• Land and Housing Review
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• v.11 no.2
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• pp.87-94
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• 2020

Offshore renewable energy resources are attractive alternatives in addressing the nation's clean energy policies because of the high demand for electricity in the coastal region. As a large portion of potential resources is in deep and farther water, economically competitive floating systems have been developed. Despite the advancement of floating technologies, the high capital cost remains a primary barrier to go ahead offshore renewable energy projects. The dynamically installed piles (DIPs) have been considered one of the most economical pile concepts due to their simple installation method, resulting in cost and time-saving. Nevertheless, applications to real fields are limited because of uncertainties and underestimated load capacity. Thus, this study suggests the appropriate analytical approach to estimate the inclined load capacity of the DIPs by using the upper bound plastic limit analysis (PLA) method. The validity of the PLA under several conditions is demonstrated through comparison to the finite element (FE) method. The PLA was performed to understand how flukes, soil profiles, and load inclinations can affect the inclined load capacity and to provide reliable evaluations of the total resistance of the DIPs. The studies show that PLA can be a useful framework for evaluating the inclined load capacity of the DIPs under undrained conditions.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.5
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• pp.521-529
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• 2012

Since the energy demand for refrigeration and air-conditioning has greatly increased all over the world, thermally activated refrigeration cycle has attracted much attention. This study carries out a performance analysis of a vapor compression cycle (VCC) driven by organic Rankine cycle (ORC) utilizing low-temperature heat source in the form of sensible heat. The ORC is assumed to produce minimum net work which is required to drive the VCC without generating an excess electricity. Effects of important system parameters such as turbine inlet pressure, condensing temperature, and evaporating temperature on the system variables such as mass flow ratio, net work production, and coefficient of performance (COP) are thoroughly investigated. The effect of choice of working fluid on COP is also considered. Results show that net work production and COP increase with increasing turbine inlet pressure or decreasing condensing temperature. Out of the five kinds of organic fluids considered $C_4H_{10}$ gives a relatively high COP in the range of low turbine inlet pressure.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.27-34
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• 2014

The paper proposed a sizing method of an energy storage system(ESS) for peak shaving of high-speed railway substations based on load profile patterns of substations. A lithium based battery ESS was selected since it can produce high-power at high speed that peak shaving requires, and also takes up a relatively smaller space for installation. Adequate size of the ESS, minimum capacity which can technically meet a peak shaving target, was determined by collectively considering load patterns of a target substation, characteristics of the ESS to be installed, and optimal scheduling of the ESS. In case study, a local substation was considered to demonstrate the proposed sizing method. Also economic analysis with the determined size of ESS was performed to calculate electricity cost savings of the peak shaving ESS, and to offer pay-back period and return on investment.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.207-217
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• 2018

Recently, the importance of energy demand management, particularly peak load control, has been increasing due to the policy changes of the Second Energy Basic Plan. Even though the installation of distributed generation systems such as Photovoltaic and energy storage systems (ESS) are encouraged, high initial installation costs make it difficult to expand their supply. In this study, the power consumption of a university building was measured in real time and the measured power consumption data was used to calculate the optimal installation capacity of the Photovoltaic and ESS, respectively. In order to calculate the optimal capacity, it is necessary to analyze the operation methods of the Photovoltaic and ESS while considering the KEPCO electricity billing system, power consumption patterns of the building, installation costs of the Photovoltaic and ESS, estimated savings on electric charges, and life time. In this study, the power consumption of the university building with a daily power consumption of approximately 200kWh and a peak power of approximately 20kW was measured per minute. An economic analysis conducted using these measured data showed that the optimal capacity was approximately 30kW for Photovoltaic and approximately 7kWh for ESS.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.165-204
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• 2022

This study organized data from 2000 to 2014 for 20 grid-connected countries in Europe and analyzed the determinants of carbon emissions through the panel GLS method considering the problem of heteroscedasticity and autocorrelation. At the same time, the effect of introducing ETS was considered by dividing the sample period as of 2005 when the European emission trading system was introduced. Carbon emissions from individual countries were used as dependent variables, and proportion of generation by each source, power self-sufficiency ratio of neighboring countries, power production from resource-holding countries, concentration of power sources, total energy consumption per capita in the industrial sector, tax of electricity, net electricity export per capita, and size of national territory per capita. According to the estimation results, the proportion of nuclear power and renewable energy generation, concentration of power sources, and size of the national territory area per capita had a negative (-) effect on carbon emissions both before and after 2005. On the other hand, the proportion of coal power generation, the power supply and demand rate of neighboring countries, the power production of resource-holding countries, and the total energy consumption per capita in the industrial sector were found to have a positive (+) effect on carbon emissions. In addition, the proportion of gas generation had a negative (-) effect on carbon emissions, and tax of electricity were found to have a positive (+) effect. However, all of these were only significant before 2005. It was found that net electricity export per capita had a negative (-) effect on carbon emissions only after 2005. The results of this study suggest macroscopic strategies to reduce carbon emissions to green growth, suggesting mid- to long-term power mix optimization measures considering the electricity trade market and their role.

• KIEAE Journal
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• v.13 no.4
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• pp.43-48
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• 2013

The effect of climate change has influenced humanity and ecosystem with tremendous changes in temperature. For the past 150 years, the national annual average temperature is 0.6 degree increased and the heating degree day reduced from April to November. However, December to January, the climate change was generated and the heating degree day increased. The blackout occured in 2011 and 2012 by increasing electricity consumption of heating and cooling equipment to the effects of climate change. That is because heating load accounted for 20% of building electric use. In this study, strengthening measures to reduce heating energy consumption is presented due to climate change in winter since 1980 to prevent blackout and reliable power supply for the building energy-saving design standards by Meteorological data provided by the National Weather Service were calculated using the heating degree days in order to present eighteen cities from 1980 to 2012. Insulation standards are presented to prevent black-out by the heating degree days. the heating energy demand was reduced almost 6% including 10% in Central, 5% in South and Jeju area based on strengthening of the insulation. It is applied to the entire country an annual economic effect of 250 billion won, and black-out can be prevented.

• Environmental and Resource Economics Review
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• v.23 no.2
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• pp.349-364
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• 2014

This study analyzed Granger causality between power consumption and production activity in manufacturing sector, by using error correction model. It found that there exists the connection between power consumption and production activity in manufacturing sector. By reflecting the industrial characteristics, it found not only the bilateral causality (power consumption ${\leftrightarrow}$ production activity) in power non-intensive industry, high value-added industry and low value-added industry, but also one-way causality (power consumption ${\rightarrow}$ production activity) in power-intensive industry. These results imply that power demand management policy focusing on efficiency improvement is necessary primarily to minimize negative impacts on production activity, and also stable power supply system is required to meet the increase of power demand.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.5
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• pp.363-370
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• 2013

Small scale hydropower is one of most attractive and cost-effective energy technologies for installation within sewage treatment plants. This study was conducted to evaluate the potential of a semi-kaplan micro-hydropower (MHP) system for application to sewage treatment plants with high flow fluctuations and a low head. The semi-kaplan MHP is equipped with an adjustable runner blade, and is without a guide vane, so as to reduce the incidence of mechanical problems. A MHP rating 13.4 kWp with a semi-kaplan turbine has been considered for Kiheung Respia sewage treatment plant, and this installation is estimated to generate 86.8 MWh of electricity annually, which is enough to supply electricity to over 25 households, and equivalent to an annual reduction of 49 ton $CO_2$. The semi-kaplan turbine showed a 90.2% energy conversion efficiency at the design flow rate of 0.35 $m^3/s$ and net head of 4.7 m, and was adaptable to a wide range of flow fluctuations. Through the MHP operation, approximately 2.1% of total electricity demand of Kiheung Respia sewage treatment plant will be achievable. Based on financial analysis, an exploiting MHP is considered economically acceptable with an internal rate of return of 6.1%, net present value of 15,539,000 Korean Won, benefit-cost ratio of 1.08, and payback year of 15.5, respectively, if initial investment cost is 200,000,000 Korean Won.

• 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.