Chang, Yung-Chung
  • Chan, Tien-Shun
  • Lee, Wen-Shing

Abstract

This study employs gradient method (GM) to solve economic dispatch of chiller plant (EDCP) problem. GM overcomes the flaw that with the Lagrangian multiplier (LM) method the system may not converge at low demand. In this study, the load balance constraint and the operating limit constraints of the chillers are fully accounted for. After analysis and comparison of the two cases studies, we are confident to say that this method not only solves the problem of convergence, but also produces results with high accuracy within a rapid timeframe. It can be perfectly applied to the operation of air-conditioning systems.

Suggested Citation

  • Chang, Yung-Chung & Chan, Tien-Shun & Lee, Wen-Shing, 2010. "Economic dispatch of chiller plant by gradient method for saving energy," Applied Energy, Elsevier, vol. 87(4), pages 1096-1101, April.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:4:p:1096-1101
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    References listed on IDEAS

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    1. Lee, Wen-Shing & Chen, Yi -Ting & Wu, Ting-Hau, 2009. "Optimization for ice-storage air-conditioning system using particle swarm algorithm," Applied Energy, Elsevier, vol. 86(9), pages 1589-1595, September.
    2. Hu, S.-C. & Chuah, Y.K., 2003. "Power consumption of semiconductor fabs in Taiwan," Energy, Elsevier, vol. 28(8), pages 895-907.
    3. Yu, F.W. & Chan, K.T., 2008. "Optimization of water-cooled chiller system with load-based speed control," Applied Energy, Elsevier, vol. 85(10), pages 931-950, October.
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    2. Tirmizi, Syed A. & Gandhidasan, P. & Zubair, Syed M., 2012. "Performance analysis of a chilled water system with various pumping schemes," Applied Energy, Elsevier, vol. 100(C), pages 238-248.
    3. Deng, Na & Cai, Rongchang & Gao, Yuan & Zhou, Zhihua & He, Guansong & Liu, Dongyi & Zhang, Awen, 2017. "A MINLP model of optimal scheduling for a district heating and cooling system: A case study of an energy station in Tianjin," Energy, Elsevier, vol. 141(C), pages 1750-1763.
    4. Federica Acerbi & Mirco Rampazzo & Giuseppe De Nicolao, 2020. "An Exact Algorithm for the Optimal Chiller Loading Problem and Its Application to the Optimal Chiller Sequencing Problem," Energies, MDPI, vol. 13(23), pages 1-29, December.
    5. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    6. Ding, Yan & Wang, Qiaochu & Kong, Xiangfei & Yang, Kun, 2019. "Multi-objective optimisation approach for campus energy plant operation based on building heating load scenarios," Applied Energy, Elsevier, vol. 250(C), pages 1600-1617.
    7. Min-Yong Qi & Jun-Qing Li & Yu-Yan Han & Jin-Xin Dong, 2020. "Optimal Chiller Loading for Energy Conservation Using an Improved Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 13(15), pages 1-18, July.
    8. Li, Ze & Guo, Junfei & Gao, Xinyu & Yang, Xiaohu & He, Ya-Ling, 2023. "A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers," Applied Energy, Elsevier, vol. 349(C).
    9. 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).
    10. Ron-Hendrik Peesel & Florian Schlosser & Henning Meschede & Heiko Dunkelberg & Timothy G. Walmsley, 2019. "Optimization of Cooling Utility System with Continuous Self-Learning Performance Models," Energies, MDPI, vol. 12(10), pages 1-17, May.
    11. Coelho, Leandro dos Santos & Klein, Carlos Eduardo & Sabat, Samrat L. & Mariani, Viviana Cocco, 2014. "Optimal chiller loading for energy conservation using a new differential cuckoo search approach," Energy, Elsevier, vol. 75(C), pages 237-243.
    12. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.

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