대한공간정보학회지 (Journal of Korean Society for Geospatial Information Science)

  • 제25권1호
  • /
  • Pages.3-8
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  • 2017
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  • 1598-2955(pISSN)
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  • 2287-6693(eISSN)
대한공간정보학회 (Korea Spatial Information Society)
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기후변화를 고려한 한반도 미래 풍력자원 지도 생산

Production of Future Wind Resource Map under Climate Change over Korea

  • 김진영 (한국에너지기술연구원 신재생에너지자원센터) ;
  • 김도용 (목포대학교 환경공학과(기후변화연구소))
  • Kim, Jin Young (New and Renewable Energy Data Center, Korea Institute of Energy Research) ;
  • Kim, Do Yong (Department of Environmental Engineering, Mokpo National University)
  • 투고 : 2016.08.16
  • 심사 : 2017.01.09
  • 발행 : 2017.03.31
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초록

본 연구에서는 앙상블 중규모기후모델 weather research and forecasting(WRF)를 이용하여 2045년부터 2054년까지 21세기 중반의 기후변화에 대한 우리나라 미래 풍력자원 지도를 제작하였고 월별, 시간대별 자원변화를 검토하였다. 분석결과, 한반도상에서 강한 몬순 순환으로 인해 뚜렷한 월별 시공간 변동성이 해륙풍에 의한 시간대별 변동성보다 컸다. 풍력자원이 큰 강풍지역은 월마다 지역마다 다르게 나타났다. 즉 겨울철 북서계절풍(여름철 남서계절풍)이 주풍일 때 각각 강원산간과 해상 그리고 남서해안에서 자원이 많을 것으로 전망되었다. 최대풍과 최소풍은 1월, 9월에 각각 나타날 것으로 전망되었고, 시간대별로 내륙과 산간은 일중편차가 컸지만 연안지역은 편차가 작을 것으로 전망되었다. 이는 현재기후에 대한 기존분석결과와는 다소 차이가 있는 것으로, 이 연구에서 생산된 미래 풍력자원 지도는 향후 기후 변화 가능성이 큰 지역의 시공간적 풍황을 감안하여 풍력단지 입지 선정 및 풍력운영을 위한 장기계획 마련에 있어서 유용한 자료가 되리라 기대된다.

In this study future wind resource maps have been produced under climate change scenario using ensemble regional climate model weather research and forecasting(WRF) for the period from 2045 to 2054(mid 21st century). Then various spatiotemporal analysis has been conducted in terms of monthly and diurnal. As a result, monthly variation(monsoon circulation) was larger than diurnal variation(land-sea circulation) throughout the South Korea. Strong wind area with high wind power energy was varied on months and regions. During whole years, strong wind with high wind resource was pronounced at cold(warm) months in particular Gangwon mountainous and coastal areas(southwestern coastal area) driven by strong northwesterly(southwesterly). Projected strong and weak wind were presented in January and September, respectively. Diurnal variation were large over inland and mountainous area while coastal area were small. This new monthly and diurnal variation would be useful to high resource area analysis and long-term operation of wind power according to wind variability in future.

키워드

참고문헌

  1. Glenn, J. C., Florescu, E. and The Millenium Project Team, 2016, 2015-16 state of the future(Executive summary), Research report, Global Futures Intelligence System, USA, pp. 1-10.
  2. Hong, S. Y., Dudhia, J. and Chen, S. H., 2004, A revised approach to ice microphysical processes for the bulk parameterization of cloud and precipitation, Monthly Weather Review, Vol. 132, pp. 103-120. https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2
  3. Hong, S. Y., Noh, Y. and Dudhia, J., 2006, A new vertical diffusion package with an explicit treatment of entrainment processes, Monthly Weather Review, Vol. 134, pp. 2318-2341. https://doi.org/10.1175/MWR3199.1
  4. IPCC, 2007, Climate Change 2007: The physical science basis, Research report, Intergovernmental Panel On Climate Change, UK and USA, pp. 7-45.
  5. Journal of Geophysical Research, Vol. 115, D05105, doi:10.1029/2009JD012574.
  6. Kain, J. S. and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain-Fritsch scheme. The representation of cumulus convection in numerical models, Meteorological Monographs, Vol. 24, pp. 165-170.
  7. KIER, 2016, New&Renewable Data Center, http://kredc.kier.re.kr.
  8. Kim, D. Y. and Han, K. S., 2012, Temporal and spatial wind information production and correction algorithm development by land cover type over the Republic of Korea, Journal of the Korean Society for Geospatial Information Science, Vol. 20, No. 3, pp. 19-27.
  9. Kim, D. Y., Kim, J. Y. and Kim, J. J., 2013, Mesoscale simulations of multi-decadal variability in the wind resource over Korea, Asia-Pacific Journal of Atmospheric Sciences, Vol. 49, No. 2, pp. 183-192. https://doi.org/10.1007/s13143-013-0019-9
  10. Kim, J. Y. and Kim, D. Y., 2013, Spatio-temporal characteristics of wind observations over South Korea: 1982-2011, Asia-Pacific Journal of Atmospheric Sciences, Vol. 49, No. 4, pp. 551-560. https://doi.org/10.1007/s13143-013-0049-3
  11. Kim, J. Y. and Kim, D. Y., 2016, Study on longterm changes in wind power classes over South Korea, Journal of Korean Society of Environmental Technology, Vol. 17, No. 5, pp. 442-450.
  12. Kim, J. Y., Kim, D. Y. and Oh, J. H., 2014, Projected changes in wind speed over the Republic of Korea under A1B climate change scenario, International Journal of Climatology, Vol. 34, No. 5, pp. 1346-1356. https://doi.org/10.1002/joc.3739
  13. National Institute of Meteorological Sciences, 2016, Weather Resource Maps, NIMS, http://greenmap.go.kr/
  14. Mari, R., Bottai, L., Busillo, C., Calastrini, F., Gozzini, B. and Gualtieri, G., 2011, A GIS-based interactive web decision support system for planning wind farms in Tuscany(Italy), Renewable Energy, Vol. 36, pp. 754-763. https://doi.org/10.1016/j.renene.2010.07.005
  15. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J. and Clough, S. A., 1997, Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave, Journal of Geophysical Research, Vol. 102, pp. 16663-16682. https://doi.org/10.1029/97JD00237
  16. Nakanishi, M. and Niino, H., 2006, An improved Mellor Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Boundary-Layer Meteorology, Vol. 119, pp. 397-407. https://doi.org/10.1007/s10546-005-9030-8
  17. REN21, 2016, Renewables 2016-Global status report, Research report, Renewable Energy Policy Network for the 21st century, France, pp. 75-85.
  18. Roeckner, E., Baeuml, G., Bonventura, L., Brokopf, R., Esch, M., Giorgetta, M., Hangemann, S., Kirchner, I., Komblueh, L., Manzini, E., Rhodin, A., Schlese, U., Schulzweida, U. and Tompkins, A., 2003, The atmospheric general circulation model ECHAM5, Part I: Model description-Report 349, Research report, Max Plank Institute for Meteorology, Germany, pp. 1-127.
  19. Skamarock, W. C., Kelmp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X. Y., Wang, W. and Powers, J. G., 2008, A description of the advanced research WRF version 3, Technical report, NCAR, USA, pp. 1-113.
  20. UNFCCC, 2015, Climate action now-Summary for policy makers, Technical report, United Nations Framework Convection on Climate Change, Germany, pp. 4-66.
  21. von Storch, H., Lagenberg, H. and Feser, F., 2000, A spectral nudging technique for dynamical downscaling purpose, Monthly Weather Review, Vol. 128, pp. 3664-3673. https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2
  22. Wang, W, Bruyere, C., Duda, M. G., Dudhia, J., Gill, D. O., Lin, H. C., Michalakes, J., Rizvi, S. and Zhang, X., 2011, ARW version 3 modeling system user's guide, Technical report, NCAR, USA, pp. 1-371.