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

Sun Oh Kim, Jin Hur

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish
Pages (from-to)1719-1726
Number of pages8
JournalRenewable Energy
Volume179
DOIs
StatePublished - Dec 2021

Keywords

  • Monte-carlo simulation
  • Network congestion
  • Probabilistic model
  • Security assessment
  • Wind generating resources

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