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Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study

Author

Listed:
  • Chi-Chun Lo

    (Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan
    Department of Engineering and Maintenance, Chang Gung Memorial Hospital, Kaosiung 83301, Taiwan
    These authors contributed equally to this work.)

  • Shang-Ho Tsai

    (Institute of Electrical and Control Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan
    These authors contributed equally to this work.)

  • Bor-Shyh Lin

    (Institute of Imaging and Biomedical Photonics, National Chiao Tung University, Tainan 71150, Taiwan)

Abstract

This paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR) is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO) algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the demand response of ice storage air-conditioning systems to demonstrate the optimization of its energy savings and operational efficiency and offering greater energy efficiency.

Suggested Citation

  • Chi-Chun Lo & Shang-Ho Tsai & Bor-Shyh Lin, 2016. "Ice Storage Air-Conditioning System Simulation with Dynamic Electricity Pricing: A Demand Response Study," Energies, MDPI, vol. 9(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:113-:d:64000
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    References listed on IDEAS

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    Cited by:

    1. Pei Cai & Youxue Jiang & He Wang & Liangyu Wu & Peng Cao & Yulong Zhang & Feng Yao, 2020. "Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    2. Ahmad Murtaza Ershad & Robert Pietzcker & Falko Ueckerdt & Gunnar Luderer, 2020. "Managing Power Demand from Air Conditioning Benefits Solar PV in India Scenarios for 2040," Energies, MDPI, vol. 13(9), pages 1-19, May.
    3. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    4. Hao, Ling & Wei, Mingshan & Xu, Fei & Yang, Xiaochen & Meng, Jia & Song, Panpan & Min, Yong, 2020. "Study of operation strategies for integrating ice-storage district cooling systems into power dispatch for large-scale hydropower utilization," Applied Energy, Elsevier, vol. 261(C).
    5. Qingshan Xu & Yifan Ding & Aixia Zheng, 2017. "An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability," Sustainability, MDPI, vol. 9(5), pages 1-20, May.

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