• 전기학회논문지
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• 제58권8호
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• pp.1504-1511
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• 2009

By development of renewable energies and high-efficient facilities and deregulated electricity market, the operation cost of distributed generation(DG) becomes more competitive. The amount of distributed resource is considerably increasing in the distribution network consequently. Also, international environmental regulations of the leaking carbon become effective to keep pace with the global efforts for low-carbon paradigm. It contributes to spread out the business of DG. Therefore, the operator of DG is 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, community energy system(CES) having DGs is recently 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 the transmission service charges and etc. Therefore, CES who owns DGs has to control the produced electric power per hourly period in order to maximize the profit. If there is no regulation for carbon emission(CE), the generators which get higher production than generation cost will hold a prominent position in a competitive price. However, considering the international environment regulation, CE newly 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 will introduce 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 and Particle Swarm Optimization (PSO) will be used to solve this problem. The optimal operation of DG represented in this paper is to be resource to CES and system operator for determining the decision making criteria.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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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

• 대한전기학회논문지:전력기술부문A
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• 제54권10호
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• pp.480-486
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• 2005

This paper proposes a method to consider an aging failure probability and survival probability of power system components, though only aging failure probability has been considered in existing mean life calculation. The estimates of the mean and its standard deviation is calculated by using Weibull distribution, and each estimated parameters is obtained from Data Analytic Method (Type H Censoring). The parameter estimation using Data Analytic Method is simpler and faster than the traditional calculation method using gradient descent algorithm. This paper shows calculation procedure of the mean life and its standard deviation by the proposed method and illustrates that the estimated results are close enough to real historical data of combustion turbine generating units in Korean systems. Also, this paper shows the calculation procedures of a probabilistic failure prediction through a stochastic data analysis. Consequently, the proposed methods would be likely to permit that the new deregulated environment forces utilities to reduce overall costs while maintaining an are-related reliability index.

• 조명전기설비학회논문지
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• 제24권6호
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• pp.52-59
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• 2010

지속적인 전력수요 증가에 따라 대단위 발전설비가 증설되고 있다. 특히, 국제적인 친환경 기조에 따라 배전계통 내에 신재생 에너지를 기반으로 하는 분산전원이 급속히 확산되고 있다. 또한, 전력공급의 신뢰도 향상 및 계통 운영의 유연성을 위해서 배전계통이 망상 형태로 점점 복잡해지고 있는 실정이다. 이러한 변화는 사고시의 고장전류 크기를 증가시킨다. 따라서, 앞으로 고장전류는 현재 설치된 차단기의 차단 한계용량을 넘어설 것으로 예상된다. 이를 위한 해결책으로 보호설비의 교체나 용량증대 등을 들 수 있다. 하지만, 기술적 경제적인 측면을 고려해 볼 때 초전도한류기가 그 대안이 될 수 있다. 본 논문에서는 초전도한류기의 최적 위치를 선정하는 기법을 제안한다. 최적 위치는 초전도한류기의 복합배전계통 내 설치 위치에 따른 설비별 고장전류의 저감에 기초해 설비별 고장율을 재산정하고 이를 통한 수용가별 신뢰도 향상을 신뢰도 민감도 지수를 이용하여 산정한다. 또한, 수지형 계통 및 망상형 계통에 제안한 초전도한류기의 최적 위치 기법을 적용함으로써 그 타당성을 입증하였다.