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A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation

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  • Kim, Gyeongmin
  • Hur, Jin

Abstract

Renewable energy resources have garnered considerable attention owing to concerns regarding climate change mitigation and sustainability. The performance of renewable energy resources varies based on weather conditions, which is an important consideration in power system planning as renewable energy penetration increases. In this study, a probabilistic approach for the potential estimation of renewable energy resources based on augmented spatial interpolation was proposed. The proposed algorithm was verified using empirical data obtained from wind farms in Jeju Island. Wind power output scenarios were modeled through ordinary kriging and Monte Carlo simulations. Moreover, the point and cause of line overload according to the seasonal wind power output and power demand were analyzed through transmission security analysis, and the frequency and scale of the curtailments were estimated. This can be used to estimate potential renewable energy resources and establish a power system operation plan. Further study includes the development of stable power system operation plans for large-scale renewable energy resource-integrated power systems.

Suggested Citation

  • Kim, Gyeongmin & Hur, Jin, 2023. "A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation," Energy, Elsevier, vol. 263(PA).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222024689
    DOI: 10.1016/j.energy.2022.125582
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    1. Chen, Xin & Ye, Xiaoling & Xiong, Xiong & Zhang, Yingchao & Li, Yuanlu, 2024. "Improving the accuracy of wind speed spatial interpolation: A pre-processing algorithm for wind speed dynamic time warping interpolation," Energy, Elsevier, vol. 295(C).
    2. Yoo, Yeuntae & Jung, Seungmin, 2025. "Correlation-segregated joint pdf for stochastic assessment of synthetic inertia in wind turbines," Renewable Energy, Elsevier, vol. 253(C).
    3. Wenshuai Bai & Dian Wang & Zhongquan Miao & Xiaorong Sun & Jiabin Yu & Jiping Xu & Yuqing Pan, 2023. "The Design and Application of Microgrid Supervisory System for Commercial Buildings Considering Dynamic Converter Efficiency," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    4. Costinela Fortea & Marius Sorin Dinca & Romeo Victor Ionescu & Monica Laura Zlati & Valentin-Marian Antohi, 2023. "Analyzing the Socio-Economic and Energy Implications of Green Economy Transition Across Six EU Member States," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 680-693.
    5. Yang, YeHa & Yang, SoYoung & Moon, HyungBin & Woo, JongRoul, 2024. "Analyzing heterogeneous electric vehicle charging preferences for strategic time-of-use tariff design and infrastructure development: A latent class approach," Applied Energy, Elsevier, vol. 374(C).

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