• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2446-2448
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• 2001

In addition to the stabilization of electricity supply and the quality management of electricity, load balance has been an important strategy for achieving high quality load management. Among many techniques for load management, direct load management has been actively studied and applied for increasing the efficiency of power facility and suppressing peak load. In Korea, the highest peak load is demanded in summer rather than in winter, and almost 50% of the peak load comes from cooling load. Currently, applicable systems are limited to air conditioners that have the cooling capacity less than 2kW. This paper describes the development of remote controlled air conditioners and the result of the field test of the new type air conditioner. The technical specification based on the test will be applied to the new model of the remote controlled air conditioner. The wide distribution of the air conditioners to the public will be helpful to control peak demand due to cooling load in summer time. Financial investment to generating, transmission, distribution facilities will be decreased from flatting the seasonal power load.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2825-2827
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• 2000

In electric power industry. load balance has been one of the most fundamental and important management goals. Therefore. the strategy to achieve high quality load management now includes load balance besides the stabilization of electricity supply and quality management of electricity. Amongst many techniques of load management. direct load management has been actively studied and utilized to increase power facility and peak load suppression. Higher peak load situation is appeared during summer than during winter in Korea. and approximately 20% of the peak load is due to the load for air-conditioning. To cope with this peak load problem during summer KEPCO is performing a research project to develop a system to remotely control air-conditioning load using wireless communication. Currently, applicable facilities are limited to small-scale air-conditioning facility that has less than 2KW power capacity. This paper described the 1st year of efforts made in the study.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.1
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• pp.20-27
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• 2001

In this paper, we propose a method for estimating the peak load of pole-transformer in summer season considering the degree of cooling load possession in customer. The cooling load of customer is selected as the most reliable parameter of peak load in summer season. The proposed estimation method is restricted to the aspect of load management for pole-transformer. The main concept of proposed method is that the error of peak load estimation using load regression equation reduces with considering the degree of cooling load possession in customer. We propose an index for estimation of cooling load possession in each customer. The proposed index is defined as cooling load possession in customer (CLPC) and obtained from the increment of monthly electric energy. The membership function for deciding the uncertainty of cooling load possession in customer is used. The database of pole-transformer in Korea Electric Power Corporation (KEPCO) is used for case studies. Through the case studies, we verify that the proposed method reduces the error of peak load estimation than the conventional method in domestic.

• 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
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• v.41 no.9
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• pp.994-1002
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• 1992

With regard to price elasticity and cross elasticity of electricity, optimal generation expansion planning method including load management effect is suggested. In addition, optimal peak time price can be determined simultaneously, and we adopt peak time tariff as load management strategy. Instead of using hourly marginal demand curves where we can get customer surplus, we used chronological load curve with constraints to preserve social welfare. This method is proved useful in short-term generation expansion planning.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.572-573
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• 2008

Demand side management can be defined as series of planning and programs to change the electric usage pattern of customers from their normal ones with a least cost while meeting customers electric demand. In general, conventional demand side management programs can be classified into two groups, one of which is a load management and the other is energy efficiency. In this paper, the load management tariff programs in Korea are explored in terms of their effect on the peak demand reduction.

• Proceedings of the KIEE Conference
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• 1985.07a
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• pp.144-147
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• 1985

In electricity industry, the improvement of load factor by flattening of 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 occured early evening in winter during past decades, but we found the trend has changed so that annual peak load occured during the daytime in summer since1981 The useful practicing methods of this load management ale as follows; 1. Inducing of midnight load by thermal storage water heating 2. Seasonal differential rates. 3. Revising the peak load priceing (Time-of -use) It seems hard to expect that load research can be carried out in a short time, and we all have to exert outselves continuously to provide efficient load management method without wasting resources.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1267-1269
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• 1999

This paper studies on arrangement of load correlation coefficient for advanced load management. To accurate load correlation coefficient, we used two real factors, electrical energy(kWh) and peak load current of pole transformers, acquired by measuring instrument. Out of several correlation equations, we find that the quadratic equation is the most accurate to express peak load current and working electrical energy. If the data is located in the outside of ${\pm}3{\sigma}$ it is discarded. For load management, we rearranged load correlation coefficient considering +2${\sigma}$ at load correlation equation. Comparing conventional load correlation coefficient with rearranged one, we can get the result of error reduced and it is adjacent to the actual data. It will be used peak load forecasting from working electrical energy and we are able to prevent from the damaging of pole transformer due to overload.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.141-146
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• 2014

This paper suggests a way of generating one-day load curves for performance improvement of peak shaving in a power system. This Peak Shaving algorithm is a long-term scheduling algorithm of PMS (Power Management System) for BESS (Battery Energy Storage System). The main purpose of a PMS is to manage the input and output power from battery modules placed in a power system. Generally, when a Peak Shaving algorithm is used, a difference occurs between predict load curves and real load curves. This paper suggests a way of minimizing the difference by making predict load curves that consider weekly normalization and seasonal load characteristics for smooth energy charging and discharging.