Demand Response Program Implementation Practices In Korea

Kim, Jin-Ho; Nam, Young‐Woo; Hahn, Tae-Kyung; Jun-hee, Hong.

doi:10.3182/20110828-6-it-1002.03774

Cited by 8 publications

(11 citation statements)

References 2 publications

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“…The LAs calculate the actual load reduction as the difference between the measured load consumption and the Customer Base Line (CBL). The CBL is calculated every day based on the past 10-day power consumption data by using weighted averaging windows [5]. As shown in Figure 6, the customers can be divided into two groups such as automatic DR (ADR) loads and semi-ADR loads.…”

Section: Emergency Demand Response Programmentioning

confidence: 99%

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Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

et al. 2013

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Microgrids can be considered as controllable units from the utility point of view because the entities of microgrids such as distributed energy resources and controllable loads can effectively control the amount of power consumption or generation. Therefore, microgrids can make various contracts with utility companies such as demand response program or ancillary services. Another advantage of microgrids is to integrate renewable energy resources to low-voltage distribution networks. Battery energy storage systems (BESSs) can effectively compensate the intermittent output of renewable energy resources. This paper presents intelligent control schemes for BESSs and autonomous energy management schemes of microgrids based on the concept of multi-agent systems. The proposed control scheme consists of two layers of decision-making procedures. In the bottom layer, intelligent agents decide the optimal operation strategies of individual microgrid entities such as BESSs, backup generators and loads. In the upper layer, the central microgrid coordinator (MGCC) coordinates multiple agents so that the overall microgrid can match the load reduction requested by the grid operator. The proposed control scheme is applied to Korea Power Exchange's Intelligent Demand Response Program.

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Section: Emergency Demand Response Programmentioning

confidence: 99%

“…Therefore, this paper presents intelligent DR schemes of microgrids using intelligent multiple agents. In this paper, the proposed method is applied to the Intelligent DR program of the Korea Power Exchange (KPX) that is a type of emergency demand response (EDR) program [5,6]. This EDR program of KPX is elaborated in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

et al. 2013

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“…The participants will reduce energy consumption to avoid expensive electric bills. Figure 5 illustrates the configuration of the EDR programs developed by Korea Power Exchange (KPX) that consists of two markets [12]. One is Direct DR market for large-size loads and the other is Intelligent DR market for medium and small-size loads.…”

Section: Emergency Demand Responsementioning

confidence: 99%

“…Demand response (DR) programs can be defined as the changes in electricity consumption of loads during the peak loading period [12][13][14][15]. DR programs are divided into two groups such as incentive-based and price-based programs.…”

Section: Emergency Demand Responsementioning

confidence: 99%

“…The LAs calculate the actual load reduction by calculating the difference between the measured load consumption and the Customer Base Line (CBL). The CBL is calculated every day based on the past 10-day power consumption data by using weighted averaging windows [12][13]. The customers can be divided into two groups such as automatic DR (ADR) loads and semi-ADR loads according to the communication channel.…”

Section: Emergency Demand Responsementioning

confidence: 99%

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Hardware-in-the-Loop Simulation of Distributed Intelligent Energy Management System for Microgrids

Yoo

Chung

et al. 2013

Energies

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Microgrids are autonomous low-voltage power distribution systems that contain multiple distributed energy resources (DERs) and smart loads that can provide power system operation flexibility. To effectively control and coordinate multiple DERs and loads of microgrids, this paper proposes a distributed intelligent management system that employs a multi-agent-based control system so that delicate decision-making functions can be distributed to local intelligent agents. This paper presents the development of a hardware-in-the-loop simulation (HILS) system for distributed intelligent management system for microgrids and its promising application to an emergency demand response program. In the developed HILS system, intelligent agents are developed using microcontrollers and ZigBee wireless communication technology. Power system dynamic models are implemented in real-time simulation environments using the Opal-RT system. This paper presents key features of the data communication and management schemes based on multi-agent concepts. The performance of the developed system is tested for emergency demand response program applications.

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Supervised learning‐based demand response simulator with incorporation of real time pricing and peak time rebate

Sharma

Saxena

Palwalia

2021

Int Trans Electr Energ Syst

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Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non‐viable. In this paper, the footprint of two time‐based DR programs is explored on the peak reduction namely; Real‐Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN‐based DR topologies namely; Radial Basis Function Neural Network‐Demand Response (RBFN‐DR), Feedforward Backprop‐Demand Response (FFBP‐DR), Layer Recurrent‐Demand Response (LR‐DR), and Generalized Regression‐Demand Response (GR‐DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN‐DR proved its efficacy by giving better results for both price‐based programs namely; RTP and PTR.

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Demand Response Program Implementation Practices In Korea

Cited by 8 publications

References 2 publications

Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

Intelligent Control of Battery Energy Storage for Multi-Agent Based Microgrid Energy Management

Hardware-in-the-Loop Simulation of Distributed Intelligent Energy Management System for Microgrids

Supervised learning‐based demand response simulator with incorporation of real time pricing and peak time rebate

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