• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.1-13
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• 2020

There are several methods of peak-shaving, which reduces grid power demand, electricity bought from electricity utility, through lowering "demand spike" during On-Peak period. An optimization method using linear programming is proposed, which can be used to perform peak-shaving of grid power demand for grid-connected PV+ system. Proposed peak shaving method is based on the forecast data for electricity load and photovoltaic power generation. Results from proposed method are compared with those from On-Off and Real Time methods which do not need forecast data. The results also compared to those from ideal case, an optimization method which use measured data for forecast data, that is, error-free forecast data. To see the effects of forecast error 36 error scenarios are developed, which consider error types of forecast, nMAE (normalizes Mean Absolute Error) for photovoltaic power forecast and MAPE (Mean Absolute Percentage Error) for load demand forecast. And the effects of forecast error are investigated including critical error scenarios which provide worse results compared to those of other scenarios. It is shown that proposed peak shaving method are much better than On-Off and Real Time methods under almost all the scenario of forecast error. And it is also shown that the results from our method are not so bad compared to the ideal case using error-free forecast.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.152-155
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• 1991

The analysis and forecast of summer cooling load is one of the major concerns of utility company(KEPCO). In this paper, various methodologies to assess the weather sensitive load are introduced and the cause of remarkable growth of the summer cooling load in the last years are analized. To establish the effective measures to migrate the peak building by the summer cooling, a number of practical institutional policies are offered for future implementation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.11
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• pp.1536-1541
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• 2018

For effective electricity consumption in urban railway station such as peak load shaving, it is important to know each electrical load pattern by various usage. The total electricity consumption in the urban railway substation is already measured in Korea, but the electricity consumption for each usage is not measured. The author proposed the deep learning method to estimate the electrical load pattern for each usage in the urban railway substation with public data such as weather data. GRU (gated recurrent unit), a variation on the LSTM (long short-term memory), was used, which aims to solve the vanishing gradient problem of standard a RNN (recursive neural networks). The optimal model was found and the estimation results with that were assessed.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1211-1217
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• 2001

This paper presnts a controller for the heat capacity of thermal storage systems using off-peak electricity which is composed of an identifier using neural networks and a storage time adjuster in order to store exactly the required thermal energy without loss. Since thermal storage systems have nonlinear characteristics and large time constant, even if we predict the heating load accurately, it is very difficult to store exactly the required thermal energy. Thus, in the neural network for the identifier, the adaptive learning rate for high learning speed and bit inputs based on state changes of thermal storage power source are used. Also a hardware for the controller using a microprocessor is developed. The performance of the proposed controller is shown by experiment.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• IE interfaces
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• v.11 no.1
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• pp.207-214
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• 1998

Since the natural gas air-conditioning not only increases the base load of the gas company but also decreases the summer peak load of the electricity company, it is considerded as an efficient demand-side management program. This paper suggests the economic evaluation method of the gas air-conditioning program from the perspectives of the participants, the pipeline gas company, the local distribution company, the electricity company, and the total resources. The absorption type gas air-conditioning/space-heating is selected as a case study to illustrate the economic analysis of the natural gas air-conditioning.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.246-250
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• 1989

Owing to the rapid development of economy and the higher living standard of people, electricity demands have growth and the peak load has been increased rapidly. To cope with this impacts and to reduce the cost of service,utilities are conserned about power load management program. This paper shows a scheme of power load control and the basic structure of direct load control system. And also radio control method using the public pager which is one of the best economical and serviceable method in techniques will be introduced briefly.

• Journal of the Korean Institute of Gas
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• v.17 no.1
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• pp.42-48
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• 2013

The global warming of the Korean Peninsula proceeds most rapidly in the world and its abnormal climate is more deepening. In the result of the surged electricity consumption by intense heat of summer and severe cold of winter, electricity supply and demand status is in hard situation. Currently, the supply of natural gas is increased because natural gas has the lowest greenhouse-gas emissions among the existed fossil fuel. Natural gas cooling has a lot of advantage such as decreasing electricity peak, reducing construction expenses in additional power plant, operating natural gas storage facilities efficiently, and playing a role as distributed generations. Therefore, this study analyzes the economic feasibilities of gas cooling as an alternative for electric power load management.