• Korean System Dynamics Review
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• v.3 no.1
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• pp.43-60
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• 2002

This paper describes the forecast of wholesale price in competitive Korean electricity market using the system dynamics approach. The system dynamics concepts have been implemented with the Ithink software. This software facilitates the development of stock and flow model with information feedback. Using this model, the future wholesale electricity price can be computed hour by hour, quarterly, and yearly. This model also gives the energy planner the opportunity to create different scenarios for the future of deregulated wholesale markets in Korea. Also It will lead to increased understanding of competitive wholesale market as a complex, dynamic system. Research results show that the plant construction appeared in waves of boom and bust in Korean electricity market like real estate construction. That is, the Korea wholesale market's new power plants and the market price will appear the Boom and Bust cycle. It is very similar behavior as real estate industry. In case of consideration of DSM program, The DSM savings lead to a somewhat different timing of the booms in construction and of price spikes. But the DSM programs do not eliminated the fundamental dynamics of the boom and bust. And the wholesale price is maintained at the lower level compared to the case of without DSM program. However, the unexpected result is found that due to the lower market price, Investor make significantly less investment in new CCs, which leads to the higher wholesale price after 2010. It suggests that the DSM Policy must be implemented with the dynamics of competitive Electricity Market.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.346-352
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• 2009

The eventual goal of this paper is to help the generating companies and load-serving entities to choose appropriate relative levels of interconnected system versus bilateral trades while considering risk, and economic performance. In competitive power markets, electricity prices are determined by balance between demand and supply in electric power exchanges or bilateral contracts. The problem formulation is bilateral contract incorporated into Multi-area unit commitment with import/export and tie-line constraints. This proposed method considers maximizing own profit or minimize the operating cost among the generating companies in multi-area system. The feasibility of the proposed algorithm has been demonstrated using IEEE system with four areas and experimental results shows that proposed method is reliable, fast and computationally efficient

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.681-688
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• 2012

This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.

• Journal of Electrical Engineering and Technology
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• v.5 no.3
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• Journal of Power Electronics
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• v.11 no.1
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• pp.90-96
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• 2011

Congestion management is one of the most challenging aspects in the recently deregulated electricity markets. FACTS devices have been shown to be an efficient alternative to control the flow of power in lines, resulting in increased loadability, lower system loss and a reduced cost of production. In this paper, the application of a static series synchronous compensator (SSSC) for the purpose of congestion management of power systems has been studied. A sensitivity-based analysis method is utilized for effective determination of the SSSC location in an electricity market. The method is topology based and it is independent of the system operation point. A power injection p-model is developed for the SSSC in this study. Numerical results based on the modified IEEE 14 bus system with/without the SSSC demonstrate the feasibility as well as the effectiveness of the SSSC for congestion management in a network. The results obtained when using the SSSC to improve system transfer capability and congestion management is encouraging.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.10
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• pp.500-506
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• 2005

With the advent of electric power systems moving to a deregulated retail electricity market environment, calculating distribution service tariffs has become a challenging theme for distribution industries and tariff regulators. As distribution business remains as a monopoly, it is necessary to be regulated. And as multiple distribution companies compete with each other, it would be efficient to adopt competition to the determination of distribution service tariffs. This paper proposes a method to calculate distribution service tariffs using yardstick regulation, which can lead to competition among multiple distribution companies. The proposed method takes into account not only recovering revenue requirements but also the advantages of the yardstick regulation based on long-term marginal costs of distribution network expansion algorithms. A computer simulation is carried out to illustrate effectiveness of the proposed method and it is estimated that the algorithm can be applied to compute the distribution service tariffs under retail electricity markets.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.5
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• pp.78-84
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• 2006

As electric power systems have been moving from a vertically integrated structure to a deregulated environment, calculating reactive power service charges is a new challenging theme for market operators. This paper examines various methods for reactive power management adopted in some deregulated foreign and domestic markets and then proposes a new method to calculate reactive power service charges using a reactive power market. The reactive power market is operated based on bids from the generating sources and is settled on uniform prices by running reactive OPF programs after the day-ahead electricity market. The proposed method takes into account recovering not only the costs of installed capacity but also the lost opportunity costs incurred by reducing active power output to increase reactive power production. A numerical sample study is carried out to illustrate the processes and appropriateness of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1480-1491
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• 2015

In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.18-27
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• 2007

Successful operation of power system under regulated as well as deregulated electricity markets is very important. This paper presents marginal power flow evaluation of KEPCO system in view point of physical and operation mode by using Physical and Operational Margins (POM Ver.2.2), which is developed by V&R Energy System Research. This paper introduces feature and operation mode of POM Ver.2.2 and then evaluates scenarios of 6 lines contingencies of 765kv of KEPCO system at peak load time on summer in 2006 you. The case study for actual 2006 year KEPCO system shows that this POM program is applicable sufficiently to KEPCO system. Futhermore, it demonstrates that it is helpful for operator's operating the system successfully by evaluating physical and operational margins quickly for various contingencies occurred in KEPCO system. Eventually, it will assist operators to operate more reliably the KEPCO system in future.