• 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.

• Journal of Energy Engineering
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• v.11 no.4
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• pp.317-323
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• 2002

This paper proposed the calculation method of the generation operating avoided cost to cost-effectiveness evaluation of energy conservation programs that compounded the Proxy Plant Method and Load Decrement Method. This method introduced an operating index of the Energy Efficiency Demand-Side Management (EEDSM) resources based on the end-user's behaviors on the electricity power usage. The operation index is applied to calculate the hourly operating capacity of diffused high-efficiency appliances. And the operating capacity on the peak load hours for reference load is computed through the reduction of the peak load that contributes to that hour. Also, the proposed method evaluated the effect of EEDSM resources. The IEEE-RTS is adopted as a sample system to analyze impacts of an EEDSM. This paper, we have analyzed the effect of EEDSM upon the changes in the generation of generator, generation cost and the system marginal price (SMP). This method can be used to evaluate the impact of the diffused DSM resource and to estimate the impact in short-term EEDSM program. Further, result of the calculation can be utilized to pabulum for effect analysis of EEDSM resources.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.977-983
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• 2013

Based on the consideration of the hourly patterns of the electricity power consumption, this study predicted the effectiveness of hybrid power generation system, which is composed with wind power generator and photovoltaic generator. And this case study is performed at Konrido, which is a affiliated island of Kyeongsangnam-do. As the results, it is obvious that it is not efficient to cover the whole electricity power consumption only with any single power generating system, because the hourly patterns of electricity power consumption, wind power generation and photovoltaic generation are quite different. And because the wind is being through almost 24 hours, it is also found out that wind power generating system with storage battery is the most efficient combination for this case study.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.1
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• pp.118-124
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• 2001

To guide the esti-on of electric d e d and energy saving, this proposed the Power Laaddensity, Elechic pwer consumption and the installed state of Power facility. For this purp~se, it wasconducted a questionnaire survey of the consumption-cattern of Electric power in 1W a p a r t t housings.As a 1-esult it is found that (i) the d m u m value of Power Load density is 7.70[~A/m"l, (ii) the avtragevalue of Power Load dens'||'&'||' is ~ . ~ A / mm' dl (iiijthe average load rate is Ed[%]. Also, the consumptionof electricity one year at tlie whole a m n t s of couniry is 14,0X[GWyearl, it was equivalent to 7[%1 ofthe total-consunmhon of electricity one year at the whole of co1mtnr. co1mtnr.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.81-91
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• 2012

The world has been replacing fast fossil fuels vehicles with electric vehicles(EVs) to cope with climate change. The government set a goal which EVs will be substitute at least 10% of the domestic small vehicles with EVs until 2020, and will try to build electric charging infrastructures in apartments with the revision the law of 'the housing construction standards'. In apartments the EVs charging infrastructure and parking space is, essential to accomplish the goal. But the studies on EVs demand are few. In this study, we predicted that the demand for EVs using time-series analysis of statistical data, survey results for apartments residents in the metropolitan area. As a result, the ratio of the EVs appeared to be 6~21% for the total vehicles in a rental apartments for the years 2020, 21~39% in apartments for sales. For the EVs, the maximum power required for 1,000 households in rental apartment is predicted to be about 4200 kwh on a daily basis, while the maximum power in the apartment for sales is predicted to be 7800kwh.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.137-143
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• 2014

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.1
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• pp.29-39
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• 1992

In this paper, the basic idea of optimum electric system design by means of the forecasting of maximum demand is presented, and the load characteristics and practical operating conditions are based on the technical data. After reconstruction of th model plant by use of above method, power supply reliability, future extention, initial cost, and running cost saving effects are analyzed. As a result, it is verified that the systems wherein the power is supply to each load frm main transformer whose capacity is calculated by forecasting are economic rather than the systems wherein the power is supply to each electric feeders from each corresponding transformer.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.109-117
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• 2002

High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

• 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.