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Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption

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  • Wen-Shing Lee

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 106344, Taiwan
    Research Center of Energy Conservation for New Generation of Residential, Commercial, and Industrial Sectors, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Wen-Hsin Lin

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Chin-Chi Cheng

    (Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

  • Chien-Yu Lin

    (Section of Refrigeration, Taipei Municipal Nangang Vocational High School, Taipei 106344, Taiwan)

Abstract

Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem.

Suggested Citation

  • Wen-Shing Lee & Wen-Hsin Lin & Chin-Chi Cheng & Chien-Yu Lin, 2021. "Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption," Energies, MDPI, vol. 14(21), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7066-:d:667335
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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