풍력단지 발전량 예측에 대한 재해석 자료 ERA5의 현장적응
Site Adaptation of the Reanalysis Data ERA5 on the Power Prediction of Wind Farms
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초록
Owing to the continuously improving spatial resolution and accuracy of the reanalysis data, a site adaptation case study was performed for the prediction of wind farm power output using ERA5, the 5th generation reanalysis data. The wind speed of the reanalysis data was substituted into the performance curve of the wind turbine by altitude and topographical speed up/down correction using the power law for maximizing the correlation between the predicted and actual power record. Cluster analysis was conducted to classify the wind farms into five groups, and representative onshore, inland, mountain wind farms were selected for case analysis from each cluster. Via the site adaptation of 41 wind farms in South Korea, the hourly, daily cumulative, and monthly cumulative correlation coefficients of the power output were calculated, which were 0.68, 0.79, and 0.85, respectively. In future, machine learning will be introduced for site adaptation in conjunction with the downscaling of wind resource maps by numerical weather prediction or computational fluid dynamics.
Abstract
Owing to the continuously improving spatial resolution and accuracy of the reanalysis data, a site adaptation case study was performed for the prediction of wind farm power output using ERA5, the 5th generation reanalysis data. The wind speed of the reanalysis data was substituted into the performance curve of the wind turbine by altitude and topographical speed up/down correction using the power law for maximizing the correlation between the predicted and actual power record. Cluster analysis was conducted to classify the wind farms into five groups, and representative onshore, inland, mountain wind farms were selected for case analysis from each cluster. Via the site adaptation of 41 wind farms in South Korea, the hourly, daily cumulative, and monthly cumulative correlation coefficients of the power output were calculated, which were 0.68, 0.79, and 0.85, respectively. In future, machine learning will be introduced for site adaptation in conjunction with the downscaling of wind resource maps by numerical weather prediction or computational fluid dynamics.
참고문헌 (16)
- Carlos M. Fernández-Peruchena, "Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure", Remote Sensing, 12(13), pp.2127~ (2020), MDPI AG[DOI]
- J. Polo, "Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets", Solar Energy, 132, pp.25~37 (2016), Elsevier BV[DOI]
- Hans Hersbach, "The ERA5 global reanalysis", Quarterly Journal of the Royal Meteorological Society, 146(730), pp.1999~2049 (2020), Wiley[DOI]
- Bénédicte Jourdier, "Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France", Advances in Science and Research, 17, pp.63~77 (2020), Copernicus GmbH[DOI]
- Giovanni Gualtieri, "Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers", Energies, 14(14), pp.4169~ (2021), MDPI AG[DOI]
- 김현구, "재해석자료를 이용한 한반도 해상의 기준풍속 추정", 신재생에너지, 17(4), pp.1~8 (2021), 한국신·재생에너지학회[DOI]
- Bengtsson, L., "FGGE 4-dimensional Data Assimilation at ECMWF", Bulletin of the American Meteorological Society, 63, pp.29~43 (1982)
- ECMWF Reanalysis Project Report 1, "Gibson, J. K.", ECMWF, ~, pp.ERA-15 Description (Version 2) (1999)
- S. M. Uppala, "The ERA-40 re-analysis", Quarterly Journal of the Royal Meteorological Society, 131(612), pp.2961~3012 (2005), Wiley[DOI]
- D. P. Dee, "The ERA-Interim reanalysis: configuration and performance of the data assimilation system", Quarterly Journal of the Royal Meteorological Society, 137(656), pp.553~597 (2011), Wiley[DOI]
- 변종기, "육· 해상 풍력자원평가를 위한 ERA-Interim 재해석 데이터의 적용", 한국태양에너지학회 논문집, 37(2), pp.1~11 (2017), 한국태양에너지학회[DOI]
- 김현구, "시계열 풍속벡터의 유사성을 이용한 포항지역 바람권역 분류", 한국태양에너지학회 논문집, 36(1), pp.11~18 (2016), 한국태양에너지학회
- 김진영, "다양한 유사성 척도를 이용한 바람권역의 군집분석", 한국풍공학회지, 25(3), pp.111~117 (2021), 한국풍공학회
- Hyun-Goo Kim, "Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement", Energies, 14(8), pp.2319~ (2021), MDPI AG[DOI]
- 우재균, "AWS 풍황데이터를 이용한 강원풍력발전단지 연간에너지발전량 예측", 한국태양에너지학회 논문집, 31(2), pp.72~81 (2011), 한국태양에너지학회
- Myeongchan Oh, "Machine learning‐based statistical downscaling of wind resource maps using multi‐resolution topographical data", Wind Energy, 25(6), pp.1121~1141 (2022), Wiley[DOI]