Probabilistic modeling of wind energy potential for power grid expansion planning

Gyeongmin Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish
Article number120831
StatePublished - 1 Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd


  • Capacity factor
  • Grid expansion planning
  • Monte Carlo simulation
  • Probabilistic model
  • Weibull distribution
  • Wind energy potential


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