• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.123-129
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• 2014

This paper suggests that it provides a electricity management system for wasting electricity, from power demand growth environments. This Energy management system based on ICT(Information & Communication Technology) can control Smart Power Outlet connecting to this system with Web Browser and Android phone, anytime, anywhere. Through analysis of acquisition data from them, this proposed system can monitor and control power consumption efficiently. This system was organized mesh network of Smart Power Outlet, gateway by wireless Zigbee, and ESS(Energy Saving System) by TCP/IP beyond existing limit of communication distance and space.

• Korean Management Science Review
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• v.26 no.1
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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.

• Proceedings of the KIEE Conference
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• 2001.07a
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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.

• Korean Management Science Review
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• v.30 no.1
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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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• v.2 no.1
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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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• v.13 no.1
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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.

• Proceedings of the KIEE Conference
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• 2004.11b
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.5
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.