• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.254-259
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• 2014

In this paper, we describe construction of a wind stabilization demo-site and effects of output power control of wind turbines for suppression of ramp rate using ESS (Energy Storage System). It is difficult to control the output power of distributed generator such as wind turbine which of variation is very large. If the large capacity wind farm be interconnected into power system may cause blackout due to Power Quality. For these reasons, the international standards such as Grid-Code is limited to less than 10 [%/min] of renewable energy ramp rate. The case of Korea, government actively conducts propagating large-scale renewable energy for green growth policy, to interconnecting more renewable energy into power system is necessary for stabilization technology. For these reasons, the POSCO consortium has constructed a wind stabilization demo-site that is configured as 500 [kWh] battery energy storage systems can output up to 3 [C-Rate] and two wind turbines rated 750 [kW]. In POSCO consortium, which implements various methods stabilizing output power of wind turbine such as smoothing, section firming and ramp control, we derive the results of long-term demonstration that can be controlled to satisfy to the international standard about ramp rate [%/kW] of wind turbine output power.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.3
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• pp.111-118
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• 2005

Since the residential load is an AC load and the output of solar cell is DC power, the photovoltaic system needs the DC/AC converter to utilize solar cell. In case of driving to interact with utility line, in order to operate at unity power factor, converter must provide the sinusoidal wave current and voltage with same phase of utility line. Since output of solar cell is greatly fluctuated by insolation, it is necessary that the operation of solar cell output in the range of the vicinity of maximum power point. In this paper, DC/AC converter is three phase PWM converter with smoothing reactor. And then, feed-forward control used to obtain a superior characteristic for current control and digital PLL circuit used to detect the phase of utility line.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.623-632
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• 2021

Through this study, we studied how to consider environment variables (such as temperatures, weekend, holiday) closely related to electricity demand, and how to consider the characteristics of Korea electricity demand. In order to conduct this study, Smoothing method, Seasonal ARIMA model and regression model with AR-GARCH errors are compared with mean absolute error criteria. The performance comparison results of the model showed that the predictive method using AR-GARCH error regression model with environment variables had the best predictive power.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.197-198
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• 2015

As the global to increase the wind power penetration in the power system, grid-integration standards have been proposed for the stable integration of the large-scale wind farm. Particularly, the low voltage ride through function has been emphasized, as it relates to the voltage and reactive power control of the wind turbine and the rest generators. This paper proposes the non-wind power generator control method in order to improve the wind power penetration. To prove the effectiveness of the proposed strategy, the simulation study is implemented in the Jeju power system. It can improve the wind power penetration by the effective control of the control generators.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.39-45
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• 2013

This paper presents the estimation of minimum BESS capacity for regulating the output of wind farms considering power grid operating condition in Jeju Island. To analyze the characteristics of wind farm outputs with a BESS, the real data of wind farms, Sung-San, Sam-dal and Hang-Won wind farm, located in the eastern part of Jeju island is considered. The wind farms are connected to Sung-san substation to transfer the electric power to Jeju power grid. Consequently, at PCC (Point of Common Coupling), it can see a huge wind farm connected to the substation and thus it can be expected that the smoothing effect is affected by not only the different wind speeds for each area but also the different mechanical inertia of wind turbines. In this paper, two kinds of simulation have been carried out. One is to analyze the real data of wind farm outputs during a winter season, and the other is to connect a virtual BESS to eliminate the unintended generating power changes by the uncontrolled wind farm outputs as shown in the former data. In the conclusion, two kinds of simulation results show that BESS installed in the substation is more efficient than each wind farms with BESS, respectively.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.5
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.503-505
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• 2012

본 연구에서는 실증기능 구현을 위해 75KW로 설계된 리튬-폴리머배터리(Lithium Polymer Battery 이하 배터리)와 ESS(Energy Storage System) 내장형 Hybrid-PCS(이하 PCS)을 이용하여 불균일한 풍력 발전원에 대한 Smoothing 제어 및 정출력 제어 대한 연구를 진행 하였다. 잦은 전류 변동으로 인해 발생되는 출력 변동의 안정화를 위해 인버터 전류제어, 벅/부스트 전압제어 기법을 사용하였다. 최적의 Smoothing 제어를 위한 필터 계수 값을 찾기 위한 시험을 진행 하였으며 Energy shift을 위한 정출력 시험도 진행 하였다. 또한 각 제어 모드에서 ESS 중요 파라미터인 SOC(State Of Charge 이하 SOC)변화를 배터리 특성과 관련하여 분석해 보았다.