• 대한임베디드공학회논문지
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• 제9권3호
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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.

• 전기학회논문지
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• 제63권1호
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• pp.191-196
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• 2014

For the large scale of electricity consumer, since the price for the electricity consumption depends on the peak power, the issue for peak power reduction have been being studied widely. Electric railway systems, which is one of the most representative large scale of loads, also has assignment to reduce the peak power since they have high peak power and low energy consumption load characteristics. In the aspect of the economic operation through reduction of peak power, this paper proposes a novel algorithm for power management system in electric railway systems using energy storage.

• 전기학회논문지
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• 제64권6호
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• 전기학회논문지
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• 제65권7호
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• pp.1128-1134
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• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 하계학술대회 논문집
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• pp.96-99
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• 1990

Recently, electric energy consumption pattern shows very high peak load with low load factor. This Load pattern have made electric utilities be interested in Load Management, many studies are reported. But most of these studies are concerned with Rate - Load shape relation, a few of these are concerned with generating cost reduction. In this study, the effect of Load Management is incorporated to establish optimal Generation Expansion Planning. Using avoided cost, optimal generation expansion planning is achieved to make maximum avoided cost of Electric utility. Dynamic programming technique is used to solve this algorithm.

• 응용통계연구
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• 제27권5호
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• pp.843-853
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• 2014

급증하고 있는 전력수요에 대한 신뢰성 있는 예측은 합리적인 전력수급계획 수립 및 운용에 있어서 매우 중대한 사안이다. 본 논문에서는 여러 시계열 모형의 비교를 통해 전력수요량과 밀접한 연관성이 있는 온도를 어떠한 형태로 고려할 것인지, 또한 4계절이 뚜렷하여 계절별 기온 차가 많이 나는 우리나라의 특성을 어떻게 고려할 것인지에 대하여 연구하였다. 모형 간 예측력을 비교하기 위하여 Mean Absolute Percentage Error(MAPE)를 사용하였다. 모형의 성능비교 결과는 냉 난방지수와 계절요인을 동시에 고려하면서 큰 변동성을 잘 고려해줄 수 있는 Reg-AR GARCH 모형이 가장 우수한 예측력을 나타냈다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.929-931
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• 1998

This paper describes the peak load forecasting technique of distribution transformers with correlation equation. While customers are demanding safe energy supply, conventional correlation equation that is used for load management of distribution transformers in domestic has some problems. To get accurate correlation equation, se-correlation equation were examined using new collected using the measuring instrument dev for this study. It was recognized that the qua equation was the most accurate for peak forecasting from working electrical energy.

• 전기학회논문지
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• 제63권12호
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• pp.1627-1636
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• 2014

In real-time electricity price environment, the energy management system can provide the significant advantage to the residential, commercial and industrial customers since it can reduce the electricity charge by controlling the load operation effectively in response to time-varying price. However, the earlier studies for load management mainly focus on the residential and commercial customers except for the industrial customers because most of load operations in industrial sector are intimately related with production schedule. So, it is possible that the inappropriate control of loads in industrial sector causes huge economic loss. In this paper, therefore, we propose load control algorithm for factory energy management system(F-EMS) to achieve not only minimizing the electricity charges but also maintaining production efficiency by considering characteristics of load operation and production schedule. Considering characteristics of load operation and production schedule, the proposed load control algorithm can reflect the various characteristics of specific industrial customer and control their loads within the range that the production efficiency is maintained. Simulation results show that the proposed load control algorithm for F-EMS leads to significant reduction in the electricity charges and peak power in industrial sector.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.570-572
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• 2000

In this paper, we present the configuration of Direct Load Control System using TCP/Ethernet Module for the Load Management Europe and America has operated the DLC system by many communication system, and Taiwan, Japan has controlled the cooling load at peak hours of power system. Up to now, Many Researches for the DLC of communication method, so called, pager, power line carrier, ripple control, etc. has advanced. Now, we present new communication method for the DLC using TCP/Ethernet module which is widespread all over the world.

• ETRI Journal
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• 제37권1호
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• pp.197-202
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• 2015

A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day-ahead pricing strategy. Based on the Rubinstein-Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak-to-average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.