• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.16-22
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• 2017

In order to reduce costs of electricity energy at periods of peak demand, there has been an exponential interest in Demand Response (DR). This paper discusses the effect on the participants' behavior in response to DR. Under the assumption of perfect competition, the equilibrium point of the electricity market with DR is derived by modeling a DR curve, which is suitable for microeconomic analysis. Cournot model is used to analyze the electricity market of imperfect competition that includes strategic behavior of the generation companies. Strategic behavior with DR makes it harder to compute equilibrium point due to the non-differential function of payoff distribution. This paper presents a solution method for achieving the equilibrium point using the best response function of the strategic players. The effect of DR on the electricity market is illustrated using a test system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2221-2224
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• 2011

During an emergency due to a shortage of power, a load aggregator (LA) can use the demand response operation system to deploy demand response resources (DRRs) through various demand response (DR) programs. This paper introduces the demand response operation system for a load aggregator to manage various demand response resources in a smart grid environment.

• Current Photovoltaic Research
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• v.5 no.3
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• pp.100-104
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• 2017

This paper analyzes the correlation between Net Benefit Test (NBT) and System marginal price (SMP), which has a significant impact on the allocation of demand response (DR) resources in resource scheduling and commitment (RSC) process, based on the performance data of the demand resource market which has been established in 2014. Demand resources compete with generation resources in the RSC process, and it is prescribed to use demand resources only when net benefit occurs. Analysis result shows that the larger the SMP than the Net Benefit Threshold Price (NBTP), the more the winning bid of demand response resource was. It is interpreted that the introduction of NBT in DR market is justified. The demand resource market has been steadily growing. It is required to expand the scope of resources up to the small-sized DR, and to expand the functionalities of demand resources not only in the current energy market but also in the reserve market in the future. In order for that, institutional improvements are required.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.23-26
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• 2017

Social Welfare is useful concept for evaluating the effectiveness of an economic policy in micro economics. This paper focuses on Social Welfare(SW) of electricity market incorporating demand response(DR). Competition between DR and generation company is modeled as a simple bid function. DR function can be considered as an negative generation(called Negawatt) and as an element of modified demand function. These two approaches result in the same demand reduction, generation power, and the market price. However, SW in the modified demand function approach is not identical to SW in the Negawatt approach. It makes the numerical index of DR effectiveness less persuasive. This paper proposes modified definition of SW in the demand function approach. The proposed definition of SW leads the DR effectiveness index to be identical to that in the Negawatt approach.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1181-1186
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• 2017

The purpose of Demand Response is to reduce the cost of excessive resources and equipment by spontaneous load reductions at peak loads. Having enough power consumers participating in these schemes is key to achieving the goal. Demand Response Aggregator (DRA) is responsible for recruiting demand resources and managing them to participate in reducing the load. DRAs change the price elasticity of demand functions by providing incentives to demand response, thereby affecting price formation in the electricity market. In this paper, this process is modeled to analyze the relationship between DRA's strategic bidding and market outcomes and load reductions. It analyzes the results by applying to competition between DRAs, competition between DR and Gencos, and coexistence of DR load and non-DR load. It is noteworthy that we have found a phenomenon called the Balloon Effect.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2157-2165
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• 2017

This study deals with the design of the mechanism in which demand response (DR) resources are traded in the power generation market. In general, a DR aggregator (DRA), which extends DR resources and provides technical support, is central to this mechanism. In this study, power users, called DR customer (DRC), participate in load reduction and are also modeled to participate directly in DR-related bidding. The DRA provides incentives to the DRC, indirectly impacting the market, and the DRC use the bid parameters strategically. We present the conditions for finding Nash Equilibrium (NE) in game problems of various participants including market operators, and analyze the characteristics of DRA and DRC related models. It also analyzes the impact of the participants on the market according to various types of competition and coalitions between DRA and DRC.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.789-795
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• 2014

In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.537_538
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• 2009

본 논문의 목적은 전력 소비자 입장에서 비용 최소화를 하는 DR(Demand Response)프로그램의 설계이다. 이는 DR자원을 입찰받는 환경에서 시장의 가격을 고려하여 DR자원의 투입량과 그 보상금을 결정하는 것에 초점을 맞추고 있다. 또한 수요 감축이 이루어진 후에 발생하는 수요증가현상(Payback)현상을 고려하였다. payback 현상을 고려하지 않고 DR프로그램을 설계할 경우 당장의 피크를 줄이기 위해 많은 DR자원 투입하는 경우가 있다. 이런 경우 당장의 피크는 없어지지만 추후의 증가하는 수요에 의해 제2의 피크가 발생하거나 비용이 증가하는 현상이 생긴다. 따라서 Payback은 DR프로그램을 실행하는데 있어서 반드시 고려되어야할 중요한 현상이다. 본 논문에서는 몇 가지 payback 모델을 바탕으로 소비자 입장에서 비용 최소화하는 DR프로그램을 구현하였다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2354-2358
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• 2009

Demand response (DR) can be used to improve the efficiency of electricity markets and increase the reliability of power systems. As more utilities attempt to reduce the purchasing costs by implementing DR programs strategically, there is an increasing need for studies of how to allocate the reduced purchasing costs among DR program participants. The rebates or incentives can be given to DR program participants in proportion to the participants' contributions to the reduced purchasing costs. This paper presents Shapley Value-based method to determine the DR program participants' contributions to the reduced purchasing costs. A numerical example is presented to validate the effectiveness of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2220-2226
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• 2018

Electric vehicles (EVs) are significant resources for demand response (DR). Thus, it is essential for EV aggregators to quantitatively evaluate their capability for DR. In this paper, a concept of dynamic equivalent battery (DEB) is proposed as a metric for evaluating the DR performance using EVs. The DEB is the available virtual battery for DR. The capacity of DEB is determined from stochastic calculation while satisfying the charging requirements of each EV, and it varies also with time. Further, a new indicator based on the DEB and time-varying electricity prices, named as value of DEB (VoDEB), is introduced to quantify the value of DEB coupled with the electricity prices. The effectiveness of the DEB and the VoDEB as metrics for the DR performance of EVs is verified with the simulations, where the difference of charging cost reduction between direct charging and optimized bidding methods is used to express the DR performance. The simulation results show that the proposed metrics accord well with the DR performance of an EV fleet. Thus, an EV aggregator may utilize the proposed concepts of DEB and VoDEB for designing an incentive scheme to EV users, who participate in a DR program.