• Proceedings of the KIEE Conference
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• summer
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• pp.162-164
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• 2004

Electrical power peak demand of Republic of Korea is annually growing and the peak demand has occurred in the summer. It is difficult that we handle with constructing power plants and increasing generation capacity to cope with a suddenly increased demand due to the cost problem, difficulty to find the new plant site, and the spread of the NIMBY. The alternative of the above problem is to efficiently manage demand of electrical power. Accordingly, load shedding of a section of demand side management is investigated. First we surveyed a trend of research in the domestic and overseas, for load curtailment and demand response program. After reviewing several demand response programs, the future research direction for load shedding in emergency and normal operation is introduced.

• Journal of IKEEE
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• v.23 no.1
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• pp.127-133
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• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.233-235
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• 2001

This paper deals with frequency control of demand users that have power plants by under-frequency relay. The demand users supply electrical power to a part of their loads by their power plants and to other rest of their loads by utility. While electrical power supply is stopped by faults of utility network, the system of demand users network is separated from total power system and their system frequency goes below normal limits. In this paper, this situation and the effect of under-frequency relay application are simulated by EMTDC.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.369-374
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• 2011

Generally the neural network and the Fuzzy compensative algorithms are applied to forecast the time series for power demand with the characteristics of a nonlinear dynamic system, but, relatively, they have a few prediction errors. They also make long term forecasts difficult because of sensitivity to the initial conditions. In this paper, we evaluate the chaotic characteristic of electrical power demand with qualitative and quantitative analysis methods and perform a forecast simulation of electrical power demand in regular sequence, attractor reconstruction and a time series forecast for multi dimension using Lyapunov Exponent (L.E.) quantitatively. We compare simulated results with previous methods and verify that the present method is more practical and effective than the previous methods. We also obtain the hourly predictability of time series for power demand using the L.E. and evaluate its accuracy.

• Journal of Information Technology Services
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• v.21 no.6
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• pp.117-125
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• 2022

In respond to climate change caused by global environmental problems, countries around the world are actively promoting the advancement of new electricity industries. The new energy business is being applied to energy storage systems (ESS), electric vehicle charging business, and power demand response using cutting edge technologies. In 2022, the Korean government is also establishing a policy stance to foster new energy industries and making efforts to improve its responsiveness to power demand response with the innovative technologies. In Korea, attempts to commercialize energy power are also being made in the private and public sectors to control energy power in houses, buildings, and industries. For example, private companies, local governments, and central government are making all-out efforts to develop new energy industry models through joint investment. There are forms such as establishing energy-independent facilities by region, establishing an electric vehicle charging system, controlling urban lighting systems with Information technologies, and managing demand between power suppliers and power consumers. This study examined the business model applied with energy storage system, electric vehicle charging business, smart lighting, and power demand response based on information communication technology to examine the site where smart energy system was introduced. According to this study, company missions and government tasks are suggested to apply new energy business technologies as economical energy solutions that meet the purpose of use by region, industry, and company.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.3
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• pp.95-99
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• 2013

The maximum power analysis simulator took advantage of the facilities and power consumption reduction simulator test scenario development and testing of improvement in the scenario. As a maximum demand power controller, Maximum power analysis simulator performs control and disperasion of maximum demand power by calculating base power, load forecast, and present power which are based on signal of watt-hour meter to keep the electricity under the target. In addition, various algorithms to select appropriate control methode on each of the light installations through the peak demand power is configured to management. The simulation shows the success of control power for the specified target controlled by five sequential lighting installations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.96.6-96
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• 2001

A high peak power demand at substations will result under Moving Block Signalling (MBS) when a dense queue of trains begins to start from a complete stop at the same time in an electrified railway system. This may cause the power supply interruption and in turn affect the train service substantially. In a recent study, measures of Starting Time Delay (STD) and Acceleration Rate Limit (ARL) are the possible approaches to reduce the peak power demand on the supply system under MBS. Nevertheless, there is no well-defined relationship between the two measures and peak power demand reduction (PDR). In order to attain a lower peak demand at substations on different traffic conditions and system requirements, an expert system is one of the possible approaches to procure the appropriate use of peak demand reduction measures ...

• Proceedings of the KIEE Conference
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• summer
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• pp.1260-1262
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• 2004

Direct Load Control(DLC) system is a load management program for stablization of electric power supply-demand. It is a series of acts limiting the demand of selected demand side at peak load or other time periods. Recently, power supply-demand instability due to dramatic increase in power usage such as summertime air-conditioning load has brought forecasts of decrease in power supply capability. Therefore heightening the load factor through systematic load management, in other words, Direct Load Control became necessary. By examining the composition and operation of the DLC system, this paper provides conceptional understanding of the DLC system and help in system research.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.833-836
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• 2012

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, examine the existing forecasting method and the exponential smoothing method, and then propose the forecasting method using Kalman Filter algorithm.