• 대한전기학회논문지:전력기술부문A
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• 제54권3호
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• pp.144-151
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• 2005

This paper presents a forecasting technique of the short-term marginal price (SMP) using an Artificial Neural Network (ANN). The SW forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approaches, and the resulting patterns are used to train the ANN. Performances of the two approaches are compared and the better estimate is selected by a composition rule to forecast the SMP. By combining the two approaches, the proposed composition technique reflects the characteristics of hourly, daily and seasonal variations, as well as the condition of sudden changes in the spot market, and thus improves the accuracy of forecasting. The proposed method is applied to the historical real-world data from the Korea Power Exchange (KPX) to verify the effectiveness of the technique.

• 전기학회논문지
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• 제60권12호
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.389-391
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• 2002

This paper presents the System Marginal Price(SMp) forecasting implementation using backpropagation Neural Networks in Competitive Electricity Market. SMP is very important term to seek the maximum profit to bidding participants. Demand and SMP that necessary data for training Neural Networks, supplied from Korea Power Exchange(KPX). Statistic analysis about predicted SMP presents a part of consideration in end of this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
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• pp.210-212
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• 1999

This paper proposes a novel wavelet transform based technique for prediction of System Marginal Price(SMP). In this paper, Daubechies D1(haar), D2, D4 wavelet transforms are adopted to predict SMP and the numerical results reveal that certain wavelet components can effectively be used to identify the SMP characteristics with relation to the system demand in electric power systems. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to predict the SMP on the next scheduling day through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed wavelet transform approach can be used as an attractive and effective means for the SMP forecasting.

• 에너지공학
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• 제11권1호
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• pp.59-66
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• 2002

수직 통합된 체제의 전력회사가 6개의 발전회사와 1개의 판매회사로 분리되고 전력거래소를 통한 전력거래가 본격화되면서 발전회사는 자체 소유 발전설비의 공급가능용량을 어떻게 활용하느냐에 따라 영업상 수익의 영향을 받게 된다. 특히, 하루 전 발표되는 한계가격(System Marginal Price, Base Load Marginal Price)에 맞도록 전력생산을 위한 발전 비용함수를 적용한다면 익일의 공급가능용량이 최적 배분됨으로써, 변동비 반영 시장(Cost based Generation Pool)과 입찰가격 반영 발전시장(Price Bidding Generation Pool)에 적용될 계통운영보조서비스의 계약 물량 산출 및 익일 생산비용의 최적화를위한 입찰전략(Bidding Strategies) 수립이 가능해 지므로 보유 설비에 대한 최적이용이 가능하게 된다. 따라서 본 논문에서는 수요예측 오차와 과거 시장운영 실적을 기초로 년 간 유지보수 계획을 수립하고, 계통운영보조서비스에 대한 계약물량 산출과 개개 발전기의 비용함수 산출, 적용을 통한 발전설비의 효율적인 입찰 방안에 대해 논하고자 한다.

• Korean Chemical Engineering Research
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• 제50권3호
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• pp.479-491
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• 2012

본 연구에서는 공급자와 소비자, 열 저장시설과 연계네트워크로 구성된 통합 지역난방시스템의 경제성을 평가하고 최적 운전조건을 규명하였다. 최적화에 있어서는 혼합 정수선형 계획법이 이용되었으며 1주일 동안의 열 요구량을 만족함과 동시에 통합 지역난방 시스템의 운전제한 조건에 따른 전체 운영비용을 목적함수로 하였다. 지역난방 네트워크 연결망을 열 병합 발전이 포함되지 않은 구역과 이를 포함하는 구역으로 나누어 최적화를 진행함으로써 열 병합 발전에 의한 비용절감 효과를 확인할 수 있었다. 아울러 계통한계가격 예측모델에 의해 예측된 계통한계가격과 실제 계통한계가격을 각각 적용하여 최적화를 진행하고 그 결과를 비교 분석하였다. 수치모사 결과 개발된 최적화 운영시스템의 도입에 의해 통합 지역난방시스템의 에너지 효율성이 증가함을 확인할 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 추계학술대회 논문집 전력기술부문
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• pp.438-440
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• 2003

This paper resents an application of an Artificial Neural Network(ANN) technique to forecast the short-term system marginal price(SMP). The forecasting of SMP is a very important factor in an electricity market for the optimal biddings of market participants as well as for the market stabilization of regulatory bodies. The proposed neural network scheme is composed of three layers. In this process, input data are set up to reflect market conditions. And the $\lambda$ that is the coefficient of activation function is modified in order to give a proper signal to each neuron and improve the adaptability for a neural network. The reposed techniques are trained validated and tested with the historical real-world data from korea Power Exchange(KPX).

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2227-2235
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• 2018

Under the current policies and compensation rules in South Korea, Photovoltaic (PV) generation supplier can maximize the profit by combining PV generation with Energy Storage System (ESS). However, the existing operational strategy of ESS is not able to maximize the profit due to the limitation of ESS capacity. In this paper, new ESS scheduling algorithm is introduced by utilizing the System Marginal Price (SMP) and PV generation forecasting to maximize the profits of PV generation supplier. The proposed algorithm determines the charging time of ESS by ranking the charging schedule from low to high SMP when PV generation is more than enough to charge ESS. The discharging time of ESS is determined by ranking the discharging schedule from high to low SMP when ESS energy is not enough to maintain the discharging. To compensate forecasting error, the algorithm is updated every hour to apply the up-to-date information. The simulation is performed to verify the effectiveness of the proposed algorithm by using actual PV generation and ESS information.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.369-372
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• 2000

An overview on applications of wavelet transform in power systems presented in this paper. Wavelet transform is capable of making trade-offs between time and frequency resolutions, which is a property that makes it appropriate for the analysis of non stationary signal. In recent years, wavelet transform is widely accepted as a technology offering an alternative way due to its flexibility in representation of non-stationary signal even in power systems. This paper presents various applications of wavelet transform in power systems. Wavelet transform has been used by the authors in the field of power system protection for the classification of transient signals, and forecasting of short term loads and system marginal price and so on. Various research works carried out by many researchers in power systems are summarized.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.580-583
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• 2008

As the construction of renewable energy generators is on the rise and gets bigger in size, researchers pay more and more attention to the impact of such facilities on the power market as well as on the stability of power grid system. In Korea, while studies on the latter, including calculating the marginal capacity of renewable energy generators, is being made, those on the former has not yet been performed. As such, this paper analyses the impact of a big renewable energy generators on the price and transaction cost of domestic power market and proposes ideas to minimize such influence by applying the technology of forecasting renewable energy.