• 한국태양에너지학회 논문집
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• 제33권6호
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• pp.32-38
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• 2013

In an efforts to encourage renewable energy deployment, the government has initiated so called 1 million green homes program but the accumulated installation capacity of small wind turbine has been about 70kW. It can be explained in several ways such that current subsidy program does not meet public expectations, economic feasibility of wind energy is in doubt or acoustic emission is significant etc. The author investigated annual energy production of Skystream 3.7 wind turbine using measured power curve and wind resource data. The measured power curve of the small wind turbine was obtained through power performance tests at Wol-Ryoung test site. AEP(Annual Energy Production) and CF(Capacity Factor) were evaluated at selected locations with the measured power curve.

• 한국태양에너지학회 논문집
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• 제31권4호
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• pp.19-26
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• 2011

We are carried out power performance testing for 3MW wind turbine system at Je-ju wind turbine testing Site and analyzed measured data which was stored through monitoring system. In this paper, we described the power performance testing results and analyzed an uncertainty of measured data sets. The power curve with measured power data is closely coincide with designed power curve except for the low wind speed sections(4m/s~7m/s) and the annual energy production which is given Ray leigh distribution was included with 1.5~5.9% of uncertainty in the wind speed region as 4~11m/s. Although the deviation of curve between measured power and designed power is high, the difference of annual energy production is low in the low wind speed region.

• The Korean Journal of Ecology
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• 제9권1호
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• pp.59-71
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• 1986

Net production, dead material increments were measured, and annual respiration loss was simulated through a year to determine the gross production at the Phragmites longivalvis, Carex scabrifolia and Zoysia sinica stand on Okryudeung, a sand bar of the Nagdong river estuary. The maximum live biomass for above-ground organs of the three stands occured in October, i.e., 1, 985, 744 and 1, 013g/m2, and belowground net productions were estimated to be 650, 440 and 412g/m2, respectively. Materials died or shedding from live aboveground organs during the growth season were estimated to be 167, 81 and 0 g/$m^2$. From the results of simulation, annual variation of respiratiion was primarily dependent on the annual variation of temperature through a year. For annual respiration loss in three stands, 21.893, 6.147 and 5.036kg $CO_2/m^2$ were calculated, respectively. Corresponding gross productions were 72, 203, 22, 109 and 19, 909kcal/$m^2$. Respiration of belowground organs corresponded to 65%, 66% and 37% of the total plant respiration, and annual respiration loss accounted for 85%, 78% and 71% of the annual gross production. In view of efficiency of solar energy utilization, 5.8%, 1.8% and 1.6% of incident light energy were converted to gross production of plants during a year. With incident light energy during the growth season from April to September, energy utilizations for net production were estimated to be 1.2%, 0.4% and 0.6% at the three stands.

• 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.

• 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.

• 한국태양에너지학회 논문집
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• 제32권3호
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• pp.33-41
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• 2012

As the wind farms in large scale demand enormous amount of construction cost, minimizing the economic burden is essential and also it is very important to measure the wind resources and forecast annual energy production correctly to judge the economic feasibility of the proposed site by way of installing a Met mast at or nearby the site. Wind resources were measured by installing a 80[m] high Met mast at WangdeungYeo Island to conduct the research incorporated in this paper and offshore wind farm was designed using WindPRO. Wind farm of 100[MW] was designed making use of 3 and 4.5[MW] wind generator at the place selected to compare their annual energy production and capacity factor applying the loss factor of 10[%] and 20[%] respectively to each farm. As a result, 336,599[MWh] was generated by applying 3[MW] wind generator while 358,565 [MWh] was produced by 4.5[MW] wind generator. Difference in the energy production by 3[MW] generator was 33,660 [MWh] according to the loss factor with the difference in its capacity factor by 3.8[%]. On the other hand, 23 units of 4.5 [MW] wind generators showed the difference of annual energy production by 35,857 [MWh] with 4.0[%] capacity factor difference.

• 경영과학
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• 제30권2호
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• pp.1-10
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• 2013

Case studies are made to investigate the relationship between the accuracy of energy production estimation and project feasibility indicators such as rate of return on equity (ROE) and debt service coverage ratio (DSCR) for three wind farm projects. It is found out that 1% improvement in the accuracy of energy production estimation may enhance the ROE by more than 0.5% in the case of P95, thanks to improved financing terms. AHP survey shows that MCP correlation of measured in situ wind data with long term wind speed distribution and hands-on experiences of flow analysis are more important than other factors for more precise annual energy production estimation.

• 한국태양에너지학회 논문집
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• 제35권3호
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• pp.17-25
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• 2015

The main objective of this study is to predict the wind power generation at the wind farm using various wake models. Modeling of wind farm is a prerequisite for prediction of annual energy production at the wind farm. In this study, we modeled 20 MW class Seongsan wind farm which has 10 wind turbines located at the eastern part of Jeju Island. WindSim based on the computational fluid dynamics was adopted for the estimation of power generation. The power curve and thrust coefficient with meteorology file were prepared for wind farm modelling. The meteorology file was produced based on the measured data of the Korea Wind Atlas provided by Korea Institute of Energy Research. Three types of wake models such as Jensen, Larsen, and Ishihara et al. wake models were applied to investigate the wake effects. From the result, Jensen and Ishihara wake models show nearly the same value of power generation whereas the Larsen wake model shows the largest value. New positions of wind turbines are proposed to reduce the wake loss, and to increase the annual energy production of the wind farm.

• KIEAE Journal
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• 제16권3호
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• pp.113-119
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• 2016

Purpose: In this study, we evaluate the annual energy performance of the detached house which was designed with the aim of zero energy. Method: The experimental house which was constructed in Gonju Chungnam in 2013, is the single family detached house of light weight wood frame with $100m^2$ of heating area. Thermal transmittance of roof (by ISO 10211) and building external walls are designed as $0.10W/m^2K$ and $0.14W/m^2$ respectively and low-e coating vacuum window glazing with PVC frame was installed. Also grid connected PV system and natural-circulation solar water heater was applied and 6kWp capacity of photovoltaic module was installed in pitched roof and $5m^2$ of solar collector in vertical wall facing the south. We analyzed the 2014 annual data of the detached house in which residents were actually living, measured though web-based remote monitoring system. Result: First, as a result, total annual energy consumption and energy production (PV generation and solar hot water) are 7,919kWh and 7,689kWh respectively and the rate of energy independence is 97.1% which is almost close to the zero energy. Second, plug load and hot water of energy consumption by category showed the highest numbers each with 33% and 31%, with following space heating 24%, electric cooker 8%, lighting 3% in order. Hot water supply is relatively higher than space heating because high insulation makes it decreased.

• 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.