• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.748-750
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• 2005

This paper approaches a methodology for deciding the optimal reliability criteria for an optimal composite power system expansion planning considering generation and transmission systems simultaneously. A probabilisticreliability criterion, $LOLE_R$(Loss of Load Expectation), is used in this study. The optimalreliability criterion $LOLE_R*$ is decided at minimum cost point of total cost curve which is the sum of the utility cost associated with construction cost and the customer outage cost associated with supply interruptions for load considering forced outage rates of elements(generators and lines) in long term forecasting. The characteristicsand effectiveness of this methodology are illustrated by the case study using MRBTS size system.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.791-804
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• 2020

Residential electricity consumption can be predicted more accurately by utilizing the realtime household electricity consumption reference that can be collected by the AMI as the ICT developed under the smart grid circumstance. This paper studied the model that predicts residential power load using the ARIMA, TBATS, NNAR model based on the data of hour unit amount of household electricity consumption, and unlike forecasting the consumption of the whole households at once, it computed the anticipated amount of the electricity consumption by aggregating the predictive value of each established model of cluster that was collected by the households which show the similiar load profile. Especially, as the typical time series data, the electricity consumption data chose the clustering analysis method that is appropriate to the time series data. Therefore, Dynamic Time Warping and Periodogram based method is used in this paper. By the result, forecasting the residential elecrtricity consumption by clustering the similiar household showed better performance than forecasting at once and in summertime, NNAR model performed best, and in wintertime, it was TBATS model. Lastly, clustering method showed most improvements in forecasting capability when the DTW method that was manifested the difference between the patterns of each cluster was used.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1019-1023
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• 2018

In this paper sensitivity analysis of temperature on special day electricity demand of land and Jeju Island is performed. The basic electricity demand per 3 hours is defined as electricity demand that reflects the GDP effect without the temperature influence. The temperature sensitivity per 3 hours is calculated through the relationship between special day electricity demand normalized to basic electricity demand and temperature. In the future, forecast error will be improved if the temperature sensitivity per 3 hours is applied to the special day load forecasting.

• KIEE International Transactions on Power Engineering
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• v.5A no.1
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• pp.62-69
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• 2005

The optimal design of transmission system expansion planning is an important part of the overall planning task of electric power system under competitive electricity market environments. One of main keys of the successful grid expansion planning comes from optimal reliability level/criteria decision, which should be given for constraint in the optimal expansion problem. However, it's very difficult to decide logically the optimal reliability criteria of a transmission system as well as generation system expansion planning in a society. This paper approaches a methodology for deciding the optimal reliability criteria for an optimal transmission system expansion planning. A deterministic reliability criteria, BRR (Bus Reserve Rate) is used in this study. The optimal reliability criteria, BRR/sup */, is decided at minimum cost point of total cost curve which is the sum of the utility cost associated with construction cost and the customer outage cost associated with supply interruptions for load considering bus reserve rate at load buses in long term forecasting. The characteristics and effectiveness of this methodology are illustrated by the case study using IEEE-RTS.

• Wind and Structures
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• v.36 no.2
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• pp.121-131
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• 2023

Aiming at the problem that fatigue characteristics of metal roof rely on local physical tests and lacks the cyclic load sequence matching with regional climate, this paper proposed a method of constructing the fatigue load spectrum based on integration of wind load model, measured data of long-span metal roof and climate statistical data. According to the turbulence characteristics of wind, the wind load model is established from the aspects of turbulence intensity, power spectral density and wind pressure coefficient. Considering the influence of roof configuration on wind pressure distribution, the parameters are modified through fusing the measured data with least squares method to approximate the actual wind pressure load of the roof system. Furthermore, with regards to the wind climate characteristics of building location, Weibull model is adopted to analyze the regional meteorological data to obtain the probability density distribution of wind velocity used for calculating wind load, so as to establish the cyclic wind load sequence with the attributes of regional climate and building configuration. Finally, taking a workshop's metal roof as an example, the wind load spectrum is constructed according to this method, and the fatigue simulation and residual life prediction are implemented based on the experimental data. The forecasting result is lightly higher than the design standards, consistent with general principles of its conservative safety design scale, which shows that the presented method is validated for the fatigue characteristics study and health assessment of metal roof.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.142-144
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• 2005

본 연에서는 현재 전력거래소에서 사용하고 있는 단기수요예측 산법을 전력시장 운영환경에 대응 하도록 보다 정확하면서도 공정성이 보장되는 산법으로 개선하였다. 접근방법은 기존의 산법들을 면밀히 분석한 다음 산법의 개선과 매개변수의 튜닝을 통하여 예측정확도를 개선하였으며, 예측과정의 투명성을 확보하기 위하여 예측절차를 출력하는 기능을 포함하였다 예측정확도를 개선하기 위한 주요 방안으로 종합분석모형의 경우는 실적자료가 생길 때 마다 즉시 민감도가 갱신되도록 하였으며, 회귀분석모형은 분석과정에서 의미가 있는 자료만을 선택하도록 하였다. 또한 신경망 모형의 경우는 모의를 통하여 최적의 입력변수를 찾아 설정하였으며, 지식기반모형에서는 최근의 수요특성을 분석하여 새로운 규칙들로 구축하였다. 제안한 산법의 효용성을 평가하기 위하여 2004년도 실계통 자료를 대상으로 모의를 해 본 결과, 모든 산법에서 개선된 예측정확도를 나타내었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.187-192
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• 2019

Hybrid power storage system with emergency power function for demand management and power outage minimizes the investment cost in the building of buildings and factories requiring emergency power generation facilities, We propose a new business model by developing technology that can secure economical efficiency by reducing power cost at all times. Normally, system power is supplied to load through STS (Static Transfer Switch), and PCS is connected to system in parallel to perform demand management. In order to efficiently operate the electric power through demand forecasting, the EMS issues a charge / discharge command to the ESS as a PMS (Power Management System), and the PMS transmits the command to the PCS controller to operate the system. During the power outage, the STS is rapidly disengaged from the system, and the PCS becomes an independent power supply and can supply constant voltage / constant frequency power to the load side. Therefore, it is possible to secure reliability through verification of actual system linkage and independent operation performance of hybrid ESS, By enabling low-carbon green growth technology to operate in conjunction with an efficient grid, it is possible to improve irregular power quality and contribute to peak load by generating renewable energy through ESS linkage. In addition, the ESS is replacing the frequency follow-up reserve, which is currently under the charge of coal-fired power generation, and thus it is anticipated that the operation cost of the LNG generator with high fuel cost can be reduced.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.364-365
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• 2011

This study develops a new system for generation system simulation and operational planning (namely, KPWR-X) including GMS(Generator Maintenance Scheduling), UC(Unit Commitment), and LF(Load Forecasting) for new power system environment in recent. The KPWR-X provides operator and planner to help the generation system more safely and economically. GUI developed in this study makes operator feel in convenient to control whole power system. In future, it is expected that generation company, ISO, and fuel procurement, etc. may use an instructional tool developer's suggestion for application. It will be also applicable to establish the operational strategies for generation control, fuel procurement and power system risk management.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.31-40
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• 2024

In the summer, when the cooling load rises due to high temperatures, the hourly demand increases during the day and is relatively less at night compared to the day. These characteristics are considered important information in predicting summer electricity demand. However, if time information is simply expressed as a dummy variable, the model simply recognizes differences between time zones rather than learning changes in time. In this study, we would like to approach this problem by using a time variable using the Fourier transform. Time variables using the Fourier transform will be able to effectively learn differences between times. As a result of evaluating the type of time variable in the summer electricity demand forecast for 2022 and 2023 using the BiGRU model, the model using the time variable using Fourier transform showed the best performance with MAPE of 2.01% and 2.04% confirmed. The results of this study are expected to improve prediction accuracy in the summer when power usage increases and prevent problems such as large-scale power outages.