Demand Response in Electricity Markets: An Overview

Albadi, Mohammed; El-Saadany, Ehab F.

doi:10.1109/pes.2007.385728

Cited by 638 publications

(376 citation statements)

References 4 publications

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“…This enables the company to remotely change tariffs in electricity meters, inform about the current price of electricity or turn off or on the equipment thus performing peak demand reduction, handling an emergency situation or ensure grid stability (Gardner, 1995) (Albadi & El-Saadany, 2007). This control involves an interaction between the utility and the end-use equipment, e.g., the water heater, the air conditioning or public lighting.…”

Section: Theory Of Load Managementmentioning

confidence: 99%

“…Another type of direct LM program may include demand bidding, which can be understood as an extension of load curtailment programs (Charles River Associates, 2005). Such programs are based on consumers bids in the wholesale electricity market, based on electricity prices they are willing to pay or the amount of demand they can reduce (Gillingham, Newell, & Palmer, 2004) (Albadi & El-Saadany, 2007) (Albadi & El-Saadany, 2008). It is also highlighted by Kostková, Omelina, Kyčina, & Jamrich (2013) benefiting from the stimuli provided by the grid, e.g., RTP, and saving money and energy.…”

Section: Theory Of Load Managementmentioning

confidence: 99%

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Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

Miguel

Neves

Martins

2014

Electric Power Systems Research

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This work proposes a methodology to evaluate the energy and power resources that can be made available through the deployment and use of a residential energy management system, as the Energy Box (EB) proposed by Livengood & Larson (2009). The impact on the electricity distribution system of such technology will result of changes in the electricity usage by end-use customers in response to stimuli like real-time changes in the electricity price, corresponding to the widely known demand response (DR). However, due to the dispersed and uncontrolled nature of the management of end-use appliances, estimating the energy and power output of the aggregation of a high number of EB's requires a specific approach which can take into account the random nature of load response.The approach that was followed simulated a certain universe of consumers from one particular city, using information regarding total electricity consumption as well as the segregated consumption per main appliance. The proposed methodology makes use of queried data regarding the willingness of consumers to postpone the start of appliances, as well as prototypes of hourly price diagrams. These prototypes were obtained through a data clustering exercise that was also proposed as part of the methodology. The possible environmental impact of the aggregated use of EB's was also estimated.The output of the methodology includes the range of released network capacity as well as load rebound, both caused by the aggregated EB response. In particular, load rebound is a relevant phenomenon that presents new challenges to the management of the grid, and for which some preventive measures are suggested.The analysis of the environmental significance of the aggregated EB resource shows that DR is not per se a tool to reduce CO 2 emissions, being strongly dependent of the generation technologies that are used to compensate demand variations. ResumoEste trabalho propõe uma metodologia para avaliar os recursos de energia e de potência que podem ser disponibilizados através da disseminação e utilização de um sistema de gestão de energia no sector residencial, como a Energy Box (EB) proposta por Livengood & Larson (2009). O impacto de tal tecnologia sobre o sistema de distribuição de energia elétrica irá resultar de mudanças no uso da eletricidade por utilização final de clientes em resposta a estímulos como mudanças em tempo real no preço da electricidade, correspondendo ao amplamente conhecido conceito de gestão da procura (DR). No entanto, a natureza dispersa e não controlada da gestão destas cargas exige uma abordagem que tenha em conta o caráter aleatório do comportamento desta procura para efetuar uma estimativa agregada do consumo de energia e da potência causados por um elevado número de dispositivos EB.A abordagem que foi seguida exigiu a simulação de um determinado universo de consumidores de uma determinada cidade, utilizando informações sobre o consumo total de energia eléctrica, bem como o consumo por eletrodoméstico. A metodologia proposta faz uso de...

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Section: Theory Of Load Managementmentioning

confidence: 99%

Section: Theory Of Load Managementmentioning

confidence: 99%

Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

Miguel

Neves

Martins

2014

Electric Power Systems Research

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“…As a part of the smart grid architecture, a home/building energy management (HEM/BEM) system can be an effective tool to reduce peak electricity demand and improve electric utility load factor [1]. It can help mitigate power system stress conditions and improve the utilization of generation, transmission and distribution resources.…”

Section: Introductionmentioning

confidence: 99%

Enabling Residential Demand Response Applications with a ZigBee-Based Load Controller System

et al. 2016

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Home energy management (HEM) systems are getting increasingly popular in both academic and commercial communities. Most HEM solutions rely on either smart appliances or plugging traditional appliances into smart outlet/smart plugs which can communicate with the central HEM server. Conventional non-communicating powerintensive loads that are directly hard-wired to the supply panel of the house in the U.S., e.g., water heaters and clothes dryers, therefore are difficult to be made part of an HEM system. This paper describes the design and implementation of a microcontroller and ZigBee based load controller system that allows these loads to be controlled by an HEM system. The proposed system consists of four load controllers (LCs) that monitor and control four power-intensive household loads, and one communication controller (CC) that acts as a communication medium between LCs and a central HEM unit. The CC and all the LCs are packaged into one load controller box that can be placed beside the main circuit panel of the house and the wirings to the power-intensive loads are routed through the LCs. Within the box, the LCs and the CC communicate via hard-wired serial peripheral interface. The CC then communicates with the HEM system via ZigBee. The proposed system is expected to offer a cost-effective method

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“…This low load factor causes inefficient operation of generation, transmission and distribution resources. To avoid system stress conditions due to increasing demand [1] and to use power system resources more efficiently, demand response (DR) can serve as an effective tool to reduce peak demand through demand-side load curtailment [2]. A fully automated DR program [3] is one that can provide load management to meet electric utility needs without direct customer intervention.…”

Section: Introductionmentioning

confidence: 99%

A Robust Building Energy Management Algorithm Validated in a Smart House Environment

et al. 2015

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A building energy management (BEM) system is a core hardware/software platform that enables demand response applications for building operators in the smart grid environment. This paper presents the BEM algorithm that is designed to be robust against communication failures and data errors. It has been implemented in the smart building located at Yildiz Technical University in Istanbul, Turkey, and results are reported herein. Appliance usage profiles and customer preferences used in the BEM demonstration were derived from a survey of Turkish customers. Both summer and winter usage profiles were used to validate the efficacy of the proposed algorithm in a real-world smart building environment. The paper also discusses lessons learned from field implementation.

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Demand Response in Electricity Markets: An Overview

Cited by 638 publications

References 4 publications

Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

Methodology to simulate the impact of a large deployment of a residential energy management system in the electricity grid

Enabling Residential Demand Response Applications with a ZigBee-Based Load Controller System

A Robust Building Energy Management Algorithm Validated in a Smart House Environment

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