• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.8
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• pp.98-105
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• 2014

In this paper, we have analyzed the power consumption property of EVs(Electric Vehicles) chargers established in a public place, proposed reasonable demand factors by the number of established EV chargers and its control method in apartments. The optimization of power system and the suppression of the peak load can be controlled through the proposed demand factors and charging scheduling control algorithm. In this paper, electrical design and an case analysis were carried out on a sample apartment complex to prove the effectiveness of the power system. As a result, emergency power transformer capacity has been reduced by approximately 25%, and we have confirmed that the electric rates saving and the control of peak load value is possible.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.1
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• pp.17-23
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• 2013

The BESS(Battery Energy Storage System) is an useful device for load leveling, but the high cost, installation space and safety issues are the main barriers for supplying it widely. The important factor in supplying BESS to customers successfully is the payback period. As most of the H/W cost factors are uncontrollable, the optimization of storage volume can be useful factor in improving payback period. In order to obtain optimized BESS volume, the load factor, demand ratio, peak shaving ratio, electric rates and benefits from DR participation of customer should be analyzed. In this paper, we could verify the peak cutting capability and cost effectiveness under the some proposed conditions and changing value of PCS and battery based on the customers data after volume optimization process was applied, and we can identified the saturation point of load factor and shortening of customer's payback period.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.3
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• pp.44-50
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• 2014

This paper describes reasonable methods by considering change of outdoor temperature into Customer Baseline Load(CBL) of Demand Resources in Smart Demand Resource Market, which controls peak power demand and maintains reliability of power system. The Smart Demand Resouce Market, which KPX(Korea Power Exchange) implement, is explained and then effects for CBL calculated by considering temperature correction factor are established. Finally, four methods for calculation of CBL are proposed and those results are compared and analyzed.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.6
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• pp.40-44
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• 2014

In this paper, maximum power is the lowering device using the facility's energy use and peak load electricity through analyzing attitude should like to make it reduce its power base rate. Simulator to manage the demand for power, a maximum electric power base power from electronic watt-hour meters by a device's signal, predictive power, the current power by computing the goal of power for less than Maximum peak power and peak shift, so that you can manage, and peak York, which role you want a cut Metal heat treatment result which analyzes the data, demand for electricity company over the years of analyzing the characteristics of each load, and effects and Reducing power consumption device every month identified seven Sequence control to the load system and successful power control is about showing that the defined goals.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.285-287
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• 2008

This paper represents the electric power load leveling effect of the Light Control System(LCS). The lighting of typical mid-large commercial buildings is the major factor of daytime electric power consumption. Since the national peak power demand occurs in between 11:00 and 16:00, the dimming control of light can contribute the decrease of the power demand We will discuss the load leveling effect of dimming control with LCS.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.233-240
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• 2000

This paper presents fuzzy clustering and wavelet transform analysis based technique for the industrial hourly load forecasting fur the purpose of peak demand control. Firstly, one year of historical load data were sorted and clustered into several groups using fuzzy clustering and then wavelet transform is adopted using the Biorthogonal mother wavelet in order to forecast the peak load of one hour ahead. The 5-level decomposition of the daily industrial load curve is implemented to consider the weather sensitive component of loads effectively. The wavelet coefficients associated with certain frequency and time localization is adjusted using the conventional multiple regression method and the components are reconstructed to predict the final loads through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed composite model of fuzzy clustering and wavelet transform approach can be used as an attractive and effective means for the industrial hourly peak load forecasting.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.432-434
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• 2002

Recently, electricity demand of remote islands in Korea has been rapidly increased. It's mainly due to increase of income level resulted from economic development. Electricity demand patterns and characteristics in remote islands are different from those of mainland in point of time of peak load, demographic and industrial characteristics of islands, and so on. The optimal power supply in remote islands has a important relationship with accurate analysis of island's load characteristics, the adoption of relevant load forecasting technique, and optimal power facilities reflecting local's electricity demand characteristics. This paper shows the recent load pattern and characteristics, load forecasting using probability distribution, and the perpetration of relevant power facilities in remote islands.

• 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 the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.98-103
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• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.64-74
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• 2015

Because of the burgeoning demand of the energy, the countries are finding sustainable solutions for these emerging challenges. Demand Side Management is playing a significant role in managing the demand with an aim to support the electrical grid during the peak hours. However, advancement in controls and communication technologies, the aggregators are appearing as a third party entity in implementing demand response program. In this paper, a detailed mathematical framework is discussed in which the aggregator acts as an energy service provider between the utility and the consumers, and facilitate the consumers to actively participate in demand side management by introducing the new concept of demand reduction bidding (DRB) under constrained direct load control. Paper also presented an algorithm for the proposed framework and demonstrated the efficacy of the algorithm by considering few case studies and concluded with simulation results and discussions.