• Journal of the Economic Geographical Society of Korea
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• v.15 no.3
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• pp.437-455
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• 2012

Korea has recently constructed a number of renewable photovoltaic power plants in Jeolla province as an effort to provide environment-friendly energy. However, several problems appeared in the power plant locations because they were not appropriately chosen ignoring social-environmental perspectives. Consequently, locations of both currently existing photovoltaic power plants require an social and environmental evaluations. This study aims to provide appropriate photovoltaic power plants locations and evaluation of current photovoltaic power plants in Jeolla province. By presenting location analysis of photovoltaic power plants, this study would minimize environmental and social side effects regarding photovoltaic power plants. Kriging and Analytic Network Process (ANP) are applied as methodology. ANP generates correct weights in combining spatial data, so that the result would present optimal locations. In addition environmentally sensitive regions were excluded in the analysis process. The results show that South and West coastal areas have a number of appropriate locations for photovoltaic power plants. In addition, evaluating currently running photovoltaic power plant locations, total 23 out 81 are turned out to be inappropriately located. This study is expected to contribute avoiding social and environmental conflicts in photovoltaic power plant locations and present criteria in evaluating photovoltaic power plants.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.591-596
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• 2012

Recently, the efficient management system for photovoltaic power plants has been required due to the continuously increasing construction of photovoltaic power plants. In this paper, we propose a cloud-based intelligent management system for many photovoltaic power plants. The proposed system stores the measured data of power plants using Hadoop HBase which is a column-oriented database, and processes the calculations of performance, efficiency, and prediction the amount of power generation by parallel processing based on Map-Reduce model. And, Web-based data visualization module allows the administrator to provide information in various forms.

• Current Photovoltaic Research
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• v.4 no.2
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• pp.80-86
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• 2016

Since the last five years it has become a lot of solar power plants installed. However, by installing the large-scale solar power station it is not easy to predict the actual generation years. Because there are a variety of factors, such as changes daily solar radiation, temperature and humidity. If the power output can be measured accurately it predicts profits also we can measure efficiency for solar power plants precisely. Therefore, Prediction of power generation is forecast to be a useful research field. In this study, out discovering the factors that can improve the accuracy of the prediction of the photovoltaic power generation presents the means to apply them to the power generation amount prediction.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.65.1-65.1
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• 2010

KPX(Korea Power Exchange) has been supervising FIT(Feed-in-tariff) for renewable energy power plants and supported 289MW photovoltaic power plants with Electric Power Industry Basis Fund in 2009. In this paper, we'll analyze utilization factor of these PV power plants in 2009 and for the latest 3 years and finally utilization factor of other renewable energy power plants in 2009.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• Bulletin of the Korea Photovoltaic Society
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• v.4 no.2
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• pp.45-53
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• 2018

Natural gas demand for power generation continued to increase until 2013 due to the expansion of large-scale LNG power plants after the black-out of 2011. However, natural gas demand for power generation has decreased sharply due to the increase of nuclear power and coal power generation. But demand for power generation has increased again as energy policies have changed, such as reducing nuclear power and coal power plants, and abnormal high temperatures and cold waves have occurred. If the gas pipeline pressure can be properly maintained by predicting these fluctuations, it can contribute to enhancement of operation efficiency by minimizing the operation time of facilities required for production and supply. In this study, we have developed a regression model with daily power demand and base power generation capacity as explanatory variables considering characteristics by day of week. The model was constructed using data from January 2013 to December 2016, and it was confirmed that the error rate was 4.12% and the error rate in the 90th percentile was below 8.85%.

• Journal of Environmental Science International
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• v.27 no.3
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• pp.227-231
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• 2018

We studied the warming effect induced by Photovoltaic(PV) power plants in rural areas during summer daytime using a simple analytical urban meteorological model. This analysis was based on observed meteorological elements and the capacity of the PV power plant was 10 MWp. The major axis length of the PV power plant was assumed to be 1km. Data of the necessary meteorological elements were obtained from a special meteorological observation campaign established for a over a PV power plant. We assumed that the wind flowed along the major axis of the PV power plant(1 km). As a result, the air temperature on the downwind side of the PV power plant was estimated to invrease by about $0.47^{\circ}C$.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.351-354
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• 2009

The solar photovoltaic power generator is more important than other renewable energy. Because The solar photovoltaic power generator has been commercialized. So the solar photovoltaic power plants have been constructed. The photovoltaic module lifetime is estimated about 20 year. But The results can not be trusted Because It did not test in the korea. In this paper, We test the maximum power of three modules used 23 years.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.96-105
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• 2009

Photovoltaic array distance will be calculated that can have maximum output through the rearrangement of photovoltaic array by comparing and analyzing the power output decrease from the shadows of photovoltaic arrays according to the consideration of incidence angle that is not suitable to the situation of our country among the causes of power output decrease by sampling photovoltaic power generation plants. When relocate simulation result photovoltaic array 1 line, when relocate about 2.8[%], photovoltaic array 3 lines when relocate about 1.2[%], photovoltaic array 2 lines, output of about 5.0[%] improved. Also, gained space gains of photovoltaic array party 1.29[m] when relocate photovoltaic array party 0.82[m], 3 lines when relocate photovoltaic array party 0.39[m], 2 lines when relocate 1 line. As gains occur on photovoltaic array clearance, time that receive effect of shade was slowed down gradually and showed result that elevation of output shows finally.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.559-568
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• 2016

For the economic management of photovoltaic power plants, it is necessary to regularly monitor the panels within the plants to detect malfunctions. Thermal infrared image cameras are generally used for monitoring, since malfunctioning panels emit higher temperatures compared to those that are functioning. Recently, technologies that observe photovoltaic arrays by mounting thermal infrared cameras on UAVs (Unmanned Aerial Vehicle) are being developed for the efficient monitoring of large-scale photovoltaic power plants. However, the technologies developed until now have had the shortcomings of having to analyze the images manually to detect malfunctioning panels, which is time-consuming. In this paper, we propose an automatic photovoltaic panel area extraction algorithm for thermal infrared images acquired via a UAV. In the thermal infrared images, panel boundaries are presented as obvious linear features, and the panels are regularly arranged. Therefore, we exaggerate the linear features with a vertical and horizontal filtering algorithm, and apply a modified hierarchical histogram clustering method to extract candidates of panel boundaries. Among the candidates, initial panel areas are extracted by exclusion editing with the results of the photovoltaic array area detection. In this step, thresholding and image morphological algorithms are applied. Finally, panel areas are refined with the geometry of the surrounding panels. The accuracy of the results is evaluated quantitatively by manually digitized data, and a mean completeness of 95.0%, a mean correctness of 96.9%, and mean quality of 92.1 percent are obtained with the proposed algorithm.