Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

Yousefi, Shaghayegh; Moghaddam, Mohsen Parsa; Majd, Vahid Johari

doi:10.1016/j.energy.2011.06.045

Cited by 162 publications

(80 citation statements)

References 22 publications

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“…En [10], [11] y [12] el actor principal en respuesta a la demanda es el usuario final. En [11] se introdujo una función de demanda que permite representar la demanda de diferentes tipos de cliente, además, de un modelo integral de respuesta de los consumidores ante variaciones de precios.…”

Section: Introductionunclassified

“…En [11] se introdujo una función de demanda que permite representar la demanda de diferentes tipos de cliente, además, de un modelo integral de respuesta de los consumidores ante variaciones de precios. En [10] se propone un modelo óptimo de respuesta a la demanda, que maximiza el beneficio para el proveedor, al tiempo que minimiza el consumo diario de energía eléctrica por parte del usuario final como respuesta a la tarifa diferenciada.…”

Section: Introductionunclassified

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Cuantificación del ahorro de energía eléctrica en clientes residenciales mediante acciones de gestión de demanda

Pavas

Vivas

Rosas

2017

revuin

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Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 y se referencia usando el siguiente formato: O. González, A. Pavas, S. Sánchez, "Cuantificación del ahorro de energía eléctrica en clientes residenciales mediante acciones de gestión de demanda", UIS Ingenierías, vol. RESUMENColombia enfrentó entre 2015 y 2016 uno de los fenómenos de El Niño más severos de su historia quedando ad-portas de un racionamiento de energía. La gestión de demanda puede ayudar, entre otros aspectos, a reducir el consumo de energía en cualquier escenario de disponibilidad de recursos energéticos, no sólo en emergencia. El artículo presenta la cuantificación de los posibles ahorros en clientes residenciales en Bogotá D.C., determinados a partir del modelado de diferentes estrategias de gestión de demanda. Los resultados muestran que pueden lograrse reducciones en el consumo entre 6 y 26% mediante sustitución de dispositivos, 7 y 19% mediante cambio de hábitos de consumo, y entre 17% y 45% como ahorro total posible. Los resultados también revelan que los clientes con menores consumos de energía pueden alcanzar ahorros de hasta el 45%, mientras que los clientes con consumos mayores de 150 kWh/mes no logran ahorros superiores al 20%.PALABRAS CLAVE: Ahorro de energía, Gestión de demanda, Estrategias de gestión de demanda, Modelado de perfiles de carga. ABSTRACTColombia faced from 2015 to 2016 one of most severe El Niño whether phenomenal in its history, being close to energy rationing. Demand management can help to reduce energy consumption under any scenario of energy availability, not only under emergencies. This article presents the quantification of potential savings in residential customers in Bogotá D.C., determined from the modeling of different demand management strategies. The results show energy reduction between 6 and 26% resorting to device substitution, 7 and 19% through energy consumption patterns adjustments, and between 17% and 45% as possible total energy savings. The results reveal as well that customers with low energy consumption can reach upto 45% savings, in contrast to customers with consumption energy greater than 150 kWh/month cannot reach energy saving above 20%.

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Section: Introductionunclassified

Cuantificación del ahorro de energía eléctrica en clientes residenciales mediante acciones de gestión de demanda

Pavas

Vivas

Rosas

2017

revuin

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“…where v is the pre-specified portion of the nominal load, and thus (17) guarantees that the load curtailment is smaller than a pre-specified portion of the nominal load.…”

Section: Optimal Scheduling Modelmentioning

confidence: 99%

“…As the electricity price has a great effect on the power consumption of end users [16], in this paper the DR is treated as an equivalent virtual generation unit in order to reflect the sensitivity of load consumption demand to the change electricity price. According to [17], the power consumption of end users with DR utilization can be expressed as:…”

Section: Demand Responsementioning

confidence: 99%

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

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“…Schweppes and his co-workers developed the concept of spot pricing of electricity to evaluate variable 4 costs of electric energy on an hourly basis and proposed three responsive load models, namely linear, potential and exponential demand functions [13]. A customers' response to the optimal real time prices has been modeled in [14] for the electricity applying multifarious mathematical load models. An optimization model to adjust the hourly load level of a given consumer in response to hourly electricity prices is proposed in [15].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Negawatt Demand Response Resource Modeling and Prioritizing in Power Markets

2017

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In recent years, integrated use of demand-and supply-side resources has been performed by electric utilities, because of its potential attractiveness, both at operation and economic levels. Demand Response Resources (DRRs) can be used as demand side options which are the consequence of implementing Demand Response Programs (DRPs). DRPs comprise the actions taken by end-use customers to reduce their electricity consumption in response to electricity market's high prices; and/or reliability problems on the electricity network. In this paper, a dynamic economic model of DRPs is derived based upon the concept of flexible elasticity of demand and the customer benefit function. Precise modeling of these virtual negawatt resources helps system operators to investigate the impact of responsive loads on power system studies. This paper also aims to prioritize multifarious DRPs by means of

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Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

Cited by 162 publications

References 22 publications

Cuantificación del ahorro de energía eléctrica en clientes residenciales mediante acciones de gestión de demanda

Cuantificación del ahorro de energía eléctrica en clientes residenciales mediante acciones de gestión de demanda

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Dynamic Negawatt Demand Response Resource Modeling and Prioritizing in Power Markets

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