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Application of cool storage air-conditioning in the commercial sector: an integrated resource planning approach for power capacity expansion planning and emission reduction

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  • Limmeechokchai, B.
  • Chungpaibulpatana, S.

Abstract

An assessment is presented of the evaluation of the application of cool storage air-conditioning (CSA) in the commercial sector as a resource in the electricity generation expansion planning. The resultant impacts of analysis of emission costs on annual emissions from power generation are also discussed. A building energy simulation tool is used for assessment of potential savings and peak load shifting of CSA application in commercial buildings. In this study, an integrated resource planning (IRP) model is used to evaluate the economic effectiveness of the CSA option. The IRP analysis with emission costs results in deferring the installation of four units of 1000-MW coal-fired power plant from 2010 to 2011, and one unit of 1000-MW coal-fired power plant and one 200-MW CSA option are removed from the IRP plan. Results show that the CSA option is a viable resource in the least-cost planning and reducing environmental emissions.

Suggested Citation

  • Limmeechokchai, B. & Chungpaibulpatana, S., 2001. "Application of cool storage air-conditioning in the commercial sector: an integrated resource planning approach for power capacity expansion planning and emission reduction," Applied Energy, Elsevier, vol. 68(3), pages 289-300, March.
    • Handle: RePEc:eee:appene:v:68:y:2001:i:3:p:289-300
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    References listed on IDEAS

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    1. Chirarattananon, Surapong & Limmeechokchai, Bundit, 1994. "A new building energy-efficiency law in Thailand: Impact on new buildings," Energy, Elsevier, vol. 19(2), pages 269-278.
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    Cited by:

    1. Sadeghi, Hadi & Rashidinejad, Masoud & Abdollahi, Amir, 2017. "A comprehensive sequential review study through the generation expansion planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1369-1394.
    2. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(3), pages 535-551, April.
    3. Hu, Qing & Huang, Guohe & Cai, Yanpeng & Huang, Ying, 2011. "Feasibility-based inexact fuzzy programming for electric power generation systems planning under dual uncertainties," Applied Energy, Elsevier, vol. 88(12), pages 4642-4654.
    4. Summerbell, Daniel L. & Khripko, Diana & Barlow, Claire & Hesselbach, Jens, 2017. "Cost and carbon reductions from industrial demand-side management: Study of potential savings at a cement plant," Applied Energy, Elsevier, vol. 197(C), pages 100-113.
    5. Zhang, X.Y. & Huang, G.H. & Zhu, H. & Li, Y.P., 2017. "A fuzzy-stochastic power system planning model: Reflection of dual objectives and dual uncertainties," Energy, Elsevier, vol. 123(C), pages 664-676.
    6. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2016. "An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings," Applied Energy, Elsevier, vol. 173(C), pages 406-417.
    7. Arteconi, A. & Hewitt, N.J. & Polonara, F., 2012. "State of the art of thermal storage for demand-side management," Applied Energy, Elsevier, vol. 93(C), pages 371-389.
    8. Ashok, S. & Banerjee, R., 2003. "Optimal cool storage capacity for load management," Energy, Elsevier, vol. 28(2), pages 115-126.
    9. Walmsley, Timothy Gordon & Philipp, Matthias & Picón-Núñez, Martín & Meschede, Henning & Taylor, Matthew Thomas & Schlosser, Florian & Atkins, Martin John, 2023. "Hybrid renewable energy utility systems for industrial sites: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    10. Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Tan, Q., 2009. "Identification of optimal strategies for energy management systems planning under multiple uncertainties," Applied Energy, Elsevier, vol. 86(4), pages 480-495, April.

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