• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.6
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• pp.266-271
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• 2000

The electric power industries are moving from the conventional monopolistic or vertically integrated environments to deregulated and competitive environments, where each participant is concerned with profit maximization rather than system-wide costs minimization. Consequently, the conventional least-cost approaches for the generation resource schedule can not exactly handle real-world situations. This paper presents a game theory application for analyzing power transactions and market design in a deregulated energy marketplace, where the market participants determine the net profits through the optimal bidding strategies. The demand elasticity of the energy price is considered for the realistic modeling of the deregulated marketplace.

• Journal of Electrical Engineering and Technology
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• v.3 no.1
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• pp.14-19
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• 2008

The paper presents an intelligent time series model to predict uncertain electricity market price in the deregulated industry environment. Since the price of electricity in a deregulated market is very volatile, it is difficult to estimate an accurate market price using historically observed data. The parameter of an intelligent time series model is obtained based on the simultaneous perturbation stochastic approximation (SPSA). The SPSA is flexible to use in high dimensional systems. Since prediction models have their modeling error, an error compensator is developed as compensation. The SPSA based intelligent model is applied to predict the electricity market price in the Pennsylvania-New Jersey-Maryland (PJM) electricity market.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.19-21
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• 2000

This paper presents a game theory application for analyzing power transactions and market design in a deregulated energy marketplace such as PoolCo. The conventional least-cost approaches for the generation resource schedule can not exactly handle recent real-world situations. A systematic tool using game theory for the market participants is presented such that it determines the net profits through the optimal bidding strategies including the strategies for the bidding prices and bidding generations. We treat this power transaction game as incomplete information one, which means each market participants does not know other's cost function. And the demand elasticity of the energy price is considered for the realistic modeling of the deregulated marketplace.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1101-1103
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• 1999

Deregulation and restructuring of electric industry change the fundamental nature of electric business which will be coordinated by the evolved market structures such as spot market with pool and bilateral transaction structure, forward market and future market. Introduction of competition can significantly change the system operation in near-terms as well as long-run generation expansion planning Previous centralized planning by monopoly utilities which was guided for the public service purpose will be replaced by decentralized investments plan by individual generation companies in response to commercial incentives. This paper reviews WASP model as a centralized planning tool and presents a methodological analysis of generation expansion planning in deregulated power systems. It stresses how affects the process of planning new generation investments by the introduction of competition and how maintains proper fuel mix and continuously sustains system reliability under deregulated environments.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.1-7
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• 2015

The industry has rapidly growth and energy supply technology advanced are become main factor which to contribute of the harmonic losses. This problem is one aspect that may affect the capability of the transmission line and also to the efficiency of electricity. This paper proposes a new scheme to allocate the cost pertaining to transmission loss due to harmonics. The proposed method, called as Generalized Harmonic Distribution Factor, uses the principle of proportional sharing method to allocate the losses among the transmission users especially for industry consumers. The IEEE 14- and 30 bus test system is used to compare the proposed method with existing method. The results showed that the proposed method provided a scheme better in allocating the cost of transmission loss, which could encourage the users to minimize the losses.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.340-345
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• 2001

Electric power market in Japan is now on the trend of deregulation and privatization just like in Europe and the United States. And various approaches for risk management have been investigated taking the electric power price fluctuation after the deregulation into account. The behavior of the investment in power generation plants has not, however, been studied in detail yet due to the complexity of the problem. The problem of the investment in the deregulated power market is that of autonomous decentralized decision-making system, which includes various kinds of decision-makers, that is, power producers called IPPS Each generator has its own criteria for plant investment. Therefore, the total behavior of the decentralized power market will be so complicated, and normative approach will not be applicable fur this analysis. We have developed a simulation-based system fur behavioral analysis and also the framework design of the decentralized power market.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.530-537
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• 2010

By development of renewable energy and more efficient facilities in an increasingly deregulated electricity market, the operation cost of distributed generation (DG) is becoming more competitive. International environmental regulations of the leaking carbon become effective to reinforce global efforts for a low-carbon paradigm. Through increased DG, operators of DG are able to supply electric power to customers who are connected directly to DG as well as loads that are connected to entire network. In this situation, a community energy system (CES) with DGs is a new participant in the energy market. DG's purchase price from the market is different from the DG's sales price to the market due to transmission service charges and other costs. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize profit. Considering the international environment regulations, CE will be an important element to decide the marginal cost of generators as well as the classified fuel unit cost and unit's efficiency. This paper introduces the optimal operation of CES's DG connected to the distribution network considering CE. The purpose of optimization is to maximize the profit of CES. A Particle Swarm Optimization (PSO) will be used to solve this complicated problem. The optimal operation of DG represented in this paper would guide CES and system operators in determining the decision making criteria.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.1
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• pp.1-6
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• 2006

The emergence of competitive power market makes dispatch algorithm with transient stability constraints increasingly important for the transparent power system operation. Heuristic and off-line evaluation for the operation point can produce a discrimination among market players in the deregulated power system. In this paper, a dispatch algorithm with transient stability constraints is proposed. Energy margin under the TEF(Transient Energy Function) structure is adopted as a measure for the stability index. Implementation issues and simulation results are discussed in the context of a 10-bus system

• 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

• Korean System Dynamics Review
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• v.6 no.1
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• pp.99-123
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• 2005

Although the generation market is competitive, the power market is easily exercised the market power by one generator due to its special futures such as a limited supplier, large investment cost, transmission constraints and loss. Specially, as Korea Electric industry restructuring is similar US competitive wholesale electricity market structure which discovered the several evidences of market power abuse, when restructuring is completed the possibility that market power will be exercised is big. Market power interferes with market competitions and efficiency of system. The goal of this study is to investigate the market price effects of the potential market power and the proposed market power mitigation strategy in Korean market using the forecasting wholesale electricity market model. This modeling is developed based on the system dynamics approach. it can analyze the dynamic behaviors of wholesale prices in Korean market. And then it is expanded to include the effect of market condition changed by 'strategic behavior' and 'real time pricing.' This model can generate the overall insights regarding the dynamic impact of output withholding by old gas fire power plant bon as a marginal plant in Korean market at the macro level. Also it will give the energy planner the opportunity to create different scenarios for the future for deregulated wholesales market in Korea.