• 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.

• Journal of the Korean Solar Energy Society
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• v.32 no.3
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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.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.300-305
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• 2008

Using WindPRO that was software for windfarm design developed by EMD from Denmark, wind resources for the western Jeju island were analyzed, and the performance of WindPRO prediction was evaluated in detail. The Hansu site and the Yongdang site that were located in coastal region were selected, and wind data for one year at the two sites were analyzed using WindPRO. As a result, the relative error of the Prediction for annual energy Production and capacity factor was about ${\pm}20%$. For evaluating wind energy more accurately, it is necessary to obtain lots of wind data and real electric power production data from real windfarm.

• Advances in Energy Research
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• v.4 no.1
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• pp.1-28
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• 2016

A grid-connected photovoltaic (PV) system comprised of multicrystalline silicon (mc-Si) modules was installed in a cold climate region in the U.S. This roof-mounted stationary PV system is a real-world application of PV for building energy generation in International Energy Conservation Code (IECC) Climate Zone 5 (and possibly similar climate zones such as 6, 7 and 8), and it served the purposes of research, demonstration, and education. The importance of this work is highlighted by the fact that there has been less emphasis on solar PV system in this region of the U.S. because of climate and latitude challenges. The system is equipped with an extensive data acquisition system capable of collecting performance and meteorological data while visually displaying real-time and historical data through an interactive online interface. Experimental data was collected and analyzed for the system over a one-year period with the focus of the study being on measurements of power production, energy generation, and efficiency. The annual average daily solar insolation incident upon the array was found to be $4.37kWh/m^2$. During the first year of operation, the PV system provided 5,801 kWh (1,264 kWh/kWp) of usable AC electrical energy, and it was found to operate at an annual average conversion efficiency and PR of 10.6 percent and 0.79, respectively. The annual average DC to AC conversion efficiency of the inverter was found to be 94 percent.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.10
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• pp.24-33
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• 2008

Optimal design of the direct-driven PM Wind Generator, combined with DEAS(Dynamic Encoding Algorithm for Searches) and FEM(Finite Element Method), has been proposed to maximize the Annual Energy Production(AEP) over the whole wind speed characterized by the statistical model of wind speed distribution. In particular, DEAS contributes to reducing the excessive computing time for the optimization process.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.48-57
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• 2009

This paper presents optimal design of direct-driven PM wind generator using MADS (Mesh Adaptive Direct Search). Optimal design of the direct-driven PM Wind Generator, combined with MADS and FEM (Finite Element Method), has been performed to maximize the Annual Energy Production (AEP) over the whole wind speed characterized by the statistical model of the wind speed distribution. In particular, the newly applied MADS contributes to reducing the computation time when compared with Genetic Algorithm (GA) implemented with the parallel computing method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.4
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• pp.266-273
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• 2015

In this paper, annual wind data in 2014 at six locations(Seosudo, Gadaeam, Sibidongpa, Galmaeyeo, Haesuseo, Jigwido) are collected and analyzed in order to review optimal candidate site for offshore wind farm in the Western Seas of Korea. Observed wind data is fitted to Rayleigh and Weibull distribution and annual energy production is estimated according to wind frequency. GWE-3kH(3 kW-class) and GWE-10KU (10 kW-class) turbine are selected as wind turbine. Also, power curve are used to calculate wind energy potential. As a result, annual mean wind speed at six locations(Seosudo, Gadaeam, Sibidongpa, Galmaeyeo, Haesuseo, Jigwido) were calculated about 4.60, 4.5, 5.00, 5.13, 5.51, 5.90 m/s, respectively. In addition, annual energy production were estimated at 10,622.752, 11,313.05, 13,509.41, 14,899.55, 17,106.13, 19,660.85 kWh. Generally, annual mean energy density were between poor and marginal class and capacity factor at Jigwido was calculated at 22.44%. Its value is higher than the others.

• Journal of Korean Society of Forest Science
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• v.89 no.2
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• pp.232-240
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• 2000

Correlation between Songyi(Tricholoma matsutake, pine mushroom, matsutake) production and the annual pine tree growth in Korea was analyzed with 18 years data of the mushroom production in Sangju area and the annual ring-growth of pine trees at Mt Sogni in the area. The two parameters were not significantly related to each other(r=0.408). A possible reason of this low relationship is that September and October climate affected annual Songyi production through mushroom primordial formation, continued growth of the primordia, while May and June climate did the annual tree-ring growth. Songyi production at Mt. Wolak in Chungcheongbukdo peaked while the minimum daily air temperature ranged about $7^{\circ}C$ to $13^{\circ}C$ during the first week of October in 1999. These show that Songyi production variation is not a simple trend depending on the energy the pine trees have accumulated. Rather, controlling soil moisture and air temperature during Songyi fruiting season can be a significant management option for improving the mushroom production.

• New & Renewable Energy
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• v.10 no.1
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• pp.41-49
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• 2014

For a proper valuation of wind power project, it is necessary to consider volatilities of key parameters such as annual energy production, electricity sales price, and long term interest rate. Real option methodology allows to calculate option values of these parameters. Volatilities to be considered in wind project valuation are 1) annual energy production (AEP) estimation due to meteorological variation and estimation errors in wind speed distribution, 2) changes in system marginal price (SMP), and 3) interest rate fluctuation of project financing which provides refinancing option to be exercised during a loan tenor for commercial scale projects. Real option valuation turns out to be more than half of the sales value based on a case study for a FIT scheme wind project that was sold to a financial investor.

• Journal of Hydrogen and New Energy
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• v.25 no.6
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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.