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An innovative approach for demand side management—optimal chiller loading by simulated annealing

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  • Chang, Yung-Chung

Abstract

This investigation attempts to solve the optimal chiller loading (OCL) problem by utilizing simulated annealing (SA). SA eliminates the limitation that the Lagrangian method cannot solve OCL because the kW-PLR curves simultaneously include convex functions and non-convex functions. This study fully addresses the load balance constraint and chiller operating limits. The case study analysis demonstrates that this method solves the Lagrangian problem and generates highly accurate results. The proposed approach can be applied perfectly to the operation of air conditioning systems.

Suggested Citation

  • Chang, Yung-Chung, 2006. "An innovative approach for demand side management—optimal chiller loading by simulated annealing," Energy, Elsevier, vol. 31(12), pages 1883-1896.
    • Handle: RePEc:eee:energy:v:31:y:2006:i:12:p:1883-1896
    DOI: 10.1016/j.energy.2005.10.018
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    References listed on IDEAS

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    1. Hu, S.-C. & Chuah, Y.K., 2003. "Power consumption of semiconductor fabs in Taiwan," Energy, Elsevier, vol. 28(8), pages 895-907.
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    Cited by:

    1. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    2. Chang-Ming Lin & Chun-Yin Wu & Ko-Ying Tseng & Chih-Chiang Ku & Sheng-Fuu Lin, 2019. "Applying Two-Stage Differential Evolution for Energy Saving in Optimal Chiller Loading," Energies, MDPI, vol. 12(4), pages 1-12, February.
    3. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
    4. Kuo, Cheng-Chien, 2009. "Reactive energy scheduling using bi-objective programming with modified particle swarm optimization," Energy, Elsevier, vol. 34(6), pages 804-815.
    5. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    6. 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.
    7. 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).
    8. Wang, Yijun & Jin, Xinqiao & Shi, Wantao & Wang, Jiangqing, 2019. "Online chiller loading strategy based on the near-optimal performance map for energy conservation," Applied Energy, Elsevier, vol. 238(C), pages 1444-1451.
    9. 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.
    10. 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.

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