• Journal of the Korean Geographical Society
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• v.49 no.6
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• pp.833-848
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• 2014

The assessment of the current and future climate change-induced potential wind energy is an important issue in the planning and operations of wind farm. Here, the authors analyze spatiotemporal characteristics and variabilities of wind energy over Korean Peninsula in the near future (2006-2040) using Representative Concentration Pathway(RCP) scenarios data. In this study, National Institute of Meteorological Research (NIMR) regional climate model HadGEM3-RA based RCP 2.6 and 8.5 scenarios are analyzed. The comparison between ERA-interim and HadGEM3-RA during the period of 1981-2005 indicates that the historical simulation of HadGEM3-RA slightly overestimates (underestimates) the wind energy over the land (ocean). It also shows that interannual and intraseasonal variability of hindcast data is generally larger than those of reanalysis data. The investigation of RCP scenarios based future wind energy presents that future wind energy density will increase over the land and decrease over the ocean. The increase in the wind energy and its variability is particularly significant over the mountains and coastal areas, such as Jeju island in future global warming. More detailed analysis presents that the changes in synoptic conditions over East Asia in future decades can influence on the predicted wind energy abovementioned. It is also suggested that the uncertainty of the predicted future wind energy may increase because of the increase of interannual and intra-annual variability. In conclusion, our results can be used as a background data for devising a plan to develop and operate wind farm over the Korean Peninsula.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.404-408
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• 2007

현재 국내에서 운용중인 풍력발전시스템은 국내 풍력자원에 대한 정확한 정보의 부재와 국내 풍황에 맞지 않는 국외 모델을 그대로 운용하는 등의 몇 가지 문제를 드러내었다. 본 연구의 목적은 국내 연안의 해상에서 한국형 해상풍력터빈을 설치하기 위한 잠재적 최적위치와 풍황자료 산출 최적화 알고리즘을 구현하는 것이다. 최적화 알고리즘은 얕은 수심 분포와 연안에서의 거리를 제약조건으로 하고 최대 에너지밀도를 가진 지점을 구하는 것으로 정식화하였다. 풍황자료 산출을 위해서 국내 연안의 해상 풍황자료를 포함하는 기상풍황자료를 통계적 모델로 분석하여 바람지도를 작성하였다. 이 바람지도를 이용하여 지질 통계학 분야의 관측기법인 크리깅 모델을 구성하고, 전역최적화기법인 유전자알고리즘을 이용하여 제약조건을 만족하는 최대에너지밀도값과 그 위치를 도출하였다. 수치최적화 결과 우리나라 풍력 자원의 대략적인 잠재량과 현황파악이 가능하였고, 해상풍력발전단지가 조성 가능한 개략적인 위치를 예측할 수 있었다.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.145-150
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• 2002

본 연구는 국내에서의 지역별 풍력자원을 조사하여 자연에너지인 풍력을 활용하고자 기상청의 기상 관측소 자료를 이용하여 분석하였던 바, 다음과 같이 요약 할 수 있다. 가. 평균풍속의 분포를 보면 제주고층이 10.8m/s로 가장 높았고, 순천이 0.6m/s로 가장적게 나타난 것으로 일반적으로 해안가나 내륙고산지대에서 높은 풍속을 보였다. 나. 풍속지속시간을 보면 대부분의 지역에서 3.0m/s 이상, 연간 5,000시간이상으로 나타나 풍력의 이용가능성을 알 수 있었다. 다. 풍력 에너지 밀도는 인천 142kWh/m$^3$, 제주도 400kWh/m$^3$, 대관령 280kWh/m$^3$, 울릉도 140kWh/m$^3$, 목포 200kWh/m$^3$및 군산 180kWh/m$^3$높게 나타났다.

• Journal of the Korean Solar Energy Society
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• v.30 no.1
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• pp.19-24
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• 2010

At the altitudes above 3km, the wind is three to four time faster and less variable than at the current MW sized wind turbine hub height of around 100m. In addition, power generation from wind turbines installed on the ground is intermittent because local wind conditions are affected by local topography and artificial structures. The wind energy researchers and engineers are now looking for revolutionary ideas to utilize high altitude wind resources in-creasing the capabilities of wind turbine installations. This article presents and discusses several concepts for wind energy exploitation from wind at high altitudes. The concepts presented in this paper make use of lifting bodies, called wings or kites, connected to a tether that stetches into the higher regions of the atmosphere.

• Journal of the Korean Solar Energy Society
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• v.33 no.2
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• pp.18-27
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• 2013

This paper presents a method for assessing the wind energy potential at complex terrain using probability distribution. And the proper probability models of the parameters estimating the wind energy are presented. Finally a mixture-Weibull determined by numerical methods procedure are proposed to assess the probability distribution of the energy potential at a site. The developed method is applied to the Kwanjungchun Bridge and compared with wind records which the neighboring weather station.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.617-625
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• 2010

The selection of a site where strong wind blows is important to increase effectively the electricity of wind power in proportion to the cube of the wind speed. It is advisable to establish the wind turbine in the coastal area with strong wind speed rather than in the inland. And the development of offshore wind energy is expected to solve the noise problem that is one of the important weaknesses in the wind turbine. In the process of the development business of wind energy, knowing forehead the wind power possibility in any area is one of the essential factors to choose the most optimum site of wind power. In this paper, the potential of wind power around JeJu coastal area is examined by using the wind data that Korea Meteorological Administration has surveyed for 10 years in 14 observation points. Wind speed data is revised to wind speed in 80 meters assuming installation height of the wind turbine, and wind power density and annual wind energy are also calculated. And annual electricity generation and percent of energy efficiency in all the observation points are estimated by using the information about 3,000 KW wind turbine.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.181.1-181.1
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• 2010

풍력터빈에서 전력생산량은 속도의 세제곱에 따라 바람의 속도에 크게 영향 받는다. 본 연구에서는 기상청에서 제공하는 새만금 인근 해안의 2005부터 2007년까지의 AWS(지역별 관측자료)와 해양에서 떨어진 새만금 서해의 2000년부터 2009년까지 QuikSCAT위성 데이터를 이용하여 속도에 따른 확률 밀도 분포를 획득하였다. 측정된 관측 데이터는 80m 높이에서의 보정 자료를 근거로 하였으며, IEC 61400-12와 13을 근거로 실증단지 적합성에 대한 분석을 하였다.

• 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 Society of Marine Environment & Safety
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• v.14 no.3
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• pp.205-210
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• 2008

In recent years, the crisis of energy is growing seriously and also the contamination of ecology has been reverberated as international problem. The social concern about energy crisis has been growing for the last several years and also the interest in new and renewable energy has been increased. The probability of wind power resource over Saemangeum is reviewed with the data of Gunsan weather station. Through this study, it is cleared that the Saemangeum area has fluent wind power source to design the wind farm in Korea. According to calculation results, it is possible to predict the wind velocity density around the Saemangeum area using the shape factor as a parameter.

• KIEAE Journal
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• v.12 no.1
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• pp.127-132
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• 2012

The energy consumption in the world is growing rapidly. And the environmental issues of climate become a important task. The interest in renewable energy like wind and solar is increasing now. Especially, by reducing power transmission loss, a small wind power is getting attention at the residential areas and campus of university. In this study, we attempted to estimate and compare the wind energy density using wind data of AWS (Automatic Weather Station) of H University. In this case of a campus, the weibull distribution parameter C is 2.27, and K is 0.88. According to the data, the energy density of the small wind power is 12.7 W/m2. We did CFD(Computational Fluid Dynamics) simulations at H University campus by 7 wind directions(ENE, ESE, SE, NW, WNW, W, WSW). In the results, we suggest 4 small wind powers. The small wind power generating system can produce 4,514kWh annually.