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Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems

Author

Listed:
  • Whei-Min Lin

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80724, Taiwan)

  • Chia-Sheng Tu

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80724, Taiwan)

  • Ming-Tang Tsai

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 83342, Taiwan)

  • Chi-Chun Lo

    (Department of Engineering and Maintenance, ChangCung Memorial Hospital, Kaohsiung 83341, Taiwan)

Abstract

This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chilled water, the return temperature of ice storage water, and the supply temperature of ice storage water, are measured. The least-squares regression (LSR) is used to obtain the input-output (I/O) curve for the cooling load and power consumption of chillers and ice storage tank. The objective is to minimize overall cost in a daily schedule while satisfying all constraints, including cooling loading under the time-of-use (TOU) rate. Based on the Radial Basis Function Network (RBFN) and Ant Colony Optimization, an Ant-Based Radial Basis Function Network (ARBFN) is constructed in the searching process. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the economic dispatch of ice storage air-conditioning systems, and offering greater energy efficiency in dispatching chillers.

Suggested Citation

  • Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai & Chi-Chun Lo, 2015. "Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems," Energies, MDPI, vol. 8(9), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:10504-10521:d:56183
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    References listed on IDEAS

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    Cited by:

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    2. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
    3. Ching-Jui Tien & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2022. "Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems," Energies, MDPI, vol. 15(11), pages 1-13, May.
    4. Ghasem Ansari & Reza Keypour, 2023. "Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory," Sustainability, MDPI, vol. 15(7), pages 1-31, April.
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