• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.860-865
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• 2014

This paper proposes the method for determination of the capacities of ESS (Energy Storage System) and PCS (Power Conditioning System) for the peak load shaving based on the load data in the substation of urban railway. In addition, this paper analyze the actual measurement of loads in the substation of urban railway. The load of a weekday in the substation of urban railway is high around rush hours in the morning and evening, which is different from that of a normal substation. The required capacities of ESS and PCS become larger as the amount of peak load shaving is higher, and are affected from the patterns of daily load in the substation.

• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.208-209
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• 2019

본 논문에서는 ESS를 이용한 전력 계통의 Peak-Shaving 제어 알고리즘 수립에 대한 연구 결과를 제시한다. 과거의 요일별 전력수요 데이터를 이용하는 경우 ESS가 포함된 전력 시스템의 연결점(PCC)에서 전력 및 에너지 평형 방정식을 수립할 수 있고, ESS의 전력 및 에너지 용량에 따른 전기요금 최소화를 목적 함수로 한 선형 계획법 문제를 정식화할 수 있다. 선형 계획법 문제 해결을 통해 ESS가 추가되는 경우에 대한 경제성을 분석할 수 있고, 이로부터 ESS의 적합한 용량도 선정할 수 있다. 또한 부하 크기 및 시간대별 ESS 최적 전력 이용 패턴을 확인할 수 있기 때문에 실 운용 로직 설계를 위한 가이드로 결과를 활용할 수 있다. 본 논문에서는 제안된 방법을 설명하기 위한 일 예시로서, 시간대별 전력 부하 패턴의 기존 연구 결과로부터 도출한 부하 전력 곡선을 이용하여 선형 계획법을 이용한 Peak-Shaving 최적 제어 결과를 제시하고, 이를 기반으로 실시간 운전 가능한 규칙 기반 알고리즘을 설계하여 ESS의 적용에 따른 요금 절감 효과를 제시한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1152-1158
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• 2018

In this paper, Optimal operation plan through load analysis of industrial end-user is suggested. It calculated economic feasibility of ESS with detailed power lad analysis and conditions. Generally, if the latest maximum power is less than 30% of contracted power, it can not be peak shaving operation plan. and if the peak load level stays stead for 24 hours, it is difficult peak shaving for ESS. In addition to, When the peak load is occurred in summer or winter, a hybrid operation method combining the peak shaving plan and the time shift method is proposed. Therefore, When ESS is installed in industrial electrical customer, it is achieved best effect through the optimal operation plan.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.457-462
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• 2003

This paper presents an optimal operating strategy of distributed generation (DG) with reliability worth evaluation of distribution systems. Using DG for peak-shaving unit could reduce the overall system operating cost, and using DG for standby power unit could reduce the customer interruption cost. If DG operating cost is less than utility power cost in peak time, DG should be running to reduce the overall system operating cost. When customer interruption cost enlarges, however, standby power strategy may be the better operating strategy than peak-shaving strategy. Selection of whether DG should be operated for peak-shaving or for standby power, needs the accurate reliability worth evaluation and the accurate power cost evaluation. Instead of using annual average reliability worth, the concept of hourly reliability worth is introduced in this paper to determine the optimal operating decision of DG. Applying suggested hourly reliability worth, the distribution companies that possess DG could set up the optimal operating strategy of DG.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.4
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• pp.81-86
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• 2003

Using DG for peak-shaving unit could reduce the overall system operating cost, and using DG for standby power unit could improve the reliability of the distribution system The models of peak-shaving unit and standby power unit are different from each other. The Monte-Carlo simulation is suitable for the purpose of the analysis of two DG models. In this paper, the reliability indices are calculated from the time-sequential method, and the merit and defect of the peak-shaving unit and standby power unit are investigated.

• Journal of IKEEE
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• v.25 no.2
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• pp.388-398
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• 2021

This paper proposes a particle swarm optimization (PSO)-based peak shaving scheme using energy storage system (ESS) for electricity tariff reduction. The proposed scheme compares the actual load with the estimated load consumption, calculates the additional output power that the ESS needs to discharge additionally to reduce peak load, and adds the input. In addition, in order to compensate for the additional power, the process of allocating power to the determined point is performed, and an optimization that minimizes the average of the load expected at the active power allocations using PSO so that the allocated value does not affect the peak load. To investigated the performance of the proposed scheme, case study of small and large load prediction errors was conducted by reflecting actual load data and load prediction algorithm. As a result, when the proposed scheme is performed with the ESS charge and discharge control to reduce electricity tariff, even when the load prediction error is large, the peak load is successfully reduced, and the peak load reduction effect of 17.8% and electricity tariff reduction effect of 6.02% is shown.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1125-1129
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• 2015

Increasing peak load is one of the major concerns about operation of urban railroad systems. Since ESSs (Energy Storage Systems) have a great potential for shaving the peak load, there has been a growing interest in the use of ESS for peak load reduction. Also, ESS can be optimally scheduled to minimize the electricity purchasing cost under a given ToU (Time-of-Use) tariff by taking advantage of electricity price difference between peak and off-peak time. This paper presents a Mixed Integer Programming (MIP)-based ESS scheduling method to minimize the electricity purchasing cost under a ToU tariff for an urban railroad system.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.720-723
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• 1996

The recent summer power peak crisis has been caused by excessive use of cooling loads at daily peak time in summer. The yearly load shape of KEPCO has gradually became very steep valley. Under this situation, more efficient DSM(Demand Side Management) tools are fully required for summer peak clipping and shaving. In this paper, the KEPCO's Jeju-Do model project for DSM, especially for Demand Controller, is presented. Demand Controller was evaluated to have the very high economical efficiency against the investment in equipment, as compared with another DSM tools. There were some serious problem to apply Demand Controller to many customers in the aspect to synchronization with KEPCO's watthour meter. But these problems have solved by Keyin's new Demand Controller using vision algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1199-1205
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• 2014

A power system configuration has been increasingly advanced with a number of generating units. In particular, renewable energy resources are widely introduced due to the environmental issues. When applying the renewable energy sources with the ESS (Energy Storage System), the ESS is the role of a potential generating resource in the power system while mitigating the output volatility of renewable energy resources. Thus, for applications of the ESS, the surrounding environment of it should be considered, which means that capacity and energy of the ESS can be affected. Moreover, operation strategy of the ESS should be proposed according to the installation purpose as well as the surrounding environment. In the paper, operation strategy of the ESS is proposed considering load demand and the output of renewable energy resources on a hourly basis. Then, the cost of electrical energy is minimized based on the economic model that consists of capital cost, operation cost, fuel cost, and grid cost for a year. It is sure that peak-shaving effects can be achieved while satisfying the minimum cost of electrical energy.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.2
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• pp.73-79
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• 1987

This paper describes a new algorithm to evaluate the production cost for a generation system including energy-limited hydroelectric plants. The algorithm is based upon the analytical production costing model developed under the assumption of Gaussian probabilistic distribution of random load fluctuations and plant outages. Hydro operation and pumped storage operation have been dealt with in the previous papers using the concept of peak-shaving operation. In this paper, the hydro problem is solved by using a new version of the gradient projection method that treats the upper / lower bounds of variables saparately and uses a specified initial active constraint set. Accuracy and validity of the algorithm are demonstrated by comparing the result with that of the peak-shaving model.