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Probabilistic power output model of wind generating resources for network congestion management

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  • Kim, SunOh
  • Hur, Jin

Abstract

With the growing importance of renewable energy generating resources around the world, the government is working towards expanding the share of renewable energy to 20% of total power generation considering the limitation of coal-fired and nuclear generation by 2030 in South Korea. However, the growing number of wind generating resources being connected to the electrical power system presents a challenge for transmission grid planning to increase the penetration of wind power while maintaining high levels of reliability and security of the electrical power system. In this paper, we propose a probabilistic power output model of wind generating resources for network congestion management. We use the historical data from the wind farms located on Jeju Island in South Korea to fit the Weibull distribution and implement Monte Carlo simulations. The simulation results, which represent network congestion with probabilistic values, are applied to the empirically modeled power grid of Jeju Island and a steady-state security evaluation is performed. The proposed probabilistic approach will be a key role to reduce the risk of over-investment in power transmission facilities compared to the deterministic approach to develop the generation mix scenarios with high wind power penetrations.

Suggested Citation

  • Kim, SunOh & Hur, Jin, 2021. "Probabilistic power output model of wind generating resources for network congestion management," Renewable Energy, Elsevier, vol. 179(C), pages 1719-1726.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1719-1726
    DOI: 10.1016/j.renene.2021.08.014
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    References listed on IDEAS

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    1. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    2. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
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    Cited by:

    1. Jaehyun Yoo & Yongju Son & Myungseok Yoon & Sungyun Choi, 2023. "A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors," Sustainability, MDPI, vol. 15(23), pages 1-15, December.
    2. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
    3. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.
    4. Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.

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