• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

• 한국인터넷방송통신학회논문지
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• 제14권5호
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• pp.123-129
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• 2014

본 논문은 급격히 증가하는 전력 다소비환경(가정, 건물, 기업, 공장 등)에서 에너지 절감시스템 구현을 통해 낭비되는 전력수요를 효율적으로 관리하는 시스템을 제안한다. 무선 ICT(Information & Communication Technology)기반의 에너지관리시스템은 사용자가 언제 어디서든 인터넷 접속만으로 Web Browser 및 안드로이드 기반 앱을 통해 Smart Power Outlet(콘센트형)을 원격관제 하며, 실시간 전력 사용량 모니터링 및 적산 데이터 분석으로 전력 에너지 수요관리를 효율적으로 수행한다. 기존 유무선 통신방식의 거리제약 및 설치 문제를 무선 Zigbee 통신 및 Mesh Network방식의 Smart Power Outlet를 통해 해결방안을 제시하고 게이트웨이는 Zigbee와 TCP/IP방식의 ESS(Energy Saving System)와 네트워크를 구성하여 효율적인 전력관리 시스템을 구현하였다.

• 경영과학
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• 제26권1호
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.550-552
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• 2001

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 utilization of their own generation equipments. Especially, the electricity demand shows seasonal and weekly regular pattern, which the some capacity should be provided into ancillary service based on the past demand forecasting error and operating results of electricity market. Namely, if generation cost function is applied to SMP and BLMP as announced the previous day, the available generation capacity of the following day could be optimally distributed, and therefore contract capacity of ancillary service applied to CBP(Cost Based Pool) and TWBP(Two-Way Bidding Pool) is determined. Consequently, it is Possible to use the retained equipments optimally. This paper represents on 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.

• 경영과학
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• 제30권1호
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• pp.139-148
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• 2013

A nuclear energy has been one of the most important sources to securely supply electricity in South Korea. Its weight in the national electricity supply has kept increasing since the first nuclear reactor was built in 1978. The country relies on the nuclear approximately 31.4% in 2012 and it is expected to increase to 48.5% in 2024 based on the long-term electricity supply plan announced by the Korean government. However, Fukushima disaster due to 9.0 magnitude earthquake followed by the tsunami has raised deep concerns on the security of the nuclear power plants. The policy makers of the country are much interested in analyzing the cost structure of the power supply in the case that the nuclear is diminished from the current supply portion. This research uses a stochastic model that aims to evaluate the long-term power supply plan and provides an extensive cost analysis on the changes of the nuclear power supply. To evaluate a power supply plan, the research develops a few plausible energy mix scenarios by changing the installed capacities of energy sources from the long-term electricity supply plan. The analyses show that the nuclear is still the most attractive energy source since its fuel cost is very much stable compared to the other sources. Also the results demonstrate that a large amount of financial expenditure is additionally required every year if Koreans agree on the reduction of nuclear to increase national security against a nuclear disaster.

• Journal of Electrical Engineering and Technology
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• 제2권1호
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• pp.42-49
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• 2007

For electricity markets to function in a truly competitive and efficient manner, it is not enough to focus solely on improving the efficiencies of power supply. To recognize price-responsive load as a reliability resource, the customer must be provided with price signals and an instrument to respond to these signals, preferably automatically. This paper attempts to develop the Windows-based load management system in competitive electricity markets, allowing the user to monitor the current energy consumption or billing information, to analyze the historical data, and to implement the consumption strategy for cost savings with nine possible scenarios adopted. Finally, this modeling framework will serve as a template containing the basic concepts that any load management system should address.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
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• pp.94-96
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• 2004

This paper discusses the development of the educational simulator for the electricity spot market in korea. The interaction between lectures and users can be much enhanced via the web-based programs which result in the student's teaming effectiveness on an electricity spot market. However the difficulties for developing web-based application programs are that there can be the numerous unspecified users to access the application programs. To overcome the aforementioned multi-users problem and to develope the educational simulator, we have revised the system architecture, the modeling of application programs, and database which efficiently and effectively manages the complex data sets related to an electricity spot market. The developed application program is composed of the physical three tiers where the middle tier is logically divided into two kinds of application programs. The divided application programs are interconnected by using the Web-service based on XML (Extended Markup Technology) and HTTP (Hyper Text Transfer Protocol) which make it possible the distributed computing technology.

• 설비공학논문집
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• 제20권5호
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• pp.321-326
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• 2008

This paper presents and analyzes the effects of on-grid electricity cost, fuel price and initial capital cost of a CHP system, on the optimum DG and AC capacity and NPV, by using the ORNL CHP Capacity Optimizer, which was applied to a library in a university. By considering the current domestic energy cost and initial capital cost, it is shown that the installation and operation of the CHP system is not economical. However, with the current domestic CHP installation cost and fuel price, the NPV achieved by the installation of CHP system is greater when the on-grid electricity price is a factor of ${\times}1.5$ the present value. Regarding the initial capital cost of the CHP system, the reduction of the DG cost is much more economical than that of the AC cost, with respect to NPV. Electricity cost and fuel price have opposite effects on NPV, and NPV is more sensitive to an increase of the electricity cost than an increase of the fuel price.

• 한국인터넷방송통신학회논문지
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• 제21권5호
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• pp.215-219
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• 2021

본 논문에서는 스마트 빌딩 시스템과 전력망이 각각의 전력거래 희망가격을 제안하고 조정하는 양방향 전력거래 협상 기법에 심층 강화학습 기법을 적용한 전력거래 기법을 제안한다. 심층 강화학습 기법 중 하나인 deep Q network 알고리즘을 적용하여 스마트 빌딩과 전력망의 거래 희망가격을 조정하도록 하였다. 제안하는 심층 강화학습 기반 양방향 전력거래 협상 알고리즘은 학습과정에서 평균 43.78회의 협상을 통해 가격 협의에 이르는 것을 실험을 통해 확인하였다. 또한, 본 연구에서 설정한 협상 시나리오에 따라 스마트 빌딩과 전력망이 거래 희망가격을 조정하는 과정을 실험을 통해 확인하였다.