• Journal of Energy Engineering
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• v.24 no.2
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• pp.59-71
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• 2015

This study attempts to examine the economic impacts of electricity industry in Korea and Japan using an inter-industry analysis. Specifically, the study analyzes and compares electricity industry between Japan and Korea through production-inducing effect and value added inducing effect of electricity industry based on demand-driven model. Moreover, this study deals with supply shortage effect and sectoral price effect by using supply-driven model and Leontief price model, respectively. This study analyses the electricity industry through exogenous approach. The results show that electricity industry induces prodution-inducing effect of 0.5946 won in other industries in Korea and 0.5446 yen in other industries in Japan. Value-added-inducing effects are 0.1716 won in other in other industries in Korea and 0.2929 yen in other industries in Japan. Supply shortage effects of electricity industry are 1.5932 won in other industries in Korea and 1.2801 yen in other industries in Japan. And sectoral price effects are 0.2113% in Korea and 0.2196% in Japan due to the price increase of 10% of electricity industry.

• 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).

• Korean System Dynamics Review
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• v.2 no.2
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• pp.25-40
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• 2001

This paper describes the forecast of power plant construction in a competitive korean electricity market. In Korea, KEPCO (Korea Electric Power Corporation, fully controlled by government) was responsible for from the production of the electricity to the sale of electricity to customer. However, the generation part is separated from KEPCO and six generation companies were established for whole sale competition from April 1st, 2001. The generation companies consist of five fossil power companies and one nuclear power company in Korea at present time. Fossil power companies are scheduled to be sold to private companies including foreign investors. Nuclear power company is owned and controlled by government. The competition in generation market will start from 2003. ISO (Independence System Operator will purchase the electricity from the power exchange market. The market price is determined by the SMP(System Marginal Price) which is decided by the balance between demand and supply of electricity in power exchange market. Under this uncertain circumstance, the energy policy planners such as government are interested to the construction of the power plant in the future. These interests are accelerated due to the recent shortage of electricity supply in California. In the competitive market, investors are no longer interested in the investment for the capital intensive, long lead time generating technologies such as nuclear and coal plants. Large unclear and coal plants were no longer the top choices. Instead, investors in the competitive market are interested in smaller, more efficient, cheaper, cleaner technologies such as CCGT(Combined Cycle Gas Turbine). Electricity is treated as commodity in the competitive market. The investors behavior in the commodity market shows that the new investment decision is made when the market price exceeds the sum of capital cost and variable cost of the new facility and the existing facility utilization depends on the marginal cost of the facility. This investors behavior can be applied to the new investments for the power plant. Under these postulations, there is the potential for power plant construction to appear in waves causing alternating periods of over and under supply of electricity like commodity production or real estate production. A computer model was developed to sturdy the possibility that construction will appear in waves of boom and bust in Korean electricity market. This model was constructed using System Dynamics method pioneered by Forrester(MIT, 1961) and explained in recent text by Sternman (Business Dynamics, MIT, 2000) and the recent work by Andrew Ford(Energy Policy, 1999). This model was designed based on the Energy Policy results(Ford, 1999) with parameters for loads and resources in Korea. This Korea Market Model was developed and tested in a small scale project to demonstrate the usefulness of the System Dynamics approach. Korea electricity market is isolated and not allowed to import electricity from outsides. In this model, the base load such as unclear and large coal power plant are assumed to be user specified investment and only CCGT is selected for new investment by investors in the market. This model may be used to learn if government investment in new unclear plants could compensate for the unstable actions of private developers. This model can be used to test the policy focused on the role of unclear investments over time. This model also can be used to test whether the future power plant construction can meet the government targets for the mix of generating resources and to test whether to maintain stable price in the spot market.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.141-163
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• 2022

This paper empirically analyzes the effect of renewable electricity generation on the System Marginal Price (SMP) in Korea. Using an ARX-GARCHX model with hourly data from 2016 to 2020, we evaluate SMP determinants and merit order effects. As a result, we find that solar and wind power, as well as gas price and total load, play a critical role in the SMP. In particular, solar power reduces the SMP level but raises volatility during peak and off-peak periods. This result implies that SMP may fall as renewable electricity generation increases, leading to a decrease in the profitability of existing power plants and investment in renewables. On the other hand, even if the subsidy of renewable energy increases the burden on the SMP, it can be offset by the merit order effect, which lowers the SMP.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1157-1166
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• 2008

This paper presents a new approach for the evaluation of location marginal prices (LMPs) considering demand-side bidding (DSB) in a competitive electricity market. The stabilization of the electric power supply and demand balance is one of the major important activities in electric power industry. In this paper, we present an analytical method for calculation of LMPs considering DSB, which has opportunity to compete with generating units, as England & Wales Pool's DSB scheme[1]. Also, we propose a new approach that LMP considering DSB is divided into three components. The proposed approach can be used for the evaluation of demand-side bidding into the electricity market and the assessment of the influence of DSB on total production costs and LMPs as well as three components.

• Korean Chemical Engineering Research
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• v.52 no.6
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• pp.796-806
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• 2014

The key for the commercial deployment of IGCC power plants or chemical (methanol, dimethyl ether, etc.) production plants based on coal gasification is their economic advantage over plants producing electricity or chemicals from crude oil or natural gas. The better economy of coal gasification based plants can be obtained by co-production of electricity and chemicals. In this study, we carried out the economic feasibility analysis on the process of co-producing electricity and DME (dimethyl ether) using coal gasification. The plant's capacity was 250 MW electric and DME production of 300,000 ton per year. Assuming that the sales price of DME is 500,000 won/ton, the production cost of electricity is in the range of 33~58% of 150.69 won/kwh which is the average of SMP (system marginal price) in 2013, Korea. At present, the sales price of DME in China is approximately 900,000 won/ton. Therefore, there are more potential for lowering the price of co-produced electricity when comparing that from IGCC only. Since the co-production system can not only use the coal gasifier and the gas purification process as a common facility but also can control production rates of electricity and DME depending on the market demand, the production cost of electricity and DME can be significantly reduced compared to the process of producing electricity or DME separately.

• Journal of Energy Engineering
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• v.15 no.1 s.45
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• pp.14-22
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• 2006

Various pros and cons are raised as to the nuclear and renewable power portions. In order to generate scientific, objective, and comparative data, this study reviewed energy policies of some countries and derived 34 possible energy mix scenarios depending on the nuclear portion, the renewable portion and the make-up power sources. For each scenario, the unit electricity cost was calculated using the BLMP (Base Load Marginal Price) and SMP (System Marginal Price) methodology, which is currently adopted in Korean electricity market. The unit electricity cost for the current energy mix was 22.18 Won/kWh and those fir other scenarios spreaded from 19.74 to 164.07 Won/kWh excluding the transmission costs and profits of the electric utility companies. Generally, the increased nuclear power portion leads reduction in the unit electricity cost while the trend is reversed in the renewable power portion. Notable observation is that when the renewable power portion exceeds 20%, as the scenario cannot enjoy the benefit of cheap base load, the unit electricity cost at low demand time zone is increased.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.183-190
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• 2018

Predicting accurate electricity prices is an important task in the electricity trading market. To address the electricity price forecasting problem, various approaches have been proposed so far and it is known that linear regression-based approaches are the best. However, the use of such linear regression-based methods is limited due to low accuracy and performance. In traditional linear regression methods, it is not practical to find a nonlinear regression model that explains the training data well. If the training data is complex (i.e., small-sized individual data and large-sized features), it is difficult to find the polynomial function with n terms as the model that fits to the training data. On the other hand, as a linear regression model approximating a nonlinear regression model is used, the accuracy of the model drops considerably because it does not accurately reflect the characteristics of the training data. To cope with this problem, we propose a new electricity price forecasting method that divides the entire dataset to multiple split datasets and find the best linear regression models, each of which is the optimal model in each dataset. Meanwhile, to improve the performance of the proposed method, we modify the proposed localized linear regression method in the map and reduce way that is a framework for parallel processing data stored in a Hadoop distributed file system. Our experimental results show that the proposed model outperforms the existing linear regression model. Specifically, the accuracy of the proposed method is improved by 45% and the performance is faster 5 times than the existing linear regression-based model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1731-1737
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• 2007

The Korea electricity wholesale market is operated under the cost-based-pool system and the government regulation to the new generation capacities in order to insure the resource adequacy. The goal of government's regulation is the electricity market stability by attracting proper generation investment while keeping the reliability of system. Generation companies must mandatory observe that government plan by now. But if the restructuring is to be complete, generation companies should not bear any obligation to invest unless their profitability is guaranteed. Namely the investors' behavior will be affected by the market prices. In this paper, the system dynamics model for Korea wholesale electricity market to examine whether competitive market can help to stabilize is developed and analyzes the investors behavior. The simulation results show that market controlled by government will be operated stable without resulting in price spike but there is no lower price because of maintaining the reasonable reserve margin. However, if the competition is introduced and the new investment is determined by the investor's decision without government intervention, the benefits from lower wholesale price are expected. Nevertheless, the volatility in the wholesale market increases, which increases the investment risks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.