• Journal of Energy Engineering
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• v.27 no.3
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• pp.10-20
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• 2018

Due to cheap electricity tariff on agricultural use, electricity consumption in agricultural sector has grown dramatically. We evaluated the negative effects of the cheap electricity tariff such as electricity over-consumption, increased dependency on electricity, decreased electricity productivity, and unequal distribution of the benefit. We also estimated the effects of agricultral output growth, structural change, and electricity intensity change on sharp electricity consumption increase in agricultural sector between 1998 and 2016 using the Log Mean Divisia Index decomposition method. The findings reinforce the necessity of raising the electricity tariff for agricultural use.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.381-398
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• 2011

Recently, electricity peaks have frequently occurred in winter, and the concerns about failure in power supply are soaring. One of reasons is due to the increase in industrial demand with economic recovery, and the increases in household and commercial demand with unusual cold waves are spotted as a primary cause. Especially, the diffusion of electricity heat pumps (EHP) has led to the rapid increase in commercial electricity demand. The EHP diffusion is mainly associated with low electricity tariff and cheaper heating and cooling costs compared to those of gas-engine heat pumps (GHP). The problem is that distorted electricity tariff and herd behaviour toward heating and cooling devices could bring about nation-wide inefficiencies in resource allocation. The key countermeasures are as follows. The electricity tariff should be readjusted to a realistic level. The tariff scheme should be so modified that consumers rationally respond to the tariff. In addition, one should find ways to effectively manage electricity demand.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.4
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• pp.11-27
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• 2002

This paper is aimed at surveying the method that supports logical and theoretical back grounds of electricity service pricing, to investigate whether the ESPM can reflect comprehensively the various interests of parties and persons concerned with electricity supply and demand, and analyzing the practical applicability of the model in short-term perspectives. The major findings of this study can be summarized as fellows. First, the ESPM explains what process the equilibrium price is attained through, which is the essential concept and object in evaluating the value of public enterprises or utilities and the price of electricity Second, the ESPM provides the logics and methods that can objectify the discrete price by each electricity user. Third, the ESPM presents theoretical logics and practical methods that can calculate the basic price and the variable price per electricity unit which are key concepts in the two-part tariff. Fourth, the ESPM has powerful practical applicabilities in the reasonable electricity pricing and in the explanation for the balance between parties and persons interested with electricity supply and demand.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1304-1312
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• 2009

The purpose of this study is to build promotive measures and to develop alternative policies of DG(Distributed Generation) by finding and analysing effects of four business environment factors related to DEPB(District Electricity Power Business) on boosting DG. In this study, four business environment factors, which are the electric power industry restructuring, electricity tariff and pricing structure, regulations for DEPB, and conflicts of stake-holding groups, are considered as independent variables. And promotion factors of DG including small CHP(Combined Heat and Power) generation, which is outcome of DEPB, are considered as dependent variables. But dependent variables including booming of new renewable energy generation due to green energy pricing incentives, the electric power industry restructuring, and electricity tariff and pricing policies were separatively considered. In this study, some policies were proposed reflecting research results of empirical demonstrative analysis, previous studies, overseas cases, etc.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1278-1285
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• 2018

For saving electricity bill, energy storage system(ESS) is being installed in factories, public building and commercial building with a Time-of-Use(TOU) tariff which consists of demand charge(KRW/kW) and energy charge(KRW/kWh). However, both of peak reduction and ESS special tariff are not considered in an analysis of initial cost payback period(ICPP) on ESS. Since it is difficult to reflect base rate by an amount of uncertain peak demand reduction during mid-peak and on-peak periods in the future days. Therefore, the ICPP on ESS can be increased. Based on this background, this paper presents the advanced analysis method for the ICPP on ESS. In the proposed algorithm, the representative days of monthly electricity consumption pattern for the amount of peak reduction can be found by the k­means clustering algorithm. Moreover, the total expected energy costs of representative days are minimized by optimal daily ESS operation considering both peak reduction and the special tariff through a mixed-integer linear programming(MILP). And then, the amount of peak reduction becomes a value that the sum of the expected energy costs for 12 months is maximum. The annual benefit cost is decided by the amount of annual peak reduction. Two simulation cases are considered in this study, which one only considers the special tariff and another considers both of the special tariff and amount of peak reduction. The ICPP in the proposed method is shortened by 18 months compared to the conventional method.

• 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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• 1999.07b
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• pp.800-802
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• 1999

The meters for low voltage customer presently being used in Korea are all mechanical type and there is strict limitation on applying various customized tariff structure. Recently low voltage solid state meter is under development by several makers of the watt hour meter in Korea. Also, KEPCO has a plan to use the solid state meter for low voltage customer to reduce peak power. So, this paper suggests the needed function of low voltage solid state meter considering electricity tariff policy.

• Korean System Dynamics Review
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• v.7 no.2
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• pp.35-56
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• 2006

Korea is implementing strong regulatory derives such as Feed in Tariff to provide incentives for renewable energy developers. But if the government is planning to increase the renewable capacity with only "Price policy" not considering the investors behavior in the competitive electricity market, the policy would be failed. It is necessary system thinking and simulation model analysis to decide government's incentive goal. This study is focusing on the assesment of the competitiveness of renewable energy with the current Feed in Tariff incentives compared to the traditional energy source, specially coal and gas. The simulation results show that the market penetration of renewable energy with the current Feed-in-Tariff level is about 60-70% of the government goal under condition that the solar energy and fuel cell are assumed to provide the whole capacity set in the governmental goal. If the contribution from solar and fuel cell is lower than planned, the total penetration of renewable energy will be dropped more. Notably, Wind power turned out to be proved only 10% of government goal because of its low availability.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.225-238
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• 2022

In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.

• 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.