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Cost Analysis Method for Estimating Dynamic Reserve Considering Uncertainties in Supply and Demand

Author

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  • Kyung-bin Kwon

    (Electrical Engineering, Korea Military Academy (KMA), Seoul 01850, Korea)

  • Hyeongon Park

    (Department of Statistics Institute of Engineering Research, Seoul National University, Seoul 08826, Korea)

  • Jae-Kun Lyu

    (Korean Electric Power Corporation (KEPCO), Naju 58217, Korea)

  • Jong-Keun Park

    (Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

The use of appropriate hourly reserve margins can maintain power system security by balancing supply and demand in the presence of errors in the forecast demand, generation outages, or errors in the forecast of wind power generation. Because the cost of unit commitment increases with larger reserve margins, cost analysis to determine the most economical reserve margin is an important issue in power system operation. Here, we define the “short-term reliability of balance” and describe a method to determine the reserve margin based on the short-term reliability of balance. We describe a case study, in which we calculate the reserve margin using this method with various standards of short-term reliability of balance. A cost analysis is then performed to determine the most economic standard, and a comparison between our method and a conventional method is carried out. The results show that our method with an economic short-term reliability of balance enables more reliable and efficient operation of the power system. Moreover, with an hourly reserve margin, we show that an increase in wind power generation can result in a significant decrease in the operating cost, which makes wind power generation economically viable.

Suggested Citation

  • Kyung-bin Kwon & Hyeongon Park & Jae-Kun Lyu & Jong-Keun Park, 2016. "Cost Analysis Method for Estimating Dynamic Reserve Considering Uncertainties in Supply and Demand," Energies, MDPI, vol. 9(10), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:10:p:845-:d:80949
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    References listed on IDEAS

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    4. Jianhua Chen & Wenchuan Wu & Boming Zhang & Bin Wang & Qinglai Guo, 2013. "A Spinning Reserve Allocation Method for Power Generation Dispatch Accommodating Large-Scale Wind Power Integration," Energies, MDPI, vol. 6(10), pages 1-25, October.
    5. Georgopoulou, Chariklia A. & Giannakoglou, Kyriakos C., 2010. "Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages," Applied Energy, Elsevier, vol. 87(5), pages 1782-1792, May.
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    Cited by:

    1. Kwon, Kyung-bin & Kim, Dam, 2020. "Enhanced method for considering energy storage systems as ancillary service resources in stochastic unit commitment," Energy, Elsevier, vol. 213(C).
    2. Gang Wang & Dahai You & Zhe Zhang & Li Dai & Qi Zou & Hengwei Liu, 2018. "Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power," Energies, MDPI, vol. 11(2), pages 1-20, February.
    3. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    4. Rishang Long & Jianhua Zhang, 2016. "Risk Assessment Method of UHV AC/DC Power System under Serious Disasters," Energies, MDPI, vol. 10(1), pages 1-13, December.

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