• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.908-912
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• 1996

The yearly load forecasting system has been developed for the economic and secure operation of electric power system. It forecasts yearly peak load and thereafter deduces hourly load using the top-down approach. Relative coefficient model has been applied to estimate peak load of a specific date or a specific day of the week. It is equipped with graphic user interface which enables a user to easily access to the system. Yearly average forecasting error may be reduced to $2{\sim}3$(%) only if we can forecast summer-time temperature correctly.

• Journal of the Korean Solar Energy Society
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• v.26 no.4
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• pp.39-45
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• 2006

The decrease in the summer peak electric load in our country is very important. The government has arranged and implemented a lot of support policies and statutes to decrease the peak electric load. And the ice thermal energy storage system is known as one of the alternatives. The purpose of this paper is to evaluate the efficiency and thermal characteristics of the closed ice thermal energy storage system using screw capsules. The measured thermal energy storage density is about 18.4 USRT-h/m3 (=232.9 MJ/m3), which is higher than 13.0 USRT-h/m3 (=164.6 MJ/m3), a low criterion of normal performance. And The efficiency of the discharging process and the total energy utilization is 96.2% and 2028.4 kcal/kWh respectively.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1037-1040
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• 2000

We introduce the forecasting method for a next day electric peak load that uses the optimal combination of two types of neural networks. First network uses learning data that are past 10days of the target day. We name the neural network Short Term Neural Network (STNN). Second network uses those of last year. We name the neural network Long Term Neural Network (LTNN). Then we get the forecasting results that are the linear combination of the forecasting results by STNN and the forecasting results by LTNN. We name the method Combination Forecasting Method (CFM). Then we discuss the optimal combination of STNN and LTNN. Using CFM of the optimal combination of STNN and LTNN, we can reduce the forecasting error.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1079-1080
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• 2006

This paper presents the methods to save energy by using load consumption pattern analysis. Base on the substation annual power usage data, the characteristic of the substation power consumption have investigated. With this investigation effective electrical billing condition and the control target load has been determined. Most of power usage comes from the motor car, so power system and load table of motor car were reviewed. And in the metropolitan area the peak power consumption occurs at summer or winter rush hours because of its typical characteristics. From this data control point of time are determined. Through the whole study the methode to control the power Peak consumption are proposed and prototype was designed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.4
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• pp.2777-2783
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• 2015

Peak power reduction is becoming increasingly important not only for grid operators but also for residential users. The scheduling of electric loads tries to reduce the power peak by splitting the power-on period of an electric device into multiple smaller ones and by interleaving the on-periods of every device in a holistic way. This paper analyzes the performance of EDF, LSF, TCBM, and lazy scheduling algorithms and proposes the enhancement schemes. For analysis, we have implemented the scheduling policies in a simulation environment for distributed control systems. Through extensive experiments using real power traces, we discuss their performance characteristics in terms of power deviation, switch count, and temperature violation ratio. To prevent excessive switching, we propose to employ the concept of limited preemptibility and evaluate its effect on performance. It is found that the best performance is achieved when the scheduler capacity is dynamically adjusted to the actual power demand. The experiment results show that, by load scheduling, the probability of having a power deviation greater than 150W is reduced from 21.5% down to 3.2%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1214-1220
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• 2010

This paper proposes an efficient under frequency relay load shedding scheme for the korea power system which is more than two times than the system size and its capacity of the power system 10 years ago. The proposed method is keeping the power system stability and supports for the operating system during critical situations such as big disturbances and unstable in supply and demand. In order to determine the number of load shedding steps, the load to be shed per step, and frequency level, it is necessary to investigate and analyze maximum losses of generation due to the biggest contingency, maximum system overload, maximum keeping frequency, maximum load to be shed, and recovery frequency. The proposed method is applied to Off-peak load(25,400MW) and Peak load(62,290MW) of Korea Electric Power Corporation to demonstrate its effectiveness.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.6
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• pp.265-274
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• 2001

Recently, the operation of power systems has become more difficult because the peak demand load is increasing continuously and the daily load factor is getting worse and worse. Also, the consideration of deregulation and global environment in electric power industry is required. In order to overcome those problems, a study on the planning and operation in power systems of dispersed generating sources such as fuel cell systems, photovoltaic systems and wind power systems, has been performed energetically. This paper presents a method for determining an optimal operation strategy of dispersed co-generating sources, especially fuel cell generation systems, considering thermal supply as well as electric power supply. In other words, the optimal operation of those sources can be determined easily by the principle of equal incremental fuel cost and the thermal merit of those sources can be also evaluated quantitatively through Kuhn-Tucker's optimal conditions. In additions, an priority method using the comparison of total cost at the peak load time interval is presented in order ot select the optimal locations of those sources. The validity of the proposed algorithms is demonstrated using a model system.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.267-278
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• 2020

ESS is an essential requirement for resolving power shortages and power demand management and promoting renewable energy at a time when the energy paradigm changes. In this paper, we propose a cost-effective ESS Peak-Shaving operation plan through load and solar power generation forecast. For the ESS operation plan, electric load and solar power generation were predicted through RMS, which is a statistical measure, and a target load reduction guideline for one hour was set through the predicted electric load and solar power generation amount. The load and solar power generation amount from May 6th to 10th, 2019 was predicted by simulation of load and photovoltaic power generation using real data of the target customer for one year, and an hourly guideline was set. The average error rate for predicting load was 7.12%, and the average error rate for predicting solar power generation amount was 10.57%. Through the ESS operation plan, it was confirmed that the hourly guide-line suggested in this paper contributed to the peak-shaving maximization of customers.Through the results of this paper, it is expected that future energy problems can be reduced by minimizing environmental problems caused by fossil energy in connection with solar power and utilizing new and renewable energy to the maximum.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.12
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• pp.836-842
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• 2011

It has been shown that the air-conditioning system with slab cooling storage is effective in cutting peak load and utilizing nighttime electric power. The stochastic properties of internal heat generation which has great influence on the cooling load are examined in this paper. Based on the measured cooling load and electric power consumption in an office building with slab cooling storage, stochastic time series models to simulate these random processes are investigated. Furthermore, a calculated result by an optimal control method of thermal analysis taking into account the internal heat is compared with the measured cooling load.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.25-32
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• 2019

K-BEMS System was introduced to reduce peak load and to save total energy of the 200 buildings that KEPCO headquarter and branch offices use. And K-BEMS system is composed of PV, battery, and hybrid PCS. KEPCO research institute has carried out this K-BEMS research project for 3 years since January 2016. In this paper, the results of the project are shown. 9 modes of test results of K-BEMS system and are operational problems were analyzed. And measures to cure the trouble are also suggested. Batteries are operated more than 20% of SOC, and less than 20% of SOC battery protection switches are automatically shutting down the system and the system no longer respond to EMS, ending the supply of PV, and so therefore to continue the PV power supply it was turn out to be necessary that the EMS should automatically change its policy to change PV only supply mode automatically when the Battery Switch automatically operated. To operate the system continuously and automatically, it is necessary to modify the minimum operational SOC value, and in addition to that the EMS computer must remember the last shut-down SOC and Voltage which interrupted the system and add some margin to reflect the measurement error in the system.