• Journal of Communications and Networks
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• v.14 no.6
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• pp.662-671
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• 2012

Plug-in hybrid electric vehicles (PHEVs) will be widely used in future transportation systems to reduce oil fuel consumption. Therefore, the electrical energy demand will be increased due to the charging of a large number of vehicles. Without intelligent control strategies, the charging process can easily overload the electricity grid at peak hours. In this paper, we consider a smart charging and discharging process for multiple PHEVs in a building's garage to optimize the energy consumption profile of the building. We formulate a centralized optimization problem in which the building controller or planner aims to minimize the square Euclidean distance between the instantaneous energy demand and the average demand of the building by controlling the charging and discharging schedules of PHEVs (or 'users'). The PHEVs' batteries will be charged during low-demand periods and discharged during high-demand periods in order to reduce the peak load of the building. In a decentralized system, we design an energy cost-sharing model and apply a non-cooperative approach to formulate an energy charging and discharging scheduling game, in which the players are the users, their strategies are the battery charging and discharging schedules, and the utility function of each user is defined as the negative total energy payment to the building. Based on the game theory setup, we also propose a distributed algorithm in which each PHEV independently selects its best strategy to maximize the utility function. The PHEVs update the building planner with their energy charging and discharging schedules. We also show that the PHEV owners will have an incentive to participate in the energy charging and discharging game. Simulation results verify that the proposed distributed algorithm will minimize the peak load and the total energy cost simultaneously.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1091-1096
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• 2011

Currently, the concern about the environment is the issue all over the world, and in particular, carbon emissions of the power plants will not be able to disregard from the respect of generation cost. This paper proposes DR (demand response) as a method of reducing carbon emissions and therefore, carbon emissions cost. There are a number of studies considering DR, and in this paper, the effect of DR is focused on the side of carbon emission reduction effect considering Time-Of-Use (TOU) program, which is one of the most important economic methods in DSM. Demand-price elasticity matrix is used in this paper to model and analyze DR effect. Carbon emissions is calculated by using the carbon emission coefficient provided by IPCC (Intergovernmental Panel on Climate Change), and generator's input-output characteristic coefficients are also used to estimate carbon emission cost as well as the amount of carbon emissions. Case study is conducted on the RBTS IEEE with six buses. For the TOU program, it is assumed that parameters of time period partition consist of three time periods (peak, flat, off-peak time period).

• Proceedings of the KIEE Conference
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• summer
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• pp.663-665
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• 2004

There are some demand response program which is Direct Load Control and so on in Korea. These are used to manage lack of power stability or shift peak time for shading load. It is very important not only using stability power system but controling and scheduling power system on the whole. Interruptible loads are essential resources to solve lack of energy and limit of constructing generator On recently days, Demand Response Program's reliability is recognized as ancillary or reserve service in many country. This paper presents a necessity to apply demand resource to our ancillary program. For this reason, it is introduce overseas ancillary program using load resource.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1608-1614
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• 2014

Power demand forecasting is an important factor of the peak management. This paper deals with the 15 minutes ahead load forecasting problem in a DC urban railway system. Since supplied power lines to trains are connected with parallel, the load characteristics are too complex and highly non-linear. The main idea of the proposed method for the 15 minutes ahead prediction is to use the daily load similarity accounting for the load nonlinearity. An Euclidean norm with weighted factors including loads of the neighbor substation is used for the similar load selection. The prediction value is determinated by the sum of the similar load and the correction value. The correction has applied the neural network model. The feasibility of the proposed method is exemplified through some simulations applied to the actual load data of Incheon subway system.

• Journal of Advanced Marine Engineering and Technology
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• v.15 no.5
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• pp.38-46
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• 1991

In the design of an electric power plant, the capacity to meet the peak load demand is one of the important factors to be considered. This peak load usually occurs when the most of the cooling air-conditioning systems are being operated during daytime in summer season. Therefore, it is necessary to construct an additional electric power plant and to develop the new air-conditioning system for relieving the peak load. This paper analysed the performance characteristics of this experimental regenerative ice energy system by means of a bundle of the heat-pipes. And the result of this analysis was applied to the simulation of an air-conditioning system model. Also, an operation program of moisture air was made according to air load and in order to computerize the air-conditioning system a CAD program was developed by the properties of moisture air.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2202-2205
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• 2011

Demand side management is used to maintain the reliability of power systems and to increase the economic benefits by avoiding power plant construction. This paper presents a systematic method to calculate the quantity of seasonal demand side management using time series. A numerical example is presented to calculate the quantity of demand side management in winter season using time series.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.455-461
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• 2009

This paper proposes Direct Load Control(DLC) operation scheme using a bidding system and the methodology to value proper quantity decided by the DLC program, which is a kind of resources for stabilization of electricity market price during peak times by managing consumer electricity demand. Since DLC program in Korea is based on the contract with the customers participating in this program, it is difficult to anticipate voluntary participation. That is, incentive for participants in DLC program is insufficient. To cope with this point, it is necessary to develop a new market mechanism and market compatible operation scheme for DLC programs. DLC market mechanism is deemed to be equipped with iterative bidding system, independent operation from energy market, and interactive with bidding information on energy market. With this market mechanism, it is important to find the optimal operation point of DLC allowing for the factors of stabilizing the electricity market price and compensating DLC implementation. This paper focuses on the mathematical approaches for the bid-based DLC operation scheme and examines several scenarios for the following technical justifications: 1) stabilization of electricity market price during peak times, 2) elasticity of demand.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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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.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.856-857
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• 2007

For the sake of a stable power supply, an electric power company must have power generation facilities that can generate more electricity than the maximum demand of the year. Due to the fact that the maximum electricity demand will also continue to increase, enormous investments are needed annually to build power plants. For that reason, electric power companies are propelling 'Demand Side Management' which improve the form of electrical usage for the customer in a positive way. This paper presents the concept of 'Cool Storage System' which is the most representative program, which lowers the peak demand during the on-peak time periods in a day and creates a base load simultaneously during the night time hours among the DSM programs.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1125-1131
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• 2014

Application of residential demand response (DR) programs are currently realized up to a limited extent due to customers' difficulty in manually responding to the time-differentiated prices. As a solution, this paper proposes an automatic home load management (HLM) framework to achieve the household minimum payment as well as meet the operational constraints to provide customer's comfort. The projected HLM method controls on/off statuses of responsive appliances and the charging/discharging periods of plug-in hybrid electric vehicle (PHEV) and battery storage at home. This paper also studies the impacts of different time-varying tariffs, i.e., time of use (TOU), real time pricing (RTP), and inclining block rate (IBR), on the home load management (HLM). The study is effectuated in a smart home with electrical appliances, a PHEV, and a storage system. The simulation results are presented to demonstrate the effectiveness of the proposed HLM program. Peak of household load demand along with the customer payment costs are reported as the consequence of applying different pricings models in HLM.