• 산업기술연구
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• 제31권A호
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• pp.119-122
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• 2011

AWS (Automated Weather Station) wind data was used to predict the annual energy production of Gangwon wind farm having a total capacity of 98 MW in Korea. Two common wind energy prediction programs, WAsP and WindSim were used. Predictions were made for three consecutive years of 2007, 2008 and 2009 and the results were compared with the actual annual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm. The results from both prediction programs were close to the actual energy productions and the errors were within 10%.

• 한국태양에너지학회 논문집
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• 제36권2호
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• pp.9-17
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• 2016

In order to examine how accurately the wind farm design software, WindPRO and Meteodyn WT, predict annual energy production (AEP), an investigation was carried out for Seongsan wind farm of Jeju Island. The one-year wind data was measured from wind sensors on met masts of Susan and Sumang which are 2.3 km, and 18 km away from Seongsan wind farm, respectively. MERRA (Modern-Era Retrospective Analysis for Research and Applications) reanalysis data was also analyzed for the same period of time. The real AEP data came from SCADA system of Seongsan wind farm, which was compare with AEP data predicted by WindPRO and Meteodyn WT. As a result, AEP predicted by Meteodyn WT was lower than that by WindPRO. The analysis of using wind data from met masts led to the conclusion that AEP prediction by CFD software, Meteodyn WT, is not always more accurate than that by linear program software, WindPRO. However, when MERRA reanalysis data was used, Meteodyn WT predicted AEP more accurately than WindPRO.

• 한국수소및신에너지학회논문집
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• 제24권6호
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• pp.558-565
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• 2013

This paper presents how to determine AEP(Annual Energy Production) by a small wind turbine in DuckjeokDo island. Evaluation of AEP is introduced to make a self-contained island including renewable energy sources of wind, solar, and tidal energy. To determine the AEP in DuckjeokDo island, a local wind data is analyzed using the annual wind data from Korea Institute of Energy Research firstly. After the wind data is separated in 12-direction, a mean wind speed at each direction is determined. And then, a small wind turbine power curve is selected by introducing the capacity of a small wind turbine and the energy production of the wind turbine according to each wind direction. Finally, total annual wind energy production for each small wind turbine can be evaluated using the local wind density and local energy production considering a mechanical energy loss. Throughout the analytic study, it is found that the AEP of DuckjeokDo island is about 2.02MWh/y and 3.47MWh/y per a 1kW small wind turbine installed at the altitude of 10 m and 21m, respectively.

• 한국태양에너지학회 논문집
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• 제31권5호
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• pp.1-8
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• 2011

In this study, we performed a sensitivity analysis to see how the true north error of a wind direction vane installed to a meteorological mast affects predictions of the annual-average wind speed and the annual energy production. For this study, two meteorological masts were installed with a distance of about 4km on the ridge in complex terrain and the wind speed and direction were measured for one year. Cross predictions of the wind speed and the AEP of a virtual wind turbine for two sites in complex terrain were performed by changing the wind direction from $-45^{\circ}$ to $45^{\circ}$with an interval of $5^{\circ}$. A commercial wind resource prediction program, WindPRO, was used for the study. It was found that the prediction errors in the AEP caused by the wind direction errors occurred up to more than 20% depending on the orography and the main wind direction at that site.

• 대한기계학회논문집B
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• 제41권7호
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• pp.469-480
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• 2017

풍력발전단지에서 연간 에너지 생산량 예측의 정확도를 위해서는 바람 방향별 후류영향에 의한 풍속감소와 이에 따른 발전량 손실을 효과적으로 계산하여야 한다. 본 연구에서는 연간 에너지 생산량 예측을 위하여 방향별 후류영향을 고려한 계산 프로그램을 개발하고, 예측 적합성을 확인하기 위해 실제 풍력발전단지의 연간 에너지 생산량 분석 결과 및 기존 상용 소프트웨어의 계산결과와 비교하였다. 적용된 계산식들은 기존 이론들을 바탕으로 하고 있어 상용 소프트웨어와 동일하지만 풍향별 후류영향 범위의 계산과정에서 차이가 있다. 비교결과 개발 프로그램은 실제 풍력발전단지 전체 시스템 이용율에 1% 이내로 근접하였고 기존 상용 프로그램을 이용한 예측 결과보다 2% 이상 실제 연간 시스템 이용율에 근접하는 결과를 보여주었다.

• 해양환경안전학회지
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• 제18권6호
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• pp.545-550
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• 2012

풍력발전 단지의 설계시 풍력 자원 평가 과정은 필수적인 과정이다. 풍력 자원 평가를 위해 장기풍황(20년)자료를 이용하여야 하지만 장기간 관측하는 것은 어렵기 때문에 예정지의 1년 이상의 관측데이터로 평가를 실시하였다. 예정지의 단기 풍황탑(Met-Mast; Meteorology Mast) 자료를 주변의 장기관측 자료인 자동기상관측(AWS; Automatic Weather Station)데이터를 이용하여 수학적 보간법으로 예정지의 데이터를 장기 데이터로 변환한 것을 MCP(Measure-Correlative-Predict)기법이라 한다. 본 연구에서는 MCP기법 중 선형 회계방법을 적용하였다. 선택된 MCP 회귀 모델식에 따라 제주 북동부 구좌지역의 AWS데이터를 제주 북동부 한동 지역의 Met-mast 데이터에 적용하여 연간 에너지 생산량을 예측 하였다. 예정지의 단기 풍황을 이용하였을 때와 보정된 장기 풍황을 이용하여 때 연간 에너지 생산량을 비교하였다. 그 결과 연간 약 3.6 %의 예측오차를 보였고, 이는 연간 약 271 MW의 에너지 생산량의 차이를 의미한다. 풍력발전기의 생애주기인 20년을 비교 하였을 때 약 5,420 MW의 차이를 나타내었으며, 이는 약 9개월 정도의 에너지 생산량과 비슷한 수준이다. 결과적으로, 제안 된 선형 회귀 MCP 방법을 이용하는 것이 단기관측 자료를 통한 불확식성을 제거하는 합리적인 방법으로 판단된다.

• 한국수소및신에너지학회논문집
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• 제25권6호
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• pp.629-635
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• 2014

This paper presents annual energy production (AEP) by a 1.5kW wind turbine due to be installed in Deokjeok-Do island. Local wind data is determined by geometric shape of Deokjeok-Do island and annual wind data from Korea Institute of Energy Research at three places considered to be installed the wind turbine. Numerical simulation using WindSim is performed to obtain flow pattern for the whole island. The length of each computation grid is 40 m, and k-e turbulence model is imposed. AEP is determined by the power curve of the wind turbine and the local wind data obtained from numerical simulation. To capture the more detailed flow pattern at the specific local region, Urumsil-maul inside the island, fine mesh having the grid length of 10m is evaluated. It is noted that the input data for numerical simulation to the local region is used the wind data obtained by the numerical results for the whole island. From the numerical analysis, it is found that a local AEP at the Urumsil-maul has almost same value of 1.72 MWh regardless the grid resolutions used in the present calculation. It is noted that relatively fine mesh used for local region is effective to understand the flow pattern clearly.

• 한국태양에너지학회 논문집
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• 제31권2호
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• pp.72-81
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• 2011

The wind data obtained from an AWS(Automated Weather Station) was used to predict the AEP(annual energy production) of Gangwon wind farm having a total capacity of 98 MWin Korea. A wind energy prediction program based on the Reynolds averaged Navier-Stokes equation was used. Predictions were made for three consecutive years starting from 2007 and the results were compared with the actual AEPs presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm. The results from the prediction program were close to the actual AEPs and the errors were within 7.8%.

• 한국태양에너지학회 논문집
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• 제32권6호
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• pp.1-10
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• 2012

The annual energy production of Gangwon wind farm was predicted for three consecutive years of 2007, 2008 and 2009 using commercial programs, WindPRO and WindSim which are known to be used the most for wind resource prediction in the world. The predictions from the linear code, WindPRO, were compared with both the actual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm and also the predictions from the CFD code, WindSim. The results from WindPRO were close to the actual energy productions and the errors were within 11.8% unlike the expectation. The reason for the low prediction errors was found to be due to the fact that although the wind farm is located in highly complex terrain, the terrain steepness was smaller than a critical angle($21.8^{\circ}$) in front of the wind farm in the main wind direction. Therefore no flow separation was found to occur within the wind farm. The flow separation of the main wind was found to occur mostly behind the wind farm.

• 한국태양에너지학회 논문집
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• 제34권2호
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• pp.82-90
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• 2014

The MERRA reanalysis data provided online by NASA was applied to predict the annual energy productions of two largest wind farms in Korea. The two wind farms, Gangwon wind farm and Yeongyang wind farm, are located on complex terrain. For the prediction, a commercial CFD program, WindSim, was used. The annual energy productions of the two wind farms were obtained for three separate years of MERRA data from June 2007 to May 2012, and the results were compared with the measured values listed in the CDM reports of the two wind farms. As the result, the prediction errors of six comparisons were within 9 percent when the availabilities of the wind farms were assumed to be 100 percent. Although further investigations are necessary, the MERRA reanalysis data seem useful tentatively to predict adjacent wind resources when measurement data are not available.