• Journal of Digital Convergence
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• v.17 no.7
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• pp.131-138
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• 2019

The world's electricity production ratio is 40% for coal, 20% for natural gas, 16% for hydroelectric power, 15% for nuclear power and 6% for petroleum. Fossil fuels also cause serious problems in terms of price and supply because of the high concentration of resources on the earth. Solar energy is attracting attention as a next-generation eco-friendly energy that will replace fossil fuels with these problems. In this study, we test the charge-operation plan and the discharge operation plan for peak-cut operation by applying the maximum power demand reduction simulation. To do this, we selected the electricity usage from November to February, which has the largest amount of power usage, and applied charge / discharge logic. Simulation results show that the contract power decreases as the peak demand power after the ESS Peak-cut service is reduced to 50% of the peak-target power. As a result, the contract power reduction can reduce the basic power value of the customer and not only the economic superiority can be expected, but also contribute to the improvement of the electric quality and stabilization of the power supply system.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.1-16
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• 2019

Recently, since responding to meteorological changes depending on increasing greenhouse gas and electricity demand, the importance prediction of photovoltaic power (PV) is rapidly increasing. In particular, the prediction of PV power generation may help to determine a reasonable price of electricity, and solve the problem addressed such as a system stability and electricity production balance. However, since the dynamic changes of meteorological values such as solar radiation, cloudiness, and temperature, and seasonal changes, the accurate long-term PV power prediction is significantly challenging. Therefore, in this paper, we propose PV power prediction model based on deep learning that can be improved the PV power prediction performance by learning to use meteorological and seasonal information. We evaluate the performances using the proposed model compared to seasonal ARIMA (S-ARIMA) model, which is one of the typical time series methods, and ANN model, which is one hidden layer. As the experiment results using real-world dataset, the proposed model shows the best performance. It means that the proposed model shows positive impact on improving the PV power forecast performance.

• Current Photovoltaic Research
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• v.2 no.4
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• pp.182-188
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• 2014

For the dissemination of new and renewable energy, Korean government introduced a renewable portfolio standard (RPS) scheme in 2012 after terminating feed-in tariff (FIT) scheme that was introduced in 2004. With the RPS scheme, 64.7% of its own goal (95.7% in PV and 63.3% in non-PV) was achieved in 2012 and 67.2% of that (94.9% in PV, 65% in non-PV) was achieved in 2013. The deployment of PV systems met the goal very well and that of non-PV did not. Recently, Korean government revised the target year of supplying 10% electricity from new and renewable energy from 2022 to 2024 and released a couple of measures on PV area. Recent studies showed that the bankability of a project plays a key role for PV dissemination. Therefore, the dissemination should be assessed from the point of bankability under the RPS scheme and a little adjustment is necessary to achieve the goal. Especially, installing a small size PV (<100 kwp) system needs a minimum REC price or a FIT scheme. In non-PV area, permission process is a common bottleneck and the related regulation should be eased. In addition, to achieve the long term goal, an implementing scenario has to be prepared. Currently, the portion of the waste-gas energy originated from fossil fuel is too large among the new and renewable energy sources and the portion should be lowered or eliminated in the 10% of electricity supply goal. Seoul Metropolitan Government (SMG) has its own FIT scheme for PV dissemination from 2014 SMG and revised the PV tariff from 50 to 100 won/kwh in effective of 2015. It is worth to spread the other provinces.

• Clean Technology
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• v.24 no.4
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• pp.357-364
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• 2018

Studies on the production of biogas from third generation biomass, such as micro- and macroalgae, have been conducted through experiments of various scales. In this paper, we investigated the feasibility of commercialization of integrated combined heat and power (CHP) production using biogas derived from macroalgae, i.e., seaweed biomass. For this purpose, an integrated CHP plant of industrial scale, consisting of solid oxide fuel cells, gas turbine and organic Rankine cycle, was designed and simulated using a commercial process simulator. The cost of each equipment in the plant was estimated through the calculated heat and mass balances from simulation and then the techno-economic analysis was performed. The designed integrated CHP process produces 68.4 MW of power using $36ton\;h^{-1}$ of biogas from $62.5ton\;h^{-1}$ (dry basis) of brown algae. Based on these results, various scenarios were evaluated economically and the levelized electricity cost (LEC) was calculated. When the lifetime of SOFC is 5 years and its stack price is $$225kW^{-1}$, the LEC was 12.26 ¢ $kWh^{-1}$, which is comparable to the conventional fixed power generation.

• Journal of the Korean Society of Safety
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• v.22 no.3 s.81
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• pp.7-12
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• 2007

This paper presents the development of an electrical fire detection system using digital temperature and current detectors in order to sound for electrical fire in advance. As the demand for electricity is increasing and industrial facilities are getting more complex and larger in size, the losses of human life and property are on the increase by electrical fires. In order to prevent electrical fires, it is required to find out fire signatures, or electric signal of the overcurrent and overheating. Therefore, in this paper, developed is an electrical fire detection system based on the detection of signal for overcurrent and overheating to prevent electrical accidents in advance that happen in electrical wires. The developed system gives an alarm by computer monitor, speaker system and mobile phone before electrical fires occur and give severe damages to human beings and properties, and the system can be implemented and supplied for business and residental buildings at a low price. The usefulness and validity of the system, also, verified in this paper by case study and experiments.

• Nuclear Engineering and Technology
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• v.50 no.3
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• pp.488-493
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• 2018

The Firebrick Resistance-Heated Energy Storage (FIRES) concept developed by the Massachusetts Institute of Technology aims to enhance profitability of the nuclear power industry in the next decades. Studies carried out at Massachusetts Institute of Technology already provide estimates of the potential revenue from FIRES system when it is applied to industrial heat supply, the likely first application. Here, we investigate the possibility of operating a power plant (PP) with a fluoride-salt-cooled high-temperature reactor and a closed Brayton cycle. This variant offers features such as enhanced nuclear safety as well as flexibility in design of the PP but also radically changes the way of operating the PP. This exploratory study provides estimates of the revenue generated by FIRES in addition to the nominal revenue of the stand-alone fluoride-salt-cooled high-temperature reactor, which are useful for defining an initial design. The electricity price data is based on the day-ahead markets of Germany/Austria and the United States (Iowa). The proposed method derives from the equation of revenue introduced in this study and involves simple computations using MatLab to compute the estimates. Results show variable economic potential depending on the host grid but stress a high profitability in both regions.

• Journal of Environmental Policy
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• v.9 no.1
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• pp.57-81
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• 2010

Since the Republic of Korea is highly dependent on fossil fuels despite high oil prices, it urgently needs to renew its economic and social system to cut carbon emissions and achieve green growth. Therefore, reforming or eliminating subsidies related to the use of fossil fuels is a timely and oppropriate policy recommendation for Korea. It would be a win-win deal for Korean society as it would not only reduce the use of environmentally harmful fossil fuels but also enhance economic efficiency. In particular, cross-subsidies for industrial, agricultural and night thermal-storage power services make up more than 80 percent of all subsidies provided to the entire electric power industry sector of Korea. Of these cross-subsidies, this paper analyzes the electricity subsidy for industries, which takes up the largest share (about KRW 1.6583 trillion yearly), among the environmentally harmful subsidies in the electric power sector. Thus, the paper focuses on the analysis of ripple effect anticipated when this is reformed. To examine the effects of this subsidy reform, price elasticities were estimated using the ARDL (autoregressive distributed lag) model and quarterly data from 1990 to 2007. The main results of this study show that 1) annual energy demand for electric power in the industrial sector would drop by 12,475,930MWh and 2) $CO_2$ emissions would plummet by 2,644,897 tons per year if the subsidy were reformed. We can deduct from this that the abolition of environmentally harmful subsidies in the electric power sector in the Republic of Korea would considerably contribute to $CO_2$ emissions abatement in the country.

• Journal of Hydrogen and New Energy
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• v.21 no.6
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• pp.595-603
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• 2010

This paper deals with an economic evaluation of domestic window type photoelectrochemical hydrogen production utilizing solar cells. We make some sensitivity analysis of hydrogen production prices by changing the values of input factors such as the initial capital cost, the solar to hydrogen conversion efficiency, and the system duration time. The hydrogen production price of the window type photoelectrochemical system was estimated as 1,168,972 won/$kgH_2$. It is expected that hydrogen production cost can be reduced to 47,601 won/$kgH_2$ if the solar to hydrogen conversion efficiency is increased to 14%, the system duration time is increased to 20,000 hours, and the initial capital cost is decreased to 25% of the current level. We also evaluate the hydrogen production cost of the water electrolysis using the electricity produced by solar cells. The corresponding hydrogen production cost was estimated as 37,838 won/$kgH_2$. The photoelectrochemical hydrogen production is evaluated as uneconomical at this time, and we need to enhance the solar to hydrogen conversion efficiency and the system duration time as well as to reduce prices of the system facilities.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.63-72
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• 2022

The extinction crisis of local cities, caused by a population density increase phenomenon in capital regions, directly causes the increase of vacant houses in local cities. According to population and housing census, Gunsan-si has continuously shown increasing trend of vacant houses during 2015 to 2019. In particular, since Gunsan-si is the city which suffers from doughnut effect and industrial decline, problems regrading to vacant house seems to exacerbate. This study aims to provide a foundation of a system which can predict and deal with the building that has high risk of becoming vacant house through implementing a data driven vacant house prediction machine learning model. Methodologically, this study analyzes three types of machine learning model by differing the data components. First model is trained based on building register, individual declared land value, house price and socioeconomic data and second model is trained with the same data as first model but with additional POI(Point of Interest) data. Finally, third model is trained with same data as the second model but with excluding water usage and electricity usage data. As a result, second model shows the best performance based on F1-score. Random Forest, Gradient Boosting Machine, XGBoost and LightGBM which are tree ensemble series, show the best performance as a whole. Additionally, the complexity of the model can be reduced through eliminating independent variables that have correlation coefficient between the variables and vacant house status lower than the 0.1 based on absolute value. Finally, this study suggests XGBoost and LightGBM based machine learning model, which can handle missing values, as final vacant house prediction model.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.257-290
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• 2005

Korean energy use in industrial sector has increased more rapidly than other sectors during 1980~2000 periods. Relatively higher increases in industrial sector energy consumption raise questions whether government policy of rationalization of industrial energy use has been effective. In this study, we use 80-85-90 and 90-95-00 constant price input-output table to analyze increases in industrial energy use. Using an adjusted version of structural decomposition model introduced by Chen and Rose (1990), we decompose Changes of energy use into 17 elements. We classify entire industry sector into 32 sectors including four energy sectors (coal and coal products, refined petroleum, electricity and town gas). We then analyze changes of energy use by industrial level to check differences among industrial energy demand structures. Finally, we compare three industries, electronic product manufacturing, metal manufacturing and construction, that represent technology and capital intensive, energy and material intensive and labor and capital intensive industry. As results, we find that high energy using industries make the most effort to reduce energy use. Primary metal, petrochemical and mon-metal industries show improvements in elements such as energy and material productivity, energy and material imports, energy substitution and material substitutions towards energy saving. These results imply that although those industries are heavy users of energy, they put the best effort to reduce energy use relative to other industries. We find various patterns of change in industrial energy use at industrial level. To reduce energy use, electronic product manufacturing industry needs more effort to improve technological change element while construction industry needs more effort to improve material input structure element.