• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.6
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• pp.54-59
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• 2003

This paper presents hourly system marginal price forecasting of the Korea electric power system using a fuzzy linear regression analysis method. The proposed method is tested by forecasting hourly system marginal price for a week of spring in 2002. The percent average of forecasting error for the proposed method is from 3.14% to 6.10% in the weekdays, from 7.04% to 8.22% in the weekends, and comparable with a artificial neural networks method.

• Plant Journal
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• v.14 no.1
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• pp.47-51
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• 2018

This study analyzed the effect of power output reduction in coal fired power generation on the change of system marginal price and green house gas emissions. Analytical method was used for electricity market forecasting system used in korea state owned companies. Operating conditions of the power system was based on the the 7th Basic Plan for Electricity Demand and Supply. This as a reference, I analyzed change of system marginal price and green house gas emission by reduced power output in coal fired power generation. The results, if the maximum output was declined as 29 [%] to overall coal-fired power plant, system marginal price is reduced 12 [%p] compared to before and decreasing greenhouse gas emissions were 9,966 [kton]. And if the low efficiency coal fired power plant that accounted for 30 [%] in overall coal-fired power plant stopped by year, system marginal price is reduced 14 [%p] compared to before and decreasing greenhouse gas emissions were 12,874 [kton].

• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.107-128
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• 2021

It has been recognized that implementing the marginal price mechanism to CBP is not acceptable due to the lack of revenue of the marginal generators. This study shows that it is not the problem of marginal price mechanism but the structural problems originated by the suspension of restructuring, the technical limits of RSC program and inaccuracy of the generation cost estimation method. This study explains the method to calculate the cost function in operating modes of the CC generators and proposes the modeling for the CC generators in RSC program. To implementing the cost function in operating modes could give an opportunity to change the price setting mechanism from average to marginal cost. The price setting mechanism based on the marginal cost will be one of the main points to provide the right price signals and to introduce a real-time and A/S markets to prepare the energy transition era.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.871-881
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• 2010

In the uniform price electricity market or bilateral electricity market, the energy transactions in which the network is not considered and ISO's system operation costs which ISO try to minimize are settled separately. In this paper, transmission loss, one of the ISO's system operation costs, was dealt. The conventional marginal loss allocation method gives economic signals but three aspects have to be considered; excessiveness, arbitrariness and cross-subsidy. In this paper, marginal loss compensation efficiency method was suggested which consider those aspects of the conventional marginal loss allocation method. Also the characteristics of the marginal loss compensation efficiency were analyzed in the appendixes. And simple 2-bus system and IEEE 14 bus system were used to explain these characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.453-459
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• 2021

The system marginal price of electricity is the amount paid to all the generating units, which is an important decision-making factor for the construction and maintenance of an electrical power unit. In this paper, we suggest a long-term forecasting model for calculating the system marginal price based on prices of natural gas and oil. As most variables used in the analysis are nonstationary time series, the long run relationship among the variables should be examined by cointegration tests. The forecasting model is similar to an error correction model which consists of a long run cointegrating equation and another equation for short run dynamics. To mitigate the robustness issue arising from the relatively small data sample, this study employs various testing and estimating methods. Compared to previous studies, this paper considers multiple fuel prices in the forecasting model of system marginal price, and provides greater emphasis on the robustness of analysis. As none of the cointegrating relations associated with system marginal price, natural gas price and oil price are excluded, three error correction models are estimated. Considering the root mean squared error and mean absolute error, the model based on the cointegrating relation between system marginal price and natural gas price performs best in the out-of-sample forecast.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.2
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• pp.85-93
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• 2006

Since the SMP(System Marginal Price) is a vital factor to the market participants who intend to maximize the their profit and to the ISO(Independent System Operator) who wish to operate the electricity market in a stable sense, the short-term marginal price forecasting should be performed correctly. In an electricity market the short-term market price affects considerably the short-term trading between the market entities. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a new methodology for a day-ahead SMP forecasting using ARIMA(Autoregressive Integrated Moving Average) model based on the time-series method. And also the correction algorithm is proposed to minimize the forecasting error in order to improve the efficiency and accuracy of the SMP forecasting. To show the efficiency and effectiveness of the proposed method, the case studies are performed using historical data of SMP in 2004 published by KPX(Korea Power Exchange).

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.634-635
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• 2011

국내 전력시장은 발전기별 비용에 근거한 운영체계를 가지고 있다. 특히 각 발전기의 임시발전가격(IGP, Interim Generating Unit Price)은 발전기별 기동비용 및 무부하비용을 평활화하여 시간대별 가격으로 변환하고 증분비를 더함으로써 산정되고 있으며 그 중에서 가장 높은 임시발전가격이 계통한계가격(SMP, System Marginal Price)으로 결정되고 있으나, 발전비용 최소화를 위하여 단시간 가동되는 소규모 발전기의 발전력 배분으로 계통한계가격의 Spike 현상이 종종 발생하고 있다. 이는 수요증가 및 발전력 부족에 의한 영향보다는 발전비용 최소화 구현과정에서의 현상이므로 이를 해결하기 위한 대안을 검토하고 이에 따른 전력시장의 계통한계가격 영향을 검토하였다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.8
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• pp.341-350
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• 2006

This paper presents a new methodology to draw the components of locational marginal price (LMP) in electricity market. Recently, the changing environments surrounding electricity industries resulted in the unbundled services provided by electricity market players, which may require the new pricing mechanisms based on the LMP. The changed pricing mechanisms will provide the price signals of time and location to the market participants. Most of the existing studies of LMP are based on the Lagrangian multipliers as shadow prices to evaluate the equivalent values of constraints or factors for security, reliability and quality. However, the shadow prices cannot provide enough information for components of LMP. In this paper, therefore, we proposed a new approach that LMP is divided into three components. To do this, we first present the method for shadow prices calculation and then break down LMP into a variety of parts corresponding to the concerned factors. The proposed approach is applied to 5-bus and modified IEEE 14-bus sample system in order to verify its validity.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.819-821
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• 2005

Since the System Marginal Price (SMP) is a vital factor to the market entities who intend to maximize the their profit, the short-term marginal price forecasting should be performed correctly. In a electricity market, the short-term trading between the market entities can be generally affected a short-term market price. Therefore, the exact forecasting of SMP can influence on the profit of market participants. This paper presents a methodology of day-ahead SMP foretasting using Autoregressive Integrated Moving Average (ARIMA). To show the efficiency and effectiveness of the proposed method, the numerical studies have been performed using historical data of SMP in 2004.