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A Decision-Making Model for Optimized Energy Plans for Buildings Considering Peak Demand Charge—A South Korea Case Study

Author

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  • Jinho Shin

    (Korea Electric Power Research Institute (KEPRI), Korea Electric Power Corporation (KEPCO), 105, Munji-ro, Yuseong-gu, Daejeon 34056, Korea)

  • Jihwa Jung

    (Korea Electric Power Research Institute (KEPRI), Korea Electric Power Corporation (KEPCO), 105, Munji-ro, Yuseong-gu, Daejeon 34056, Korea)

  • Jaehaeng Heo

    (Raonfriends Corp., Corporate R & D Center, 66, Beolmal-ro, Dongan-gu, Anyang-si 14058, Korea)

  • Junwoo Noh

    (Raonfriends Corp., Corporate R & D Center, 66, Beolmal-ro, Dongan-gu, Anyang-si 14058, Korea)

Abstract

The energy industry has been trying to reduce the use of fossil fuels that emit carbon and to proliferate renewable energy as a way to respond to climate change. The attempts to reduce carbon emissions resulting from the process of generating the electric and thermal energy needed by a building were bolstered with the introduction of the concept of nZEB (nearly zero-energy building). In line with such initiatives, the South Korean government made it mandatory for new buildings to have an nZEB certificate as a way to promote the supply of renewable energy. The criteria for Energy Independence Rate, which is one of the nZEB certification criteria in South Korea, is to maintain the share of renewable energy as at least 20% of the primary energy sources for the building. For a new building in South Korea to have an nZEB certificate, it is required to establish an energy plan that would allow the building to meet the Energy Independence requirement. This optimally reflects the cost of installation for renewable energy facilities and the cost of purchasing energy from external sources, such as the national grid or district heating companies. In South Korea, the base retail rate of energy is calculated based on the peak demand per hour over the year, rather than the contracted energy. This has produced difficulties in standardizing the process with a mathematical model; in addition, there have not been many preceding studies that could be used as a reference. In this regard, this paper analyzed a modeling strategy for developing a realistic yet optimized energy plan in consideration of the unique conditions of the retail energy rates of South Korea, and analyzed the impact of the rates based on peak demands upon the total energy plan. In this study, our research team analyzed the electric billing system, conducted a case study, and analyzed the impact of the billing system that is based on the peak demand upon the optimal cost. By utilizing the restrictions for reaching the 20% Energy Independence goal, this paper calculated the proper energy supply facility capacity for renewable energy. Then, the cases in which the maximum demand modeling was used and the cases without one were compared to confirm the cost benefits observable when the suggested model is added or implemented.

Suggested Citation

  • Jinho Shin & Jihwa Jung & Jaehaeng Heo & Junwoo Noh, 2022. "A Decision-Making Model for Optimized Energy Plans for Buildings Considering Peak Demand Charge—A South Korea Case Study," Energies, MDPI, vol. 15(15), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5628-:d:879127
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    References listed on IDEAS

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    1. Yeweon Kim & Ki-Hyung Yu, 2020. "Study on the Certification Policy of Zero-Energy Buildings in Korea," Sustainability, MDPI, vol. 12(12), pages 1-11, June.
    2. Moon, Jongwoo & Jung, Tae Yong, 2020. "A critical review of Korea's long-term contract for renewable energy auctions: The relationship between the import price of liquefied natural gas and system marginal price," Utilities Policy, Elsevier, vol. 67(C).
    3. Liu, Jia & Cao, Sunliang & Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2021. "Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage," Applied Energy, Elsevier, vol. 281(C).
    4. Savolainen, Rebecka & Lahdelma, Risto, 2022. "Optimization of renewable energy for buildings with energy storages and 15-minute power balance," Energy, Elsevier, vol. 243(C).
    5. Lee, Byoung-Hoon & Ahn, Hyeon-Hyo, 2006. "Electricity industry restructuring revisited: the case of Korea," Energy Policy, Elsevier, vol. 34(10), pages 1115-1126, July.
    6. Iijima, Fuyumi & Ikeda, Shintaro & Nagai, Tatsuo, 2022. "Automated computational design method for energy systems in buildings using capacity and operation optimization," Applied Energy, Elsevier, vol. 306(PA).
    7. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
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