• Journal of the Korean Solar Energy Society
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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.

• Journal of the Korean Solar Energy Society
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• 제33권5호
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• pp.1-8
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• 2013

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and -9.57%~9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within ${\pm}10%$.

• The Journal of the Korea institute of electronic communication sciences
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• 제14권1호
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• pp.155-160
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• 2019

In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.

• Journal of Energy Engineering
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• 제21권2호
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• pp.124-132
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• 2012

This study presents the simulation results of Photopia when a lecture room with north-facing windows were illuminated by two different types of daylighting systems to improve the imbalance in its lighting conditions. Especially, the candela power distribution curves (CDCs) on a clear sunny day at the summer solstice, reaching $80^{\circ}$ in solar altitude, were analyzed with respect to the illuminance available at task areas (work planes). The difference between its illuminance on the north and south areas exceeded 1,000 lux without any daylighting system. This, however, decreased drastically with the application of a daylighting system. When a light tube system was introduced, it reduced from 906lx to 603lx and, even further to 308lx with the application of a dish concentrator system. Generally, the performance of a light tube system was greatly influenced by solar altitude while its effect on the dish concentrator system was rather negligible.

• The Journal of the Korea institute of electronic communication sciences
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• 제17권2호
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• pp.299-308
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• 2022

Recent, energy transition policies are driving to increase in the number of small photovoltaic(PV) generators. It is difficult for system operators to accurately anticipate the amount of power generated from such small scale PV generation, and this may disrupt dispatch schedules and result in an increase in cost. The need for a Virtual Power Plant(VPP) is emerging as a way of resolving these problems, as it would integrate small-scale PV plants and eliminate uncertainty about the amount of power generated, control voltage, and provide power reserves. In this paper, the cost evaluation methods are described for determination of VPP cloud charges both Net Present Value(NPV) method and Profitability Index(PI) method, the calculated outcomes of the two types of cost evaluation methods are presented in detail. It seems we secure profitability as we get 1.22 of profitability index from calculation results, it may be attractive for the aggregator as NPV is enough for satisfying profitability.

• Journal of Technology Innovation
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• 제22권2호
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• pp.185-220
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• 2014

The purpose of this study is to get a whole picture of financial conditions of the new and renewable energy sector which have been growing rapidly and predict bankruptcy risk quantitatively. There have been many researches on the methodologies for company failure prediction, such as financial ratios as predictors of failure, analysis of corporate governance, risk factors and survival analysis, and others. The research method for this study is Altman Z-score which has been widely used in the world. Data Set was composed of 121 companies with financial statements from KIS-Value. Covering period for the analysis of the data set is from the year 2006 to 2011. As a result of this study, we found that 38 percent of the data set belongs to "Distress" Zone (on alert) while 38% (on watch), summed into 76%, whose level could be interpreted to doubt about the sustainability. The average of the SMEs in wind energy sector was worse than that of SMEs in solar energy sector. And the average of the SMEs in the "Distress" Zone (on alert) was worse than that of the companies of large group in the "Distress" Zone (on alert). In conclusion, Altman Z-score was well proved to be effective for New & Renewable Energy Industry in Korea as a result of this study. The importance of this study lies on the result to demonstrate empirically that the majority of solar and wind enterprises are facing the risk of bankruptcy. And it is also meaningful to have studied the relationship between SMEs and large companies in addition to advancing research on new start-up companies.

• Journal of the Korean Solar Energy Society
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• 제26권4호
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• pp.1-7
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• 2006

This study is to develop a model to predict the soil temperature variation in Korea Institute of Energy Research using its thermal properties, such as thermal conductivity and diffusivity. Soil depth temperature variation is very important in the design of a proper Ground Source Heat Pump (GSHP) system. This is because the size of the borehole depends on the soil temperature distribution, and this can decrease GSHP system cost. If the thermal diffusivity and thermal conductivity are known, the soil temperature can be predicted by either the Krarti equation or the Spitler equation. Then a comparison with the Krarti equation and Spitler equation data with the real measured data can be performed. Also, the thermal properties can be reasonably approximated by performing a fit of the Krarti and Spitler equations with measured temperature data. This was done and, as a result, the Krarti equation and Spitler equation predicted values very close to the measured data. Although there is about a $0.5^{\circ}C$ difference between the deep subsurface prediction (16m - 60m), with this equation, were expected to have model this Non-Homogeneous Soil Temperature phenomenon properly. So, it has been shown that a prediction of non-homogeneous soil temperature variation influenced by solar radiation can be achieved with a model.

• Journal of the Korean Solar Energy Society
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• 제36권1호
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• pp.27-37
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• 2016

A simple but practical Ensemble Prediction System(EPS) for wind power forecasting was developed and evaluated using the measurement of the offshore meteorological tower, HeMOSU-1(Herald of Meteorological and Oceanographic Special Unite-1) installed at the Southwest Offshore in South Korea. The EPS developed by the Korea Institute of Energy Research is based on a simple ensemble mean of two Numerical Weather Prediction(NWP) models, WRF-NMM and WRF-ARW. In addition, the Kalman Filter is applied for real-time quality improvement of wind ensembles. All forecasts with EPS were analyzed in comparison with the HeMOSU-1 measurements at 97 m above sea level during Typhoon Bolaven episode in August 2012. The results indicate that EPS was in the best agreement with the in-situ measurement regarding (peak) wind speed and cut-out speed incidence. The RMSE of wind speed was 1.44 m/s while the incidence time lag of cut-out wind speed was 0 hour, which means that the EPS properly predicted a development and its movement. The duration of cut-out wind speed period by the EPS was also acceptable. This study is anticipated to provide a useful quantitative guide and information for a large-scale offshore wind farm operation in the decision making of wind turbine control especially during a typhoon episode.

• Proceedings of the Korea Water Resources Association Conference
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• 한국수자원학회 2022년도 학술발표회
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• pp.72-72
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• 2022

전 세계적으로 기후변화로 인한 기상재해의 발생 빈도가 증가하고 있다. 특히 기후변화로 인한 기온상승은 사계절이 뚜렷한 우리나라의 기후도 동남아와 같은 아열대 기후로 변하고 있는 추세이다. 기후변화 전망보고서에 따르면는 우리나라의 연 강우량이 현재(1,491mm)보다 약 11% 증가(1,658mm) 하고, 연평균기온이 현재 대비 2040년대 0.7℃, 2090년대 3.1℃ 상승할 것으로 전망했다. 기후변화에 의한 여름철 기온 상승과 겨울철 기온 하강은 에너지 소비량과 소비 패턴 변화를 유발하고 에너지 수요와 공급 불일치의 원인이 된다. 이에 정부에서는 기후변화에 적응하기 위해 화석연료 기반의 에너지 생산에서 그린에너지를 이용한 에너지 생산으로 전환이 효과적이라고 공표하였다. 이어 2050년까지 탄소중립 달성을 위해 신재생에너지르 통한 도전과제를 제시하였으며, 기업 및 공공기관의 RE100참여를 확대하고 활용 가능한 유망 재생에너지원을 발굴을 목표로 하고 있다. 이에 본 연구팀은 국가 기상재해·그린에너지 빅데이터 센터를 설립하여 정부의 다양한 이행수단의 근거 데이터를 제공하고, 민·관에서 활용 할 수 있는 그린에너지 데이터를 제공하고자 한다. 본 센터에서는 침수예측데이터, 풍력, 태양광, 소수력, 수열 잠재 에너지 데이터를 생산하고 있으며, 각 데이터에 대한 활용 및 서비스 방안을 소개하고자 한다.

• Journal of Intelligence and Information Systems
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• 제29권4호
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.