• 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.

• Environmental and Resource Economics Review
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• v.23 no.4
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• pp.617-641
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• 2014

Korean electricity market is a Cost-Based Pool (CBP) designed to minimize electricity production cost through cost by providing cost reduction incentives to generators. Generation companies have shown diverse efforts to reduce costs in CBP market such as procuring low-price fuels, installing high efficiency gas turbine and constructing power plants near the heavy-load site. Recently, as a way to improve CBP market, a proposal to exclude no-load cost from System Marginal Price (SMP) and to compensate generators ex post was suggested to Korea Power Exchange. This study analyzes the impact of excluding no-load cost from SMP on the cost reduction incentive of generators. We found that excluding no-load cost from SMP enhances the likelihood of decreasing the cost reduction incentives of LNG combined-cycle generators lying on the price-setting range.

• Environmental and Resource Economics Review
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• v.31 no.3
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• pp.393-417
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• 2022

Using hourly SMP data from 2016 to 2020, this paper measures the weekly realized volatility and investigates the main force of its determinants. To this end, we extend the Bayesian variable selection by incorporating the regime-switching model which identifies important variables among a large number of predictors by regimes. We find that the increase in coal and nuclear generation, as well as solar power, reinforce the SMP volatility in both high volatility and low volatility regime. In contrast the increase in gas generation and gas price decrease SMP volatility when SMP volatility is high. These results suggest that the expansion of renewable energy according to 2050 Carbon Neutrality or energy transition policies increases SMP volatility but the increase in the gas generation or reduction of coal generation might offset its impact.

• 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

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.76.1-76.1
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• 2011

With the growing penetration of distributed generation including from renewable sources, smart grid power system is needed to address the reliability problem. One important feature of smart grid is demand response. In order to design a demand response program, it is indispensable to understand how consumer reacts upon the change of electricity price. In this paper, we construct an econometrics model to estimate the hourly price elasticity of demand. This panel model utilizes the hourly load data obtained from KEPCO for the period from year 2005 to 2009. The hourly price elasticity of demand is found to be statistically significant for all the sample under investigation. The samples used for this analysis is from the past historical data under the price structure of three different time zones for each season. The result of the analysis of this time of use pricing structure would allow the policy maker design an appropriate incentive program. This study is important in the sense that it provides a basic research information for designing future demand response programs.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.3
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• pp.391-395
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• 2016

In the last few decades, global electricity consumption has dramatically increased and has become drastically fluctuating and uncertain causing blackout. Due to the unexpected peak electricity demand, we need significant electricity supply. The solutions to these problems are smart grid system which is envisioned as future power system. Smart grid system can reduce electricity peak demand and induce effective electricity consumption through various price policies, demand response (DR) control methodologies, and state-of-the-art smart equipments in order to optimize electricity resource usage in an intelligent fashion. Demand response (DR) is one of the key technologies to enable smart grid. In this paper, we propose greedy technique for demand response smart grid system. The proposed scheme focuses on minimizing electricity bills, preventing system blackout and sacrificing user convenience.

• Transactions of the Korean hydrogen and new energy society
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• v.22 no.1
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• pp.118-127
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• 2011

An IGCC was evaluated as one of the next generation technologies that would be able to substitute for coal-fired power plants. According to "The 4th Basic Plan of Long-term Electricity Supply & Demand" which is developed by the Electricity Business Acts, the first IGCC will be operated at 2015. Like other new and renewable energy such as solar PV, Fuel cell, The IGCC is considered as non-competitive generation technology because it is not maturity technology. Before the commercial operation of an IGCC in our electricity market, its economic feasibility under the Korean electricity market, which is cost-based trading system, is studied to find out institutional support system. The results of feasibility summarized that under the current electricity trading system, if the IGCC is considered like a conventional plant such as nuclear or coal-fired power plants, it will not be expected that its investment will be recouped within life-time. The reason is that the availability of an IGCC will plummet since 2016 when several nuclear and coal-fired power plants will be constructed additionally. To ensure the reasonable return on investment (NPV>0 IRR>Discount rate), the availability of IGCC should be higher than 77%. To do so, the current electricity trading system is amended that the IGCC generator must be considered as renewable generators to set up Price Setting Schedule and it should be considered as pick load generators, not Genco's coal fired-generators, in the Settlement Payment.

• Advances in Energy Research
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• v.5 no.2
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• pp.129-145
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• 2017

In this study, the use of a micro gas turbine system using biogas to supply heating, cooling and electricity loads of a rural building located in rural area around Tehran has been studied. Initially, the amount of energy needed by the farmhouse was calculated and then the number of needed microturbines was determined. Accordingly, the amount of substances entering biogas digester as well as tank volume were determined. The results of this study showed that village house loads including electrical, heating and cooling and hot water loads can be supplied by using a microturbine with a nominal power of 30 kW and $33.5m^3/day$ of biogas. Digester tank and reservoir tank volumes are $67m^3$ and $31.2m^3$, respectively. The cost of electricity produced by this system is 0.446 US$/kWh. For rural area in Iran, this system is not compatible with micro gas turbine and IC engine system use urban natural gas due to low price of natural gas in Iran, but it can be compatible by wind turbine, photovoltaic and hybrid system (wind turbine& photovoltaic) systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.97-103
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• 2004

CHP system supplies electricity and heating together with high efficiency. Current utility's CHP system uses electric power by itself and sells thermal energy to KDHC(Korea District Heating Corporation). CHP's operation cost except sales revenue of heating was covered by the sale revenue of electricity. Thus Electric generation cost in district Heating CHP system has close relationship with the level of heating price. However, after the restructuring of electricity industry, the operation cost could not be covered by sales revenue of heating and electricity. This loss was compensated by energy subsidy program in the electric power industry infrastructure fund. This paper suggests reasonable evaluation and improvement methods of the loss calculation of CHP system utilizing the infrastructure fund efficiency In terms of the direction of support by the fund, it provides the methods to prevent inefficient operation through setting up the upper limit of subsidy and to improve the loss calculation. Moreover, it suggest fixed rate support by heating supply level and reducing subsidy gradually for an efficient operation of CHP system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.222-227
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• 2015

This paper presents the optimal energy generation systems for economical EVs(Electric Vehicles) charging stations located in an island area. The system includes grid electricity, diesel generator and renewable energy sources of wind turbines and PV(Photovoltaic) panels. The independent generation system is designed with data resources such as annual average wind speed, solar radiation and the grid electricity price by calculating system cost under different structures. This sensitive analysis on the varying data resources allows for the configuration of the most economical generation system for charging stations by comparing initial capital, operating cost, NPC(Net Present Cost) and COE(Cost of Energy). Depending on the increase of the grid cost, the NPC variation of the most economical system which includes renewable energy generations and grid electricity can be smaller than those of other generation systems.