• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.59-68
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• 2014

The Fukushima nuclear accident in 2011 had an extensive impact on the national electricity plans. This paper outlines alternative electricity scenarios that meet the goals of nuclear phase-out and greenhouse gas (GHG) emission reduction. This paper also analyzes the results of each scenario in respect to the electricity mix, GHG emissions, costs and employment effects. The Long-range Energy Alternatives Planning system (LEAP) model was used to simulate the annual electricity demand and supply system from 2011 to 2030. The reference year was 2009. Scenarios are reference (where existing plans are continued), A1, A2, B1, B2, and C2 (where the levels of demand management and nuclear phase-out are different). The share of renewable energy in the electricity mix in 2030 for each scenario will be increased from about 1% in 2009 to 8% in the reference scenario and from 11% to 31% in five alternative scenarios. Total cumulative cost increases up to 14% more than the reference scenario by replacing nuclear power plants with renewable energy in alternative scenarios could be affordable. Deploying enough renewable energy to meet such targets requires a roadmap for electricity price realization, expansion of research, development and deployment for renewable energy technologies, establishment of an organization dedicated to renewable energy, and ambitious targets for renewable energy.

• Journal of Energy Engineering
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• v.24 no.2
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• pp.167-173
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• 2015

This paper attempts to estimate the electricity demand function in Korea with quarterly data of average temperature, GDP and electricity price over the period 2005-2013. We apply lagged dependent variable model and ordinary least square method as a robust approach to estimating the parameters of the electricity demand function. The results show that short-run price and income elasticities of the electricity demand are estimated to be -0.569 and 0.631, respectively. They are statistically significant at the 1% level. Moreover, long-run income and price elasticities are estimated to be 1.589 and -1.433, respectively Both of results reveal that the demand for electricity is price- and income-elastic in the long-run. The relationship between electricity consumption and temperature is supported by many of references as a U-shaped relationship, and the base temperature of electricity demand is about $15.2^{\circ}C$. It is shown that power of explanation and goodness-of-fit statistics are improved in the use of the lagged dependent variable model rather than conventional model.

• Journal of Korean Society for Quality Management
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• v.49 no.3
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• pp.327-340
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• 2021

Purpose: The purpose of this study is to propose a system predicting whether an electricity distribution system is abnormal by analyzing the temperature of the deteriorated system. Traditional electricity distribution system abnormality diagnosis was mainly limited to post-inspection. This research presents a remote monitoring system for detecting thermal images of the deteriorated electricity distribution system efficiently hereby providing safe and efficient abnormal diagnosis to electricians. Methods: In this study, an object detection algorithm (YOLOv5) is performed using 16,866 thermal images of electricity distribution systems provided by KEPCO(Korea Electric Power Corporation). Abnormality/Normality of the extracted system images from the algorithm are classified via the limit temperature. Each classification model, Random Forest, Support Vector Machine, XGBOOST is performed to explore 463,053 temperature datasets. The process capability index is employed to indicate the quality of the electricity distribution system. Results: This research performs case study with transformers representing the electricity distribution systems. The case study shows the following states: accuracy 100%, precision 100%, recall 100%, F1-score 100%. Also the case study shows the process capability index of the transformers with the following states: steady state 99.47%, caution state 0.16%, and risk state 0.37%. Conclusion: The sum of caution and risk state is 0.53%, which is higher than the actual failure rate. Also most transformer abnormalities can be detected through this monitoring system.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.678-681
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• 2007

Now Korea depends upon the imported resources for about 97% of total using energy. So from October, 2001 Korean government has supported renewable energy business owners by providing them with Electric Power Industry Basis Fund. Only plant-use electricity of the small hydro power plant is exactly managed, but other renewable energy plants is unprepared or not yet managed. Therefore, in this paper, we'll analyze the plant-use electricity management of the small hydro power and propose improvement plans for plant-use electricity of the photovoltaic power plant.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.2
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• pp.18-26
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• 2012

Though electricity consumption amount in industry has been increased gradually, corresponding power supply show symptoms of marginal point. Importance of demand-side management from large-industries has also been raised. This paper deals with induction motor, which is one of representative examples of heavy electricity consumption utilities, to analyze potential technical capability, economic feasibility from consumers' viewpoint and demand-side management feasibility from nation-wide perspective. Nation-wide economic feasibility analysis was done through California test, which has been used as demand-side management evaluation model. This paper also describes limitation of existing high efficiency induction motor in terms of contribution to demand-side management and utilizes premium motor to calculate demand-side management contribution level and economic feasibility evaluation. Likewise, this paper emphasizes the efficiency improvement of induction motor and analyzes how much premium motor related technologies can contribute to demand-side management.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.10
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• pp.381-386
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• 2016

An optimization algorithm is developed based on a simulation case of a single family house model equipped with PV arrays. To increase the nationwide use of PV power generation facilities, a market-competitive electricity price needs to be introduced, which is determined based on the time of use. In this study, quadratic programming optimization was applied to minimize the electricity bill while maintaining the indoor temperature within allowable error bounds. For optimization, it is assumed that the weather and electricity demand are predicted. An EnergyPlus-based house model was approximated by using an equivalent RC circuit model for application as a linear constraint to the optimization. Based on the RC model, model predictive control was applied to the management of the cooling load and electricity for the first week of August. The result shows that more than 25% of electricity consumed for cooling can be saved by allowing excursions of temperature error within an affordable range. In addition, profit can be made by reselling electricity to the main grid energy supplier during peak hours.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.12
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• pp.549-555
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• 2001

Due to the development of information technology, the operating power systems under the deregulated environment has the advantages of a introduction of the market function, a competition in sales and purchases of Power, as well as the difficulty of maintaining reliability on the same or high level with it in a monopolistic market. This paper presents a basic scheme of the congestion management in the Korea electricity market under the deregulated environment. We investigated some cases of the congestion management in the world and the effects of the congestion management in the power systems. A basic idea of the congestion management in the Korea is presented based on the analysis of transmission congestion management in the competitive electricity market.

• ETRI Journal
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• v.35 no.4
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• pp.677-686
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• 2013

In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.17-18
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• 1991

In electric Industry, the improvement of load factor by flattening load has been considered to be more important than any other tasks and has received wide concern and interest. Especially while annual peak load had occurred early evening in winter during past decades, but we found the trend has changed so that annual peak load occurred during the daytime in summer since 1981. In this paper we introduce various method for the load management.