IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i9p10504-10521d56183.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/9/10504/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/9/10504/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shirazi, Ali & Najafi, Behzad & Aminyavari, Mehdi & Rinaldi, Fabio & Taylor, Robert A., 2014. "Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling," Energy, Elsevier, vol. 69(C), pages 212-226.
    2. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    3. 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.
    4. Chang, Yung-Chung & Chen, Wu-Hsing, 2009. "Optimal chilled water temperature calculation of multiple chiller systems using Hopfield neural network for saving energy," Energy, Elsevier, vol. 34(4), pages 448-456.
    5. Powell, Kody M. & Cole, Wesley J. & Ekarika, Udememfon F. & Edgar, Thomas F., 2013. "Optimal chiller loading in a district cooling system with thermal energy storage," Energy, Elsevier, vol. 50(C), pages 445-453.
    6. 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.
    7. Chan, Apple L.S. & Chow, Tin-Tai & Fong, Square K.F. & Lin, John Z., 2006. "Performance evaluation of district cooling plant with ice storage," Energy, Elsevier, vol. 31(14), pages 2750-2762.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. She, Xiaohui & Cong, Lin & Nie, Binjian & Leng, Guanghui & Peng, Hao & Chen, Yi & Zhang, Xiaosong & Wen, Tao & Yang, Hongxing & Luo, Yimo, 2018. "Energy-efficient and -economic technologies for air conditioning with vapor compression refrigeration: A comprehensive review," Applied Energy, Elsevier, vol. 232(C), pages 157-186.
    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.
    5. Yuridiana Rocio Galindo-Luna & Efraín Gómez-Arias & Rosenberg J. Romero & Eduardo Venegas-Reyes & Moisés Montiel-González & Helene Emmi Karin Unland-Weiss & Pedro Pacheco-Hernández & Antonio González-, 2018. "Hybrid Solar-Geothermal Energy Absorption Air-Conditioning System Operating with NaOH-H 2 O—Las Tres Vírgenes (Baja California Sur), “La Reforma” Case," Energies, MDPI, vol. 11(5), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    2. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    3. Powell, Kody M. & Kim, Jong Suk & Cole, Wesley J. & Kapoor, Kriti & Mojica, Jose L. & Hedengren, John D. & Edgar, Thomas F., 2016. "Thermal energy storage to minimize cost and improve efficiency of a polygeneration district energy system in a real-time electricity market," Energy, Elsevier, vol. 113(C), pages 52-63.
    4. 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.
    5. Guelpa, Elisa & Verda, Vittorio, 2019. "Thermal energy storage in district heating and cooling systems: A review," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Ebrahimi, Mahyar, 2020. "Storing electricity as thermal energy at community level for demand side management," Energy, Elsevier, vol. 193(C).
    7. Neri, Manfredi & Guelpa, Elisa & Verda, Vittorio, 2022. "Design and connection optimization of a district cooling network: Mixed integer programming and heuristic approach," Applied Energy, Elsevier, vol. 306(PA).
    8. Luerssen, Christoph & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2020. "Life cycle cost analysis (LCCA) of PV-powered cooling systems with thermal energy and battery storage for off-grid applications," Applied Energy, Elsevier, vol. 273(C).
    9. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    10. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    11. Luerssen, Christoph & Gandhi, Oktoviano & Reindl, Thomas & Sekhar, Chandra & Cheong, David, 2019. "Levelised Cost of Storage (LCOS) for solar-PV-powered cooling in the tropics," Applied Energy, Elsevier, vol. 242(C), pages 640-654.
    12. Yani Bao & Wai Ling Lee & Jie Jia, 2018. "Exergy Analyses and Modelling of a Novel Extra-Low Temperature Dedicated Outdoor Air System," Energies, MDPI, vol. 11(5), pages 1-25, May.
    13. 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.
    14. 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.
    15. She, Xiaohui & Cong, Lin & Nie, Binjian & Leng, Guanghui & Peng, Hao & Chen, Yi & Zhang, Xiaosong & Wen, Tao & Yang, Hongxing & Luo, Yimo, 2018. "Energy-efficient and -economic technologies for air conditioning with vapor compression refrigeration: A comprehensive review," Applied Energy, Elsevier, vol. 232(C), pages 157-186.
    16. Parameshwaran, R. & Kalaiselvam, S. & Harikrishnan, S. & Elayaperumal, A., 2012. "Sustainable thermal energy storage technologies for buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2394-2433.
    17. Yasemin Merzifonluoglu & Eray Uzgoren, 2018. "Photovoltaic power plant design considering multiple uncertainties and risk," Annals of Operations Research, Springer, vol. 262(1), pages 153-184, March.
    18. Chen, Long Xiang & Xie, Mei Na & Zhao, Pan Pan & Wang, Feng Xiang & Hu, Peng & Wang, Dong Xiang, 2018. "A novel isobaric adiabatic compressed air energy storage (IA-CAES) system on the base of volatile fluid," Applied Energy, Elsevier, vol. 210(C), pages 198-210.
    19. Miguel J. Prieto & Juan Á. Martínez & Rogelio Peón & Lourdes Á. Barcia & Fernando Nuño, 2017. "On the Convenience of Using Simulation Models to Optimize the Control Strategy of Molten-Salt Heat Storage Systems in Solar Thermal Power Plants," Energies, MDPI, vol. 10(7), pages 1-17, July.
    20. Wang, Longyi & Wu, Mei & Sun, Xiao & Gan, Zhihua, 2016. "A cascade pulse tube cooler capable of energy recovery," Applied Energy, Elsevier, vol. 164(C), pages 572-578.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:10504-10521:d:56183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.