• Korean Management Science Review
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• v.30 no.2
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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.

• 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.

• 한국해양학회지
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• v.26 no.3
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• pp.291-294
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• 1991

MAPP a computer program provides an estimate of the annual production of macroalgae. The calculation of the annual production is based on the Photosynthesis-Irradiance relationship under different temperature conditions and annual changes of algal biomass. The production in a given time was obtained from the multiplication of biomass by the production rate measured by in situ experiments. The annual production, $P_{yr}$, is calculated from $P_{yr}{\;}={\;}{\int}B_t{\cdot}P_r{\;}dt$, where, $P_t$ = f(T,L)and T, L = f(t). The program is written in Pascal language to facilitate the usage with personal computers. The data of the photosynthetic rates and biomass of Sargassum confusum measured at Ohori, on the east coast of Korea, was used for an example.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.177.1-177.1
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• 2010

Large offshore wind farms have actively been developed in order to meet the needs for wind energy since the land-based wind farms have almost been fully developed especially in Europe. The key problem for the construction of offshore wind farms may be on the high cost of energy compared to land-based ones. NREL (National Renewable Energy Laboratory) has developed a spreadsheet-based tool to estimate the cost of wind-generated electricity from both land-based and offshore wind turbines. Component formulas for various kinds and scales of wind turbines were made using available field data. Annual energy production has been estimated based on the Weibull probability distributions of wind. In this paper, this NREL estimation model is introduced and applied to the offshore wind turbines now under designing or in production in Korea, and the result is discussed.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.6
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• pp.481-485
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• 2016

When analyzing economic feasibility for installing a PV generation plant at a certain location, the prediction of possible annual power production at the site using the target PV panels should be conducted on the basis of the local weather data provided by a local weather forecasting office. In addition, the prediction of PV generating power under certain weather conditions is useful for fault diagnosis and performance evaluation of PV generation plants during actual operation. This study analyzes PV cell characteristics according to a variety of weather conditions, including ambient temperature and irradiance level. From the analysis and simulation results, this work establishes a proper model that can predict the output characteristics of PV cells under changes in weather conditions.

• The Journal of Fisheries Business Administration
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• v.25 no.2
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• pp.47-58
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• 1994

This paper tries to estimate cost and profit functions under Cobb - Douglas production technology in the coastal fishing, using duality theory of production technology and cost function. Therefore this paper estimates in advance the production functions with two input variables, the number of working persons per tonage(WEMP) and the number of fishing equipment per tonage(WEQU). Then this paper estimates profit function and implicit cost function using the estimated coefficients from production functions. The results of this study show that the annual average profit pertonage of long bag set fishing and trap fishing amount to 4.1 million won and 3.9 million won respectively.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.322-326
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• 2008

This paper presents an estimation method of small hydro power(SHP) potential using a SHP resource map. As a basic unit of SHP energy potential at a certain area, capacity and annual energy production of unit head was calculated from the catchment area given by a SHP resource map which was established by numerical hydrologic simulation so that a logical and relatively accurate potential estimation was possible comparing with the performance analysis model for SHP sites. The performance characteristics for Samok-Ri site were analyzed, using the SHP resource map and the developed model. It was found that the SHP resource map and the developed model is useful tool to estimate SHP potential.

• 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.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.9 no.4
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• pp.245-249
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• 1976

The present paper deals with estimation of zooplankton production in the South Sea of Korea based on the plankton data of the Annual Report of Oceanographic Observations, Fisheries Research and Development Agency, Korea during the period of seven years from 1967 through 1973. Net zooplankton biomass of the layer tipper 150 meters is calculated with an average of $70.2\;mg/m^3$ and gross production in the region $59,800\;km^2$ are about $5.14\~10.27\times10^6\;tons/year$. Mean zooplankton productivity is estimated $86\~172\;tons/km^2/year$.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.6
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• pp.1629-1639
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• 2016

This study is to estimate the production efficiency of abalone aquaculture and to find its determinants utilizing the survey data of operating expenses in 2015. The first part of the analysis applied both DEA and Super-DEA for the estimation of efficiency of each aquaculture household as DMU. We used wages, feeding costs and area as inputs and annual profits and sales as outputs of the model. The second part of the study applied both Tobit and OLS for the identification of determinants of the efficiency. We investigated cost-ratio, depreciation costs, careers, value of living seeds, cleaning costs of farming ground and a ratio of 1 and 2 year-old abalone at shipment as potential determinants. The estimation results show us that the average technical efficiency, pure technical efficiency, and scale efficiency score turn out to be 72%, 81% and 85% respectively. The Super-BCC and Super-CCR models reveal their average efficiency scores as 81% and 80%. All of the variables used to identify the determinants of the efficiency. The study results suggests that the production efficiency can be improved by cleaning farming ground and hence lowering the death rate of seeds.