• Transactions of the Korean Society of Mechanical Engineers B
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• v.41 no.7
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• pp.469-480
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• 2017

For accurate AEP prediction in a wind farm, it is necessary to effectively calculate the wind speed reduction and the power loss due to the wake effect in each wind direction. In this study, a computer program for AEP prediction considering directional wake effect was developed. The results of the developed program were compared with the actual AEP of the wind farm and the calculation result of existing commercial software to confirm the accuracy of prediction. The applied equations are identical with those of commercial software based on existing theories, but there is a difference in the calculation process of the detection of the wake effect area in each wind direction. As a result, the developed program predicted to be less than 1% of difference to the actual capacity factor and showed more than 2% of better results compared with the existing commercial software.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.65-71
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• 2013

Wind shear means the variation of wind speed according to the height. Wind shear is the important factor affecting the energy production of wind turbines. Power Law is used to extrapolate wind speed data. Normally, a Power Law exponent of 0.143 is used and this is referred to as the 1/7th Power Law. The Power Law exponent is affected by atmospheric stability and surface roughness of the site. Thus, it is necessary to calculate the Power Law exponent of the site exactly for an accurate estimation of wind energy. In this study, wind resources were measured at the three Met-masts which were located in the coastal area of northeastern Jeju Island. The Power Law exponents of the sites were calculated and proposed using measured data. They were 0.141 at Handong, 0.138 at Pyeongdae, and 0.1254 at Udo. We compared annual energy productions which are calculated using a Power Law exponent of 0.143, the proposed value of the Power Law exponents for each site, and the measured data. As a result, the cases of calculating using the proposed values were more similar to the cases using the measured data than the cases using the 0.143 value. Finally, we found that the propsed values of the Power Law exponent are available to more accurately estimate wind resources.

• Journal of the Korean Society of Marine Environment & Safety
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• v.18 no.6
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• pp.545-550
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• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.49.1-49.1
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• 2011

집광채광 설비는 건축물의 조명에너지 절감 및 자연광의 실내 유입을 위해 적용 가능한 태양에너지설비로써 다른 신 재생 에너지 설비와 다르게 연간에너지생산에 대한 정량적 데이터가 아직까지 부재하다. 집광채광 설비의 설치효과를 판단하기 위해서는 집광채광 설비 설치에 따른 연간 에너지생산량 산출이 필요하며, 이를 위해서는 각 구성부분(집광부, 전송부 및 산광부)의 광전송 효율에 대한 데이터가 구축되어야 한다. 본 연구는 집광채광 설비의 효율 분석에 관한 첫 번째 단계로써 외부광속에 대한 집광부 통과 직후의 내부광속의 비율을 예측하였다. 국내에 보급된 집광채광 설비는 대부분 프리즘형과 광덕트형이며, 우선적으로 집광부 입사면의 경사각과 방위각이 다양하여 내부광속 산출방법론이 매우 복잡한 프리즘형을 분석대상으로 삼았다. 전일사량, 외부조도 및 집광부 내부조도가 측정되었으며, 외부광속으로부터 내부광속을 산출하는 공식을 유도하기 위해 천공상태에 따라 전일사량 측정치가 직산분리 되었다. Perez model과 Liu and Jordon에 의해 제시된 계산식과 입사면 및 집광부 면적을 고려하여 수평면 외부조도 측정치로부터 외부광속이 그리고 내부조도로부터 내부광속이 산출되었다. 입사면의 투과율이 동일하다는 전제 하에 천공상태에 따른 태양광 투과 비율을 도출한 결과, 담천공(Kt ${\leq}$ 0.3)에서 0.39, 부분담천공(0.3${\geq}$ 0.78)에서 1.0으로 나타났다. 도출된 투과비율을 외부광속에 적용하여 내부광속을 계산한 결과치와 측정치는 약 ${\pm}9%$ 정도의 차이를 보였다. 연간 기상데이터에 위와 같은 방법론이 적용되면 프리즘형 집광부의 연간 내부광속이 산출될 수 있다. 또한 기존 연구에서 제시된 발광효율 산출식과 일사 파장에 따른 시감도를 고려하면 매 시간별 외부조도도 산출이 가능하다. 일사량 측정치와 외부조도 측정치 사이의 상관관계를 분석한 결과 결정계수 $R^2$이 0.99인데 반해 일사량 측정치와 외부조도 계산치 사이의 상관관계 결정계수는 0.95로 측정치 보다 약간 작은 값을 갖는다. 이렇게 산출된 외부조도는 각 입사면의 면적을 반영하여 외부광속으로 변환되고, 앞서 산출된 천공상태별 투과비율이 적용됨으로써 내부광속이 도출될 수 있다. 이와 같은 집광부에 대한 연구를 바탕으로 향후 전송부와 산광부 효율을 도출하고 궁극적으로 집광채광 설비를 통해 실내에 전달되는 연간 빛에너지를 예측할 수 있을 것이다. 또한 본 연구의 방법론은 다른 형태의 집광채광 설비에도 적용이 가능할 것으로 판단되며, 국내 집광채광 설비의 연간 에너지생산량에 대한 폭 넓은 데이터 구축이 가능할 것으로 기대된다.

• New & Renewable Energy
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• v.3 no.3
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• pp.63-71
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• 2007

본 연구의 목적은 3차원 풍력터빈 블레이드 최적형상설계를 위한 실용적이고 효율적인 설계과정을 구현하는 것이다. 국내 연안의 해상풍력에 적용하기 위해서 통계적 모델을 이용하여 풍황자료를 분석하였다. 설계에 관련된 많은 수의 설계변수를 효과적으로 관리하기 위해서 설계과정은 운용조건 최적화와 블레이드 형상설계의 2단계로 구성하였다. 실험계획법에 의해 추출된 각 운용조건 설계점은 형상설계를 위한 입력 값으로 제공된다. 형상설계 단계에서는 최소에너지손실 조건과 결합된 BEMT를 이용하여 각 블레이드 단면에서의 시위길이와 피치각 분포를 최적화하였다. 블레이드 단면 익형은 NREL S830을 이용하였고, 익형의 공력성능은 XFOIL을 이용하여 예측하였다. 설계된 블레이드 형상의 성능해석을 수행하고 그 결과를 바탕으로 반응면을 구성하였다. 좀 더 나은 성능을 가진 블레이드 형상을 찾기 위해서 초기설계공간에서 확률적 방법을 이용하여 타당성 있는 설계공간까지 운용조건 설계변수를 이동시키고 구배최적화 기법을 통해 각각의 제약함수를 만족하면서 연간에너지생산량을 최대로 하는 최적블레이드 형상을 구현하였다. 제시된 최적설계과정은 풍력터빈블레이드 개발에 실용적이고 신뢰성 있는 설계툴로서 사용이 가능하다.

• Journal of the Korean Solar Energy Society
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• v.37 no.6
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• pp.13-24
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• 2017

Finding promising wind farm sites in islands of Korea is performed in this study. Total ten islands that have been measuring wind speed and direction using automatic weather stations for at least ten years were subjects of this study. Conditions for finding suitable wind farm sites including wind resource and various exclusion factors were applied and two islands that were found not to be suitable for wind farms were excluded. Micositing of a single wind turbine for the remaining eight islands was performed to estimate the annual energy production and the capacity factor.. Based on the simulation results, the wind farm sites selected within the eight islands were found to be suitable for wind power. The capacity factors were varied between 22.3% and 33.0% for a 100 kW wind turbine having a hub height of 30 m.

• Clean Technology
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• v.9 no.1
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• pp.23-28
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• 2003

The generation of food waste in Korea amounts to 4.10 million per year, which corresponds to 820,000 dry ton of organic waste. This has been used traditionally as animal feed or soil conditioner, but its efficacy has remained doubtful in recent years. In this study as an alternative we considered methane production by anaerobic treatment, which has an advantage of 200 million US dollars over aerobic methods. The production of methane amounts to $4.40{\times}10^8m^3$, 3.43% of $1.28{\times}10^8m^3$, total natural gas used in Korea. Furthermore the methane from household kitchen food waste amounts to 28.9% of the total gas used in the kitchen.

• New & Renewable Energy
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• v.1 no.2 s.2
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• pp.19-25
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• 2005

This study explains ther procedure that should be taken to develp a successful wind park project. It provides a guideline for the activities and studies to be done as a step by step solution. This study follows a chronological flow throughout the whole development Process. This Paper covers technical consideration, assessment of wind energy resource, wind Park siting and energy yield calculation This presented knowledge h3s been mostly gained by the experience from Youngduk wind park project. The further comparison study will be performed between the theoretical prediction and the actual yield of the Youngduk wind park.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.448-451
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• 2005

This paper presents the methodology to analyze flow duration characteristics and performance prediction for small hydro power(SHP) plants and its application. The flow duration curvecan be decided by using monthly rainfall data at the most of the SHP sites with no useful hydrological data. It was proved that the monthly rainfall data can be characterized by using the cumulative density function of Weibull distribution and Thiessen method were adopted to decide flow duration curve at SHP plants. And, the performance prediction has been studied and development. One SHP plant was selected and performance characteristics was analyzed by using the developed technique. Primary design specfications such as design flowrate, plant capacity, operational rate and annual electricity production for the SHP plant were estimated. It was found that the methodology developed in this study can be a useful tool to predict the performance of SHP plants and candidate sites in Korea.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.4
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• pp.201-212
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• 2016

A Wind resource assessment and optimal micrositing of wind turbines were implemented for the development of an onshore wind farm of 30 MW capacity on Gadeok Island in Busan, Republic of Korea. The wind data measured by the automatic weather system (AWS) that was installed and operated in the candidate area were used, and a reliability investigation was conducted through a data quality check. The AWS data were measured for one year, and were corrected for the long term of 30 years by using the modern era retrospective analysis for research and application (MERRA) reanalysis data and a measure- correlate-predict (MCP) technique; the corrected data were used for the optimal micrositing of the wind turbines. The micrositing of the 3 MW wind turbines was conducted under 25 conditions, then the best-optimized layout was analyzed with a various wake model. When the optimization was complete, the estimated park efficiency and capacity factor were from 97.6 to 98.7 and from 37.9 to 38.3, respectively. Furthermore, the annual energy production (AEP), including wake losses, was estimated to be from 99,598.4 MWh to 100,732.9 MWh, and the area was confirmed as a highly economical location for development of a wind farm.