• Environmental and Resource Economics Review
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• v.24 no.2
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• pp.365-410
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• 2015

Block-rate structures are widely used in utility-pricing, including the Korean residential electricity sector. In the case of the current pricing structure, Korean citizens are highly concerned about incurring excessive electricity costs. For these reasons, there have been many discussions concerning mitigation of the strict pricing structure. Existing studies on the residential electricity demand function under block-rate structure have the following three issues - the consumer's budget constraint is non-linear, perceived price under block-rate structure is uncertain, block-rate structure has endogeneity in the price variable. In this context, this paper estimates the residential electricity demand function using micro-level household expenditure data and simulates the impact of alternative block-pricing schedules.

• Environmental and Resource Economics Review
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• v.24 no.1
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• pp.225-249
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• 2015

There are growing interests in the introduction of consumer's selective electricity tariff systems in order to enhance demand response in electricity market in Korea. Real time pricing (RTP) and Time of Use (TOU) are typical examples of demand response system through which electricity price is linked to real time demand. This paper adopts an agent-based model to analyze the effects of such demand system on the counsumers' electricity costs. The result shows that real time pricing system is effective to reduce electricity costs of consumers by providing more flexible tariff system, depending on each consumer's demand pattern. This finding could be used as a basis for supporting smart grid system in the presence of responsive demand environment.

• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.499-520
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• 2020

Input ratio of electricity to other production inputs in the Korean manufacturing sector has been higher than for the other OECD countries. In addition, electricity prices in Korea has been relatively lower than the average of OECD countries. Moreover, electricity sector is responsible for most CO2 emissions in Korea as coal and natural gas account 41.9% and 26.8% of electricity production as of 2018. Therefore, it looks inevitable to raise the electricity tariff for the manufacturing sector in Korea, but there is a concern that increase in the electricity tariff might affect small and medium enterprises (SMEs) more than large firms. This study estimates electricity demand's price and output elasticities for large firms and SMEs in steel industry by employing a time varying parameter model (Kalman filter). The analysis shows that changes in output levels regardless of firms' size affect electricity demands more significantly than do changes in electricity prices. Second, large firms have higher variances for both price and output elasticities of electricity demand. Third, large firms have higher price elasticity but lower output elasticity of electricity demand relative to SMEs. Policy implications are suggested in association with how to reduce electricity demands in the energy-intensive industry.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.12
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• pp.568-574
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• 2001

The variations of real time electric power price in competitive electricity markets have influence on electric power demands of the consumers. The effects of the consumers for electric power price can be expressed the price elasticity coefficient of the power demand as a measurement. Residential, commercial, and industrial consumers with different characteristics cause the different price elasticity of the power demand due to changing the pattern of consumption. It is necessary that the effects of electric power demands as a function of elasticity coefficient for each loads should be analyzed in Korea which is processing deregulated electric market. Therefore, this paper calculate the elasticity coefficient of each loads and analysis the effects of electric power demands as a function of elasticity coefficient of inflexible and flexible consumers in competitive electricity market.

• Journal of Energy Engineering
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• v.16 no.1 s.49
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• pp.22-31
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• 2007

This paper addresses methodology in order to consider CHP (Combined Heat and Power) capacity in the Basic Plan of Long Term Electricity Supply & Demand and presents effects on it. The method performs state in extent that do not change maximum in the Basic Plan of Long Term Electricity Supply & Demand. For analysis that occurs some advantage this method compares with Basic Plan of Long Term Electricity Supply & Demand. It includes EES (Expected Energy Served), Fuel consumption, amount of $CO_{2}$ emission reduction.

• KIEAE Journal
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• v.11 no.5
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• pp.91-96
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• 2011

This study is to analysis the characteristics of electric power consumption of apartments complex in Korea. This study shows the pattern of electric power consumption and correlation of each apartment complex's completion year monthly and timely. With this result, we are able to predict the demand pattern of electricity in a house and make the schedule by demand pattern. It is expected this data is used as reference of electric consumption of Daejeon area to operate the simulation tools to predict the building energy. The yearly data of 10 apartment complexes of 2010 are analyzed. The results of this study are followed. The averaged amount of electricity consumption in winter is higher as summer because of the high capacity of heating equipment. All of the house has electric base load from 0.26kWh to 0.5kWh. The average of the electricity consumption of month is shown as 310.2kWh. A week is seperated, as 4 part such as week, weekend, Saturday and Sunday. During week, the average of timely electricity consumption is shown as 0.426kWh. The Saturday consumption is 0.437kWh. The Sunday is 0.445kWh. The peak electricity consumption in summer and winter is measured. The peak consumption on summer season is 1.389kW on 22th August 64% higher than winter season 0.887kW on 3rd January.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.3
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• pp.35-46
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• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.23-26
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• 2017

Social Welfare is useful concept for evaluating the effectiveness of an economic policy in micro economics. This paper focuses on Social Welfare(SW) of electricity market incorporating demand response(DR). Competition between DR and generation company is modeled as a simple bid function. DR function can be considered as an negative generation(called Negawatt) and as an element of modified demand function. These two approaches result in the same demand reduction, generation power, and the market price. However, SW in the modified demand function approach is not identical to SW in the Negawatt approach. It makes the numerical index of DR effectiveness less persuasive. This paper proposes modified definition of SW in the demand function approach. The proposed definition of SW leads the DR effectiveness index to be identical to that in the Negawatt approach.

• Korean Management Science Review
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• v.26 no.1
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.405-421
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• 2019

Predicting how much energy to use is an important issue in society. However, it is more difficult to capture the usage characteristics of residential buildings than other buildings. This paper provides time series analysis methods for electricity consumption in a residential building. Temperature is closely related to electricity demand. An error correction model, which is a method of adjusting the error with time, is applied when a cointegration relation is established between variables. Therefore, we analyze data via ECMs with consideration of the temperature effect.