Probabilistic Estimation of Wind Generating Resources Based on the Spatiooral Penetration Scenarios for Power Grid Expansions

Gyeongmin Kim, Hunyoung Shin, Jin Hur

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The proportion of renewable energy generation is expanding worldwide with the goal of reducing greenhouse gas. According to the 8th Basic Plan for Long-term Electricity Supply and Demand in South Korea, South Korea reduces traditional energy generation such as nuclear and coal plants and achieves 20% (58.5GW) of renewable energy generation by 2030. Wind Generating Resources (WGRs) are affected by meteorological variables such as temperature, wind speed and wind direction. Specifically, WGRs have uncertainty and variability issues depending on temporal and spatial characteristics. In this paper, we propose the probabilistic estimation of wind generating resources based on the spatiotemporal penetration scenarios for power grid expansion. The data of WGRs are analyzed based on clustering method considering the spatiotemporal penetration scenarios, and the potential scenarios are estimated using Monte Carlo simulation by selecting a representative power distribution probability for each cluster. The proposed estimation model of WGRs will play a key role to develop the hedging strategies of investment decision on power grid expansion planning with high wind power penetrations.

Original languageEnglish
Article number9328250
Pages (from-to)15252-15258
Number of pages7
JournalIEEE Access
StatePublished - 2021

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korean Government through the Ministry of Science and ICT (MSIT) under Grant 2019R1F1A1061557.

Publisher Copyright:
© 2013 IEEE.


  • Monte Carlo simulation
  • Probabilistic model and estimation
  • power grid expansion
  • spatiotemporal penetration scenarios
  • wind generating resources


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