• 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).

• 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.