• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1427-1434
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• 2017

Since the late 2000s, there has been growing preparation in South Korea for a sudden reunification of South and North Korea. Particularly in the power industry field, thorough preparations for the construction of a power infrastructure after reunification are necessary. The first step is to estimate the peak load demand. In this paper, we suggest a new peak demand estimation methodology by integrating existing correlation analysis methods between economic indicators and power generation quantities with a power supply interruption model in consideration of power consumption patterns. Through this, the potential peak demand and actual peak demand of the Nation, which experiences power supply interruption can be estimated. For case studies on North Korea after reunification, the potential peak demand in 2015 was estimated at 5,189 MW, while the actual peak demand within the same year was recorded as 2,461 MW. The estimated potential peak demand can be utilized as an important factor when planning the construction of power system facilities in preparation for reunification.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.161-163
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• 2006

This paper introduce the technology of peak demand reduction using automatic water tank pumping system on the apartment. That systems on the apartments installed water tank can control pumping(electricity) demand. Generally, system peak demand is occurred at the same time on workday and many water pumps consume electric power randomly. At this point, shift of operating time of water pump can reduce peak demand using automatic water tank pumping system. We were operating this system on some apartments for test of effect of peak demand reduction. and we represent result of demand shift. This result suggests that spread of the automatic water pumping system can contribute to reduce system peak demand and reduce system operation cost.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1128-1134
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• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1296-1304
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• 2013

This study performed an analysis on power demand reduction effects exhibited by demand response programs, which are advanced from traditional demand-side management programs, in the smart grid environment. The target demand response systems for the analysis included incentive-based load control systems (2 month-ahead demand control system, 1~5 days ahead demand control system, and demand bidding system), which are currently implemented in Korea, and price-based demand response systems (mainly critical peak pricing system or real-time pricing system, currently not implemented, but representative demand response systems). Firstly, the status of the above systems at home and abroad was briefly examined. Next, energy saving effects and peak demand reduction effects of implementing the critical peak or real-time pricing systems, which are price-based demand response systems, and the existing incentive-based load control systems were estimated.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.41-43
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• 2002

The contracted electric power and the demand factor of customers are used to predict the peak load in distribution transformers. The conventional demand factor was determined more than ten years ago. The contracted electric power and power demand have been increased. Therefore, we need to prepare the novel demand factor that appropriates at present. In this paper, we modify the demand factor to improve the peak load prediction of distribution transformers. To modify the demand factor, we utilize the 169 data acquisition devices for sample distribution transformers in winter, spring summer. And, the peak load currents were measured by the case studies using the actual load data, through which we verified that the proposed demand factors were correct than the conventional factors. A newly demand factor will be used to predict the peak load of distribution transformers.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.720-723
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• 1996

The recent summer power peak crisis has been caused by excessive use of cooling loads at daily peak time in summer. The yearly load shape of KEPCO has gradually became very steep valley. Under this situation, more efficient DSM(Demand Side Management) tools are fully required for summer peak clipping and shaving. In this paper, the KEPCO's Jeju-Do model project for DSM, especially for Demand Controller, is presented. Demand Controller was evaluated to have the very high economical efficiency against the investment in equipment, as compared with another DSM tools. There were some serious problem to apply Demand Controller to many customers in the aspect to synchronization with KEPCO's watthour meter. But these problems have solved by Keyin's new Demand Controller using vision algorithm.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.530-532
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• 2002

The contracted electric power and the demand factor of customers are used to predict the peak load in distribution transformers. The conventional demand factor was determined more than ten years ago. The contracted electric power and power demand have been increased. Therefore, we need to prepare the novel demand factor that appropriates at present. In this paper, we modify the demand factor to improve the peak load prediction of distribution transformers. To modify the demand factor, we utilize the 169 data acquisition devices for sample distribution transformers. The peak load currents were measured by the case studies using the actual load data, through which we verified that the proposed demand factors were correct than the conventional factors. A newly demand factor will be used to predict the peak load of distribution transformers.

• 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 ...