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Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response

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  • Lee, Junghun
  • Yoo, Seunghwan
  • Kim, Jonghun
  • Song, Doosam
  • Jeong, Hakgeun

Abstract

Electricity demands are steadily increasing every year because of continued improvements to the quality of life and extreme hot and cold weather conditions. Therefore, the electric demand response management (DRM) system was introduced to prevent unstable electricity supply both domestically and globally. Unlike power generation in power plants, DRM regulates the demand and supply by reducing building energy consumption. Demand management is divided into energy efficiency and demand response. Energy efficiency reduces normal energy consumption by replacing older equipment and materials with high-efficiency models, remodeling the building envelope, and efficient system operation. Demand response reduces the electric consumption of pre-contracted electrical consumers at certain times, especially at peak load times. To determine the energy savings of buildings, the customer baseline load (CBL) is used. However, the CBL cannot evaluate the energy savings due to the energy efficiency improvements because it only assesses savings based on normal energy consumption. Therefore, DRM has a high incentive for buildings with high-energy consumption, while buildings with implemented energy efficiencies have low incentives, even though electricity demand is reduced. In this paper, we present the standard energy consumption to reflect both energy efficiency and demand response which can help stabilize power supply in the nation.

Suggested Citation

  • Lee, Junghun & Yoo, Seunghwan & Kim, Jonghun & Song, Doosam & Jeong, Hakgeun, 2018. "Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response," Energy, Elsevier, vol. 144(C), pages 1052-1063.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:1052-1063
    DOI: 10.1016/j.energy.2017.12.044
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    References listed on IDEAS

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    Cited by:

    1. Lin, Jin & Dong, Jun & Liu, Dongran & Zhang, Yaoyu & Ma, Tongtao, 2022. "From peak shedding to low-carbon transitions: Customer psychological factors in demand response," Energy, Elsevier, vol. 238(PA).
    2. Mohammad Reza Mansouri & Mohsen Simab & Bahman Bahmani Firouzi, 2021. "Impact of Demand Response on Reliability Enhancement in Distribution Networks," Sustainability, MDPI, vol. 13(23), pages 1-35, November.
    3. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    4. Tao, Peng & Xu, Fei & Dong, Zengbo & Zhang, Chao & Peng, Xuefeng & Zhao, Junpeng & Li, Kangping & Wang, Fei, 2022. "Graph convolutional network-based aggregated demand response baseline load estimation," Energy, Elsevier, vol. 251(C).
    5. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    6. Venkat Durvasulu & Timothy M. Hansen, 2018. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets," Energies, MDPI, vol. 11(12), pages 1-21, December.

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