본문 바로가기
KERD
논문KCI 등재

고해상도 규모상세화 수치자료 산출체계를 이용한 남한의 풍력기상자원 특성 분석

Analyses of the Meteorological Characteristics over South Korea for Wind Power Applications Using KMAPP

2

조회

0

다운로드

7

인용

0

참고문헌

키워드

주제분류대기과학

초록

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-meansquare error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

Abstract

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-meansquare error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

참고문헌 (30)

  1. A. Allouhi, "Evaluation of wind energy potential in Morocco's coastal regions", Renewable and Sustainable Energy Reviews, 72, pp.311~324 (2017), Elsevier BV[DOI]
  2. David Carvalho, "A sensitivity study of the WRF model in wind simulation for an area of high wind energy", Environmental Modelling & Software, 33, pp.23~34 (2012), Elsevier BV[DOI]
  3. 최다영, "현업 국지모델기반 2018년 여름철 기상 1호 특별 고층관측자료의 관측 민감도 실험", 대기, 30(1), pp.17~30 (2020), 한국기상학회[DOI]
  4. Theodore M. Giannaros, "Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece", Renewable Energy, 102, pp.190~198 (2017), Elsevier BV[DOI]
  5. María Gonçalves-Ageitos, "Modelling wind resources in climate change scenarios in complex terrains", Renewable Energy, 76, pp.670~678 (2015), Elsevier BV[DOI]
  6. Giovanni Gualtieri, "Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy", Renewable Energy, 36(3), pp.1081~1094 (2011), Elsevier BV[DOI]
  7. Global wind report 2017, "GWEC", Global Wind Energy Council, 72~ (2017)
  8. Quetzalcoatl Hernandez-Escobedo, "Wind Energy Assessment for Small Urban Communities in the Baja California Peninsula, Mexico", Energies, 9(10), pp.805~ (2016), MDPI AG[DOI]
  9. Howard, T., "Correction and downscaling of NWP wind speed forecasts", Meteor. Appl., 14, pp.105~116 (2007)
  10. 김도우, "한반도 바람자원의 시공간적 분포", 대기, 18(3), pp.171~182 (2008), 한국기상학회
  11. 김현구, "시계열 풍속벡터의 유사성을 이용한 포항지역 바람권역 분류", 한국태양에너지학회 논문집, 36(1), pp.11~18 (2016), 한국태양에너지학회
  12. 김현구, "제주도 풍력자원 데이터베이스 구축을 위한 기상통계분석", 한국환경과학회지, 17(6), pp.591~599 (2008), 한국환경과학회
  13. 김선영, "한반도 호우유형의 중규모 특성 및 예보 가이던스", 대기, 29(4), pp.463~480 (2019), 한국기상학회[DOI]
  14. 권순덕, "단기 관측을 통한 풍력에너지 평가", 한국풍공학회지, 12(3), pp.165~171 (2008), 한국풍공학회
  15. 이순환, "독립된 산악지형에 의하여 형성된 중규모 소용돌이의 발달에 관한 연구 -제주도 한라산을 대상으로-", Asia-Pacific Journal of Atmospheric Sciences, 40(2), pp.191~201 (2004), 한국기상학회
  16. Kyo-Sun Sunny Lim, "Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model", Meteorology and Atmospheric Physics, 131(4), pp.975~985 (2019), Springer Science and Business Media LLC[DOI]
  17. Junkai Liu, "Wind resource potential assessment using a long term tower measurement approach: A case study of Beijing in China", Journal of Cleaner Production, 174, pp.917~926 (2018), Elsevier BV[DOI]
  18. A.C. Martín Mederos, "An offshore wind atlas for the Canary Islands", Renewable and Sustainable Energy Reviews, 15(1), pp.612~620 (2011), Elsevier BV[DOI]
  19. 문서정, "풍력자원 조사를 위한 제주 북동부 연안역의 멱지수 분석", 대한공간정보학회지, 21(4), pp.65~71 (2013), 대한공간정보학회[DOI]
  20. Nikolai Nawri, "The wind energy potential of Iceland", Renewable Energy, 69, pp.290~299 (2014), Elsevier BV[DOI]
  21. Ki-Yong Oh, "An assessment of wind energy potential at the demonstration offshore wind farm in Korea", Energy, 46(1), pp.555~563 (2012), Elsevier BV[DOI]
  22. Oke, T. R., "Boundary layer climates", pp.435~ (1987), Psychology Press
  23. Ahmed Ouammi, "Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)", Renewable and Sustainable Energy Reviews, 14(7), pp.1959~1968 (2010), Elsevier BV[DOI]
  24. 류건화, "해수면 온도에 따른 연안지역 연직풍속전단 변화 연구", 한국방재학회논문집, 18(2), pp.505~514 (2018), 한국방재학회[DOI]
  25. 서범근, "풍력기상자원지도 산출방법에 따른 풍력자원 비교", 신재생에너지, 13(4), pp.23~38 (2017), 한국신·재생에너지학회
  26. 설동일, "우리나라 근해구역에 있어서의 월별 바람분포의 기후학적 특성", 해양환경안전학회지, 12(3), pp.185~192 (2006), 해양환경안전학회
  27. 심화남, "국지풍이 내륙의 안개발생에 미치는 영향", 대기, 27(2), pp.213~224 (2017), 한국기상학회[DOI]
  28. Ushiyama, I., "Wind energy", pp.74~ (2013), Ohmsha, Ltd. And Seong An Dang Publishing Co.
  29. World nuclear performance report 2018, "WNA", World Nuclear Association, 36~, pp.Rep. No. 2018 (2018), 004
  30. Yim, S. H. L., "Developing a high‐resolution wind map for a complex terrain with a coupled MM5", CALMET system, 2007, pp.112, D05106~

관련 논문AI추천

이 간행물의 다른 논문 (8건)