• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.1-10
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• 2015

Promising onshore wind farm sites in Gangwon province of Korea were investigated in this study. Gangwon province was divided into twenty five simulation regions and a commercial program based on Reynolds averaged Navier-Stokes equation was used to find out wind resource maps of the regions. The national wind atlas with a period 2007-2009 developed by Korea institute of energy research was used as climatologies. The wind resource maps were combined to construct a wind resource map of Gangwon province with a horizontal spatial resolution of 100m. In addition to the wind resource, national environmental zoning map, distance from substation, residence and automobile road, Beakdudaegan mountain range, terrain slope, airport and military reservation district were considered to find out promising wind farm sites. A commercial wind farm design program was used to find out developable wind farm capacities in promising wind farm site with and without excluding environmental protection regions. The total wind farm capacities with and without excluding the protection regions were estimated to be 46MW and 598MW, respectively, when a 2MW commercial wind turbine was employed.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.184.1-184.1
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• 2010

In order to research the way to evaluate wind resource without actual Met Mast data, this paper has been carried out on the southeastern region of Jeju island, Korea. Although wind turbine has been an economical alternative energy resource, misjudging the prediction of lifetime or payback period occurs because of the inaccurate assessment of wind resource and the location of wind turbine. Using WindPRO(Ver. 2.7), a software for wind farm design developed by EMD from Denmark, wind resources for the southeastern region of Jeju island was analyzed, and the performance of WindPRO prediction was evaluated in detail. Met Mast data in Su-san 5.5Km far from Samdal wind farm, AWS in Sung-san 4.5km far from Samdal wind farm, and Korea Wind Map data had been collected for this work.

• Journal of the Korean Solar Energy Society
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• v.28 no.6
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• pp.1-7
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• 2008

The wind resource potentials of South Korea are estimated as preliminary stage using the national wind map which has been being established by numerical wind simulation and GIS (Geographical Information System) exclusion analysis. The wind resource potentials are classifying into theoretical, geographical, technical and implementation potentials and the calculation results are verified by comparing to other countries' potentials. In GIS exclusion, urban, road, water body, national parks and steep slope area are excluded from onshore geographical potential while water depth and offshore distance from the shoreline are applied as offshore exclusion conditions. To estimate implementation potential, dissemination records of European countries are adopted which is about 1/8 of geographical potential. The implementation potential of South Korea would correspond 12.5GW which is 1.7 times of the national wind energy dissemination target until 2030.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.80-89
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• 2016

In the complex terrain where local wind systems are formed, accurate understanding of regional wind variability is required for wind resource assessment. In this paper, cluster analysis based on the similarity of time-series wind vector was applied to classify wind regions with similar wind characteristics and the meteorological validity of regionalization method was evaluated. Wind regions in Jeju Island and Busan were classified using the wind resource map of Korea created by a mesoscale numerical weather prediction modeling. The evaluation was performed by comparing wind speed, wind direction, and wind variability of each wind region. Wind characteristics, such as mean wind speed and prevailing wind direction, in the same wind region were similar and wind characteristics in different wind regions were meteor-statistically distinct. It was able to identify a singular wind region at the top area of Mt. Halla using the inconsistency of wind direction variability. Furthermore, it was found that the regionalization results correspond with the topographic features of Jeju Island and Busan, showing the validity.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.310-313
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• 2008

The wind resource potentials of South Korea are estimated as preliminary stage using the national wind map which has been being established by numerical wind simulation and GIS (Geographical Information System) exclusion analysis. The wind resource potentials are classifying into theoretical, geographical, technical and implementation potentials and the calculation results are verified by comparing to other countries' potentials. In GIS exclusion, urban, road, water body, national parks and steep slope area are excluded from onshore geographical potential while water depth and offshore distance from the shoreline are applied s offshore exclusion conditions. To estimate implementation potential, dissemination records of European countries are adopted which is about 1/8 of geographical potential.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.532-533
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• 2009

The national wind map of South Korea has been established as a core data to support national strategy building and promotion of wind energy dissemination. The national wind map has been made by numerical wind simulation with the spatial resolution of 1 km horizontal, 10m vertical and temporal resolution of 1 hour interval for 5 years period (2003-2007). Therefore, an application system linked with the national wind map named KIER-WindMap$^{TM}$ is being developed to be used by the government, local government, developers and researchers. We introduce the current status of the application system and the future development plans.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.300-302
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• 2008

For the evaluation of wind resources, numerical simulation was carried out as a tool for establishing wind map around the korean peninsula. Initial and boundary condition are given by 3 hourly RDAPS(Regional Data Assimilation and Prediction System) data of KMA(Korea Meteorology Administration) and high resolution terrain elevation land cover(30 seconds) data from USGS(United States Geological Survey). Furthermore, Data assimilation was adopted to improve initial meteorological data with buoy and QuikSCAT seawinds data. The simulation was performed from 2003 to 2006 year. To understand wind data correctly in complex terrain as the korean peninsula, at this research, Wind map was classified 4 categories by distance from coastline and elevation.

• Journal of the Korean Solar Energy Society
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• v.36 no.4
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• pp.21-29
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• 2016

Commercial wind farm design tools and the national wind map are used to determine the implementation potential of offshore wind power in Korea in this study. For this, the territorial waters of Korea were divided into nine analysis regions and a commercial CFD code was used to obtain wind resource maps at 100m A.S.L. which is the hub height of a 5MW wind turbine used in this study. With the wind resource obtained, factors including water depth, distance from substations, minimum and maximum capacity of a wind farm, distance between turbines and wind farms were considered to determine wind power potential. Also, the conservation areas, military zones, ports, fishing grounds, etc. were considered and excluded. As the result, a total capacity of 6,720 MW was found to be the implementation potential and this corresponds to $3.38MW/km^2$ in API. Also if the distance from the substation is not considered, the potential increased to be 10,040 MW. This offshore wind farm potential is considered enough to satisfy the target of wind farm capacities in the 7th national plan for electricity demand and supply.

• New & Renewable Energy
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• v.17 no.4
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• pp.1-8
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• 2021

To determine the wind turbine class in the offshore of the Korean Peninsula, the reference wind speed for a 50-y return period at the hub height of a wind turbine was estimated using the reanalysis data sets. The most recent reanalysis data, ERA5, showed the highest correlation coefficient (R) of 0.82 with the wind speed measured by the Southwest offshore meteorological tower. However, most of the reanaysis data sets except CFSR underestimated the annual maximum wind speed. The gust factor of converting the 1 h-average into the 10 min-average wind speed was 1.03, which is the same as the WMO reference, using several meteorological towers and lidar measurements. Because the period, frequency, and path of typhoons invading the Korean Peninsula has been changing owing to the climate effect, significant differences occurred in the estimation of the extreme wind speed. Depending on the past data period and length, the extreme wind speed differed by more than 30% and the extreme wind speed decreased as the data period became longer. Finally, a reference wind speed map around the Korean Peninsula was drawn using the data of the last 10 years at the general hub-height of 100 m above the sea level.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.11-18
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• 2016

The local wind systems in the Pohang region were categorized into wind sectors. Still, thorough knowledge of wind resource assessment, wind environment analysis, and atmospheric environmental impact assessment was required since the region has outstanding wind resources, it is located on the path of typhoon, and it has large-scale atmospheric pollution sources. To overcome the resolution limitation of meteorological dataset and problems of categorization criteria of the preceding studies, the high-resolution wind resource map of the Korea Institute of Energy Research was used as time-series meteorological data; the 2-step method of determining the clustering coefficient through hierarchical clustering analysis and subsequently categorizing the wind sectors through non-hierarchical K-means clustering analysis was adopted. The similarity of normalized time-series wind vector was proposed as the Euclidean distance. The meteor-statistical characteristics of the mean vector wind distribution and meteorological variables of each wind sector were compared. The comparison confirmed significant differences among wind sectors according to the terrain elevation, mean wind speed, Weibull shape parameter, etc.