• 전기학회논문지
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• 제60권6호
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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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• 제9권3호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.