• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.23-49
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• 2008

In this paper, we estimate whether changes in the economic environment a firm operates in affects technical efficiency. Since efficiency gains can manifest themselves in a variety of ways, we estimate for efficiency gains on annual generating plants input demand for expenses and fuel use. We find that generating plants reduced their workers and costs respectively by 6.8% in 1999 and 37.9% in 2001. However, they didn't show any systematic caloric consumption changes facing various economic environmental shocks. These results suggest that restructuring brought efficiency gains by replacing regulated monopoly with a more competitive firms in the context of the Korean electricity market.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.205-232
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• 2022

This study analyzes the effect of the 2^nd pilot test of Tiime of Use (TOU) pricing for Korean households using a two-level electricity demand model. The test, implemented from May to September 2021, was conducted to compare the effects of two TOU pricing rates and the standard rates for households living in apartment and detached house in 7 provinces of Korea. Based on the data on electricity consumption during the test period and during the same period last year of the 1,292 participants and their socio-economic characteristics, this study analyzes (1) whether the relative demand across periods has changed in response to hourly price changes and (2) whether the price responsiveness of daily consumption has changed after the introduction of TOU pricing. The results show that both types of TOU pricing affect neither the relative demand across periods nor the price responsiveness of daily consumption. The reason behind the results could be related to the level of TOU pricing rates and the periodical classification, which were not sufficient to induce changes in the participants' electricity demand patterns.

• 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.

• Journal of Energy Engineering
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• v.11 no.1
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• pp.59-66
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• 2002

The vertically integrated power industry was divided into six generation companies and one market operator, where electricity trading was launched at power exchange. In this environment, the profits of each generation companies are guaranteed according to utilizing strategies of their own generation equipments. This paper presents on generator maintenance scheduling and efficient bidding strategies for generation equipments through the calculation of the contract and the application of each generator cost function based on the past demand forecasting error and market operating data.

• Journal of Energy Engineering
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• v.24 no.2
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• pp.9-16
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• 2015

As electricity is an indispensable input to human's existence and industrial production, economic benefits arise from consumption. The economic benefits of the electricity consumption are useful information in various fields of electricity-related policy. Therefore, this study attempts to value the economic benefits from electricity use. The economic benefit of electricity consumed is the area under the demand curve which made of the sum of the actual consumer expenditure and the consumer surplus. Consumer expenditure can be easily observed but the information on price elasticity of demand is necessarily required to compute consumer surplus. This study derives the estimates for price elasticities through literature review. The price elasticities of the electricity demand for residence, industry, and commercial are estimated to be -0.332, -0.351, and -0.263, respectively. Because the consumer surplus of the electricity consumption for residence, industry, and commercial are computed to be 191.54, 143.44, and 231.91 won per kWh, respectively. Given that average prices of electricity use were 127.02, 100.70, and 121.98 won per kWh for the year 2013, the economic benefit are calculated to be 318.56, 244.14, and 353.89 won per kWh, respectively. We can convert the values to 321.96, 246.75, and 357.67 won per kWh in 2014 constant price, respectively, using consumer price index. They can be used in the economic feasibility analysis of a new electricity supply project.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.85-92
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• 2018

Reductions of the electricity charge are achieved by demand management of the load. The demand management method of the load using ESS involves peak shifting, which shifts from a high demand time to low demand time. By shifting the load, the peak load can be lowered and the energy charge can be saved. Electricity charges consist of the energy charge and the basic charge per contracted capacity. The energy charge and peak load are minimized by Linear Programming (LP) and Quadratic Programming (QP), respectively. On the other hand, each optimization method has its advantages and disadvantages. First, the LP cannot separate the efficiency of the ESS. To solve these problems, the charge and discharge efficiency of the ESS was separated by Mixed Integer Linear Programming (MILP). Nevertheless, both methods have the disadvantages that they must assume the reduction ratio of peak load. Therefore, QP was used to solve this problem. The next step was to optimize the formula combination of QP and LP to minimize the electricity charge. On the other hand, these two methods have disadvantages in that the charge and discharge efficiency of the ESS cannot be separated. This paper proposes an optimization method according to the situation by analyzing quantitatively the advantages and disadvantages of each optimization method.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1749-1758
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• 1997

This paper suggests the development of dynamic forecasting model for short-term power demand based on Radial Basis Function Network and Pal's GLVQ algorithm. Radial Basis Function methods are often compared with the backpropagation training, feed-forward network, which is the most widely used neural network paradigm. The Radial Basis Function Network is a single hidden layer feed-forward neural network. Each node of the hidden layer has a parameter vector called center. This center is determined by clustering algorithm. Theatments of classical approached to clustering methods include theories by Hartigan(K-means algorithm), Kohonen(Self Organized Feature Maps %3A SOFM and Learning Vector Quantization %3A LVQ model), Carpenter and Grossberg(ART-2 model). In this model, the first approach organizes the load pattern into two clusters by Pal's GLVQ clustering algorithm. The reason of using GLVQ algorithm in this model is that GLVQ algorithm can classify the patterns better than other algorithms. And the second approach forecasts hourly load patterns by radial basis function network which has been constructed two hidden nodes. These nodes are determined from the cluster centers of the GLVQ in first step. This model was applied to forecast the hourly loads on Mar. $4^{th},\;Jun.\;4^{th},\;Jul.\;4^{th},\;Sep.\;4^{th},\;Nov.\;4^{th},$ 1995, after having trained the data for the days from Mar. $1^{th}\;to\;3^{th},\;from\;Jun.\;1^{th}\;to\;3^{th},\;from\;Jul.\;1^{th}\;to\;3^{th},\;from\;Sep.\;1^{th}\;to\;3^{th},\;and\;from\;Nov.\;1^{th}\;to\;3^{th},$ 1995, respectively. In the experiments, the average absolute errors of one-hour ahead forecasts on utility actual data are shown to be 1.3795%.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.475-478
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• 2003

본 논문에서는 쿠르노 모형을 적용한 양방향입찰 전력 풀시장에서 입찰에 참여하는 발전기가 최대 이익을 얻기 위한 입찰전략으로서 신경회로망의 오차 역전파 알고리즘을 이용하여 최적 입찰발전량과 입찰가격을 수립하는 기법에 관하여 연구한다. 전력시장 환경은 n 개의 발전기들이 참여하는 비협조적 불완전정보 시장으로 설정하고 Bayesian의 조건부 확률이론을 적용하여 상대 발전기들의 발전비용함수와 시장의 수요함수를 추정하여 발전기 상호간 쿠르노-내쉬균형점을 이루는 최적 입찰발전량을 예측한다. 그리고 이익을 극대화시키기 위해 오차 역전파 알고리즘을 이용하여 시장의 가격 탄력성과 쿠르노 시장균형가격에 연결가중치를 조절함으로써 입찰가격이 계통한계가격에 근접하도록 최적 입찰전략을 수립한다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.562-563
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• 2008

경쟁적 전력시장에서는 이익주체의 다양화로 송전망확장은 개별 시장참여자의 경제적 편익에 큰 영향을 미칠 수 있다. 송전설비 투자계획은 미래 전력 시장 및 계통의 예측을 바탕으로 하기 때문에 예측의 불확실성에서 발생하는 설비투자의 과잉.과소투자의 최소화 방안이 필요하다. 따라서 본 논문은 송전망확장사업의 경제적 가치를 평가하는 방법에 대해 연구였고 미래 시장 및 계통의 불확실성을 반영하기 위해 전력수요와 연료가격의 과거 예측오차의 표준편차를 이용한 예측값의 확률밀도함수의 모델링 방법을 이용한 송전망확장의 경제성 평가 방법을 제시한다. Monte Carlo Sampling을 이용, 송전망확장으로 인한 시장참여자의 경제적 편익 변화의 기대값과 편익 변화의 범위를 산출함으로써 설비투자의 리스크와 잠재효과에 대해 분석한다.

• Environmental and Resource Economics Review
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• v.21 no.1
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• pp.93-127
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• 2012

We estimate how much KEPCO can save their loss and how much social welfare can be increased by applying the real-time pricing instead of current regulated retail price in the electricity market in order to analyze the problem of the regulated retail price which is fixed below the marginal cost. We estimate the demand functions of peak time and off-peak time in summer (June to August) and winter (December to February). We construct the supply function based on hourly step-wise linear marginal cost functions, too. We find that the increase of social welfare will be 67 billion won in summer if the fixed retail price is changed into the real-time pricing scheme. The total 705 billion won will be transferred from consumer surplus to producer surplus and the rest (67 billion won) will be saved from the reduction of deadweight loss among KEPCO's loss. In winter, the increase of social surplus will be 225 billion won and 1,174 billion won of KEPCO's loss will be transferred from consumer surplus. As a result, we conclude that the regulation of the retail price in the electricity market induces the social welfare loss and KEPCO suffers a huge loss.