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Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling

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  • Wenxian Zhao

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Zhang Deng

    (School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411100, China)

  • Yanfei Ji

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Chengcheng Song

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Yue Yuan

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Zhiyuan Wang

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Yixing Chen

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of Ministry of Education, Hunan University, Changsha 410082, China)

Abstract

Energy saving in buildings is essential as buildings’ operational energy use constitutes 30% of global energy consumption. Urban building energy modeling (UBEM) effectively understands urban energy consumption. This paper applied UBEM to assess the potential of peak demand reduction and energy saving in a mixed-use community, using 955 residential buildings, 35 office buildings and 7 hotels in Shenzhen, China, as a case study. The building type and period were collected based on the GIS dataset. Then, the baseline models were generated by the UBEM tool—AutoBPS. Five scenarios were analyzed: retrofit-window, retrofit-air conditioner (AC), retrofit-lighting, rooftop photovoltaic (PV), and demand response. The five scenarios replaced the windows, enhanced the AC, upgraded the lighting, covered 60% of the roof area with PV, and had a temperature reset from 17:00 to 23:00, respectively. The results show that using retrofit-windows is the most effective scenario for reducing peak demand at 19.09%, and PV reduces energy use intensity (EUI) best at 29.96%. Demand response is recommended when further investment is not desired. Retrofit-lighting is suggested for its low-cost, low-risk investment, with the payback period (PBP) not exceeding 4.54 years. When the investment is abundant, retrofit-windows are recommended for public buildings, while PV is recommended for residential buildings. The research might provide practical insights into energy policy formulation.

Suggested Citation

  • Wenxian Zhao & Zhang Deng & Yanfei Ji & Chengcheng Song & Yue Yuan & Zhiyuan Wang & Yixing Chen, 2024. "Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling," Energies, MDPI, vol. 17(5), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1214-:d:1350573
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    References listed on IDEAS

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    1. Amin, Amin & Kem, Oudom & Gallegos, Pablo & Chervet, Philipp & Ksontini, Feirouz & Mourshed, Monjur, 2022. "Demand response in buildings: Unlocking energy flexibility through district-level electro-thermal simulation," Applied Energy, Elsevier, vol. 305(C).
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    3. Mikkola, Jani & Lund, Peter D., 2014. "Models for generating place and time dependent urban energy demand profiles," Applied Energy, Elsevier, vol. 130(C), pages 256-264.
    4. Triolo, Ryan C. & Rajagopal, Ram & Wolak, Frank A. & de Chalendar, Jacques A., 2023. "Estimating cooling demand flexibility in a district energy system using temperature set point changes from selected buildings," Applied Energy, Elsevier, vol. 336(C).
    5. Yanfei Ji & Guangchen Li & Fanghan Su & Yixing Chen & Rongpeng Zhang, 2023. "Retrofit Analysis of City-Scale Residential Buildings in the Hot Summer and Cold Winter Climate Zone," Energies, MDPI, vol. 16(17), pages 1-19, August.
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    Cited by:

    1. Ahmad Walid Ayoobi & Mehmet Inceoğlu, 2024. "Developing an Optimized Energy-Efficient Sustainable Building Design Model in an Arid and Semi-Arid Region: A Genetic Algorithm Approach," Energies, MDPI, vol. 17(23), pages 1-31, December.

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