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Probabilistic modeling of wind energy potential for power grid expansion planning

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  • Kim, Gyeongmin
  • Hur, Jin

Abstract

The increasing integration of wind energy generation into energy systems has led to difficulties in power flow calculations due to uncertainty and variability, which significantly affect the stability and reliability of power grids. Consequently, it is critical to evaluate the energy security limit of power grids based on probabilistic approaches. In this paper, we propose the probabilistic modeling of wind energy potential for power grid expansion planning. The proposed probabilistic model estimates wind energy potential through Weibull distribution, Monte Carlo Simulation (MCS), and the enhanced spatial modeling based on universal kriging. It can be used to establish the expansion plan for transmission and distribution facilities to resolve the variability and uncertainty issues of wind generation resources, which is expected to increase the penetration levels of renewable energy. To validate the proposed model, the empirical data from the Jeju Island's wind farms are considered in South Korea.

Suggested Citation

  • Kim, Gyeongmin & Hur, Jin, 2021. "Probabilistic modeling of wind energy potential for power grid expansion planning," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221010793
    DOI: 10.1016/j.energy.2021.120831
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    3. Lins, Davi Ribeiro & Guedes, Kevin Santos & Pitombeira-Neto, Anselmo Ramalho & Rocha, Paulo Alexandre Costa & de Andrade, Carla Freitas, 2023. "Comparison of the performance of different wind speed distribution models applied to onshore and offshore wind speed data in the Northeast Brazil," Energy, Elsevier, vol. 278(PA).
    4. Zhong, Mingwei & Xu, Cancheng & Xian, Zikang & He, Guanglin & Zhai, Yanpeng & Zhou, Yongwang & Fan, Jingmin, 2024. "DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting," Energy, Elsevier, vol. 286(C).
    5. Sun, X.Y. & Zhong, X.H. & Zhang, M.Y. & Zhou, T., 2022. "Experimental investigation on a novel wind-to-heat system with high efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    6. 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.
    7. Huang, Nantian & Zhao, Xuanyuan & Guo, Yu & Cai, Guowei & Wang, Rijun, 2023. "Distribution network expansion planning considering a distributed hydrogen-thermal storage system based on photovoltaic development of the Whole County of China," Energy, Elsevier, vol. 278(C).

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