• Korean Journal of Organic Agriculture
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• v.20 no.3
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• pp.267-282
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• 2012

In this study, RPS system, one of the renewable energy support systems, is utilized for economic analysis of solar generation equipment and the fuel cost savings plan for controlled horticulture farms with high fuel-cost dependency and facility applicability were evaluated. On the exterior of the upper layer of glass greenhouse (9917$m^2$) of controlled horticulture farms using bunker C oil, half of the area (4958$m^2$) was utilized for theoretical installation and operation of 450kW-level solar power generator, and as the result, first, the effect of investment cost only of solar generation system was found to be quite excellent, but it was analyzed that there were limits to saving the fuel costs of the controlled horticulture farms. Second, when geothermal system was first introduced in the farm and solar system was additionally introduced, it was analyzed that the effect of introducing solar system was excellent. In order to apply such effects to the sites of farming, partial supplementation of RPS system which is being uniformly applied regardless of the purpose of renewable energy is necessary. When the subject of use directly install facilities where it is directly connected to national added-value such as food security created by the farming industry, it is necessary to introduce appropriate system that corresponds to such. Moreover, it was studied that the quick development of demonstrative complex that can practically evaluate the applicability of renewable energy in farming industry and interest and preparation of related institutions in financial support structure for its site application would lead to success.

• Current Photovoltaic Research
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• v.10 no.4
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• pp.116-120
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• 2022

Solar energy is rapidly being utilized to generate power in Europe and other countries, but the environmental effect of building and operating solar farms is not fully understood. The building of a solar park demands the removal of certain vegetation and the leveling of the land. Solar energy infrastructure may involve considerable landscape change, altering soil biological processes and influencing hydrologic, carbon and vegetative dynamics. To rebuild the solar PV facilities soils, inherent plant fields might require to be re-established. Within the scope of this research, we presented an analysis of the effects that were caused by the solar farm.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.60-68
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• 2013

This paper presents the characteristics of lightning over established and scheduled wind farms of Jeju island as well as over specific range of entire Jeju Island. The lightning data for 5 years from 2008 to 2012 was obtained from IMPACT ESP which detects lightning. Lightning frequency, lightning strength and regional lightning events were analyzed in detail, and then the lightning maps of Jeju Island were created. The evaluation of lightning rate was made for all the wind farms of this study. Damage to wind turbines by lightning was found in the existing wind farms. As a result, the eastern part of Jeju Island had more lightning frequency than the western part of the Island. Also, the evaluation of lightning rate was good for all established and scheduled wind farms of Jeju Island. Hankyung is the best place for lightning safety, while precaution should be taken against lightning damage in Kimnyung. Lightning damage to wind turbines occurred in Samdal and Haengwon wind farms, which had the first and the second highest lightning rate of the five existing wind farms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.505-514
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• 2015

This paper proposes a probabilistic generation assessment model of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The proposed numerical analysis method assesses the one day-ahead generation by combining equivalent generation characteristics function and probabilistic distribution function of wind speed(WS) and solar radiation(SR) resources. The equivalent generation functions(EGFs) of the wind and solar farms are established by grouping a lot of the farms appropriately centered on Weather Measurement Station(WMS). First, the EGFs are assessed by using regression analysis method based on typical least square method from the recorded actual generation data and historical resources(WS and SR). Second, the generation of the REGs is assessed by adding the one day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials using the regression analysis. Third, a Renewable Energy Generation Assessment System(REGAS) including D/B of recorded actual generation data and historical resources is developed using the model and algorithm predicting one day-ahead power output of renewable energy generators.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1023-1025
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• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.

• Journal of the Korean Solar Energy Society
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• v.34 no.4
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• pp.67-74
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• 2014

The fluctuation of the output power of wind farms will be able to cause the impact on the Jeju power system such as power quality and stability. To settle the matter, many researchers have proposed the use of the BESS(Battery Energy Storage System) in the wind farm. In this paper, The BESS is applied to each wind farms for mitigating the fluctuation of wind power output. The BESS is controlled for smoothing the output of wind farms. Two kinds of simulation will be carried out. First, the simulation results by using PSCAD/EMTDC simulation program are compared to the measured data from the real power grid in Jeju Island. The other is to analyze the output of wind farms when the BESS is applied to the simulation works. The simulation results will demonstrate the effectiveness of using BESS to stabilize for power grid in Jeju Island.

• Journal of the Korean Solar Energy Society
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• v.34 no.2
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• pp.82-90
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• 2014

The MERRA reanalysis data provided online by NASA was applied to predict the annual energy productions of two largest wind farms in Korea. The two wind farms, Gangwon wind farm and Yeongyang wind farm, are located on complex terrain. For the prediction, a commercial CFD program, WindSim, was used. The annual energy productions of the two wind farms were obtained for three separate years of MERRA data from June 2007 to May 2012, and the results were compared with the measured values listed in the CDM reports of the two wind farms. As the result, the prediction errors of six comparisons were within 9 percent when the availabilities of the wind farms were assumed to be 100 percent. Although further investigations are necessary, the MERRA reanalysis data seem useful tentatively to predict adjacent wind resources when measurement data are not available.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.39-45
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• 2013

This paper presents the estimation of minimum BESS capacity for regulating the output of wind farms considering power grid operating condition in Jeju Island. To analyze the characteristics of wind farm outputs with a BESS, the real data of wind farms, Sung-San, Sam-dal and Hang-Won wind farm, located in the eastern part of Jeju island is considered. The wind farms are connected to Sung-san substation to transfer the electric power to Jeju power grid. Consequently, at PCC (Point of Common Coupling), it can see a huge wind farm connected to the substation and thus it can be expected that the smoothing effect is affected by not only the different wind speeds for each area but also the different mechanical inertia of wind turbines. In this paper, two kinds of simulation have been carried out. One is to analyze the real data of wind farm outputs during a winter season, and the other is to connect a virtual BESS to eliminate the unintended generating power changes by the uncontrolled wind farm outputs as shown in the former data. In the conclusion, two kinds of simulation results show that BESS installed in the substation is more efficient than each wind farms with BESS, respectively.

• Journal of the Korean Solar Energy Society
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• v.40 no.1
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• pp.1-13
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• 2020

A study on estimation of the Levelized Cost of Energy (LCOE) was conducted for the Korean onshore wind farms. The LCOE was estimated on the basis of the actual wind farm data from Data Analysis, Retrieval Transfer system (DART) run by Financial Supervisory Service. Recently, social discount rate of Korea dropped from 5.5% to 4.5%, which was taken into account for this study. The onshore wind farms studied accounted for 42% of all the onshore wind farms of South Korea. Capital Expenditure (CapEx) and Operation Expenditure (OpEx) were calculated from the actual data, while Capacity Factors (CFs) were obtained from the wind farms of five provinces. Their distributions were estimated using Maximum Likelihood Estimation method, and then Monte Carlo Simulation (MCS) was performed for estimating LCOE, Levelized Fixed Cost (LFC), and Levelized Variable Cost (LVC). As a result, the LCOEs at the two discount rates, 4.5 and 5.5%, were 142 and 152 $/MWh, respectively, which were lower than that of financially viable onshore wind project of Korea. The 1% drop of social discount rate was estimated to result in a 10 $/MWh decrease in LCOE and a 4 $/MWh in LFC, which can be an advantage for wind project investors.

• Korean Management Science Review
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• v.30 no.2
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• pp.97-106
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• 2013

Of the new and renewable energies currently being pursued domestically, wind energy, together with solar photovoltaic energy, is a new core growth driver industry of Korea. As of May 2012, 33 wind farms at a capacity of 347.8MW are in operation domestically. The purpose of this study was to compare and analyze how efficiently each operational wind farm is utilizing its power generation capacity and the weather resource of wind. For this purpose, the study proceeded in 3 phases. In phase 1, ANOVA analysis was performed for each wind farm, thereby categorizing farms according to capacity, region, generator manufacturer, and quantity of weather resources available and comparing and analyzing the differences among their operating efficiency. In phase 2, for comparative analysis of the operating efficiency of each farm, Data Envelopment Analysis (DEA) was used to calculate the efficiency index of individual farms. In the final phase, phase 3, regression analysis was used to analyze the effects of weather resources and the operating efficiency of the wind farm on the power generation per unit equipment. Results shows that for wind power generation, only a few farms had relatively high levels of operating efficiency, with most having low efficiency. Regression analysis showed that for wind farms, a 1 hour increase in wind speeds of at least 3m/s resulted in an average increase of 0.0000045MWh in power generation per 1MW generator equipment capacity, and a unit increase in the efficiency scale was found to result in approximately 0.20MWh power generation improvement per unit equipment.