• 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 Electrical Engineering and Technology
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• v.8 no.6
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• pp.1251-1260
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• 2013

This paper presents the optimal scheduling of hourly consumption in a residential community (community, neighborhood, etc.) based on real-time electricity price. The residential community encompasses individual residential loads, communal (shared) loads, and local generation. Community-aggregated loads, which include residential and communal loads, are modeled as fixed, adjustable, shiftable, and storage loads. The objective of the optimal load scheduling problem is to minimize the community-aggregated electricity payment considering the convenience of individual residents and hourly community load characteristics. Limitations are included on the hourly utility load (defined as community-aggregated load minus the local generation) that is imported from the utility grid. Lagrangian relaxation (LR) is applied to decouple the utility constraint and provide tractable subproblems. The decomposed subproblems are formulated as mixed-integer programming (MIP) problems. The proposed model would be used by community master controllers to optimize the utility load schedule and minimize the community-aggregated electricity payment. Illustrative optimal load scheduling examples of a single resident as well as an aggregated community including 200 residents are presented to show the efficiency of the proposed method based on real-time electricity price.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.266-272
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• 2011

Real Time Pricing(RTP) is used not only to stabilize the price volatility in electricity market, but to hedge the price risk for Load Serving Entity(LSE). This paper presents an efficient method to reduce the risk of the price volatility in real-time electricity market. For designing the RTP, load patterns of customer are calculated by applying the demand elasticity and customer's utility is also analyzed to compute the RTP revenue through the risk-attribute of the LSE. In the end, the distribution of the LSE's profits can be evaluated to lead the optimal RTP value, depending on the level of customer's participation. Results from the case study based on PJM data are reported to illustrate the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.49-67
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• 2014

This paper proposes a real time pricing model for smart grid considering consumers' behavior, real time price elasticity, and exogenous price. Based on the proposed model, we found the weight of utility over cost is the most sensitive factor compared to other factors. Also, if the electricity price is set to be changed too sensitively to energy consumption, it is warned that real time pricing sometimes can cause increment of peak-time demand and volatility. Finally, real time pricing could be less efficient when the difference between the maximum and the minimum consumption level is small.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1627-1636
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• 2014

In real-time electricity price environment, the energy management system can provide the significant advantage to the residential, commercial and industrial customers since it can reduce the electricity charge by controlling the load operation effectively in response to time-varying price. However, the earlier studies for load management mainly focus on the residential and commercial customers except for the industrial customers because most of load operations in industrial sector are intimately related with production schedule. So, it is possible that the inappropriate control of loads in industrial sector causes huge economic loss. In this paper, therefore, we propose load control algorithm for factory energy management system(F-EMS) to achieve not only minimizing the electricity charges but also maintaining production efficiency by considering characteristics of load operation and production schedule. Considering characteristics of load operation and production schedule, the proposed load control algorithm can reflect the various characteristics of specific industrial customer and control their loads within the range that the production efficiency is maintained. Simulation results show that the proposed load control algorithm for F-EMS leads to significant reduction in the electricity charges and peak power in industrial sector.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.889-895
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• 2014

Korea electric power industry had been under vertical monopoly but is typically getting restructured for free competition. An ideal pricing system under the competitive market system is 'unbundled pricing system' and 'marginal pricing system', but the current pricing system still adheres to the traditional bundled system and the average cost pricing system. Especially, progressive electricity rates for residential use reflect governmental policy-making which is focused on income redistribution & welfare, industrial supports and energy saving. This study proposes new and reasonable residential electricity pricing systems which are Time-Of-Use (TOU) and Real-Time Pricing (RTP) to reflect variations in the wholesale price of electricity. It also presents examples of various tariffs for residential electricity pricing systems.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.41-50
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• 2012

With the recent advances in smart grid technologies and the increasing dissemination of smart meters, the power usage of each time unit can be detected in modern smart building environments. Thus, the utility company can adopt different price of electricity at each time slot considering the peak time. Korea government also announces the smart-grid roadmap that includes a law for realtime price of electricity. In this paper, we propose an efficient power scheduling scheme for smart buildings that adopt smart meters and real-time pricing of electricity. Our scheme dynamically changes the power mode of each consumer device according to the change of power rates. Specifically, we analyze the electricity demands and prices at each time, and then perform real-time power scheduling of consumer devices based on collaboration of each device. Experimental results show that the proposed scheme reduces the electricity charge of a smart building by up to 36.4%.

• Journal of Energy Engineering
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• v.25 no.2
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• pp.37-44
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• 2016

The demand for electricity has a considerable impact on various energy sectors since electricity is generated from various energy sources. This paper attempts to estimate the electricity demand function and obtain some quantitative information on price and income elasticities of the demand. To this end, we apply a lagged dependent variable model to derive long-run as well as short-run elasticities using the time-series data over the period 1991-2014. Our dependent variable is annual electricity demand. The independent variables include constant term, real price of electricity, and real gross domestic product. The results show that the short-run price and income elasticities of the electricity demand are estimated to be -0.142 and 0.866, respectively. They are statistically significant at the 5% level. That is, the electricity demand is in-elastic with respect to price and income changes in the short-run. The long-run price and income elasticities of the electricity demand are calculated to be -0.210 and 1.287, respectively, which are also statistically meaningful at the 5% level. The electricity demand is still in-elastic with regard to price change in the long-run. However, the electricity demand is elastic regarding income change in the long-run. Therefore, this indicates that the effect of demand-side management policy through price-control is restrictive in both the short- and long-run. The growth in electricity demand following income growth is expected to be more remarkable in the long-run than in the short-run.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.435-437
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• 2002

This Paper presents the technical perspective on the Real Time Pricing(RTP). So far customer has no control on the electricity pricing. But the price hike in California makes changes in the electricity demand and pricing. In such environments. RTP has become an important role in Demand Side Management. Here the system configuration, advantages of RTP are described. Also the relationship between RTP and AMR is considered.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2826-2832
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• 2012

A smart grid is an electric-power grid that employs a new information technology.This technology makes it possible to exchange real-time energy information between suppliers and consumers, finally resulting in high energy efficiency. The energy management system in smart grid provides up to date information on electricity consumption as well as dynamic electricity price to consumers of smart grid system. However, the existing energy management systems only focus on pricing system, for example, real-time electricity prices. In this paper, we try to improve the existing energy management system and propose the energy management system that mainly focuses on the efficiency of electricity consumption. In the proposed management system, PMU(Phasor Measurement Units) installed in switchboards gathers electricity data in a real time. We also propose to use data mining method, which is applied to analyzed electricity data for improving energy efficiency. Also, the proposed energy management system is designed to efficiently control the electricity between PMU and management system in case of a shortage of electricity or surplus electricity.