Controlling a retailer's short‐term financial risk exposure using demand response

Brolin, Magnus; Sandels, Claes

doi:10.1049/iet-gtd.2018.6708

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…The number of electricity customers required for down-regulation in hours 1, 8, 17, and 24 is high, which means that there is not much flexibility (or willingness by the consumers to provide flexibility) to be activated; thus lower commitment for the aggregator in the wholesale market can be suggested. For hours 6,10,11,13,20,21,22, and 23, we stopped the simulations at 2.8 million customers without reaching 1 MW. It should, however, be noted that for a larger price deviation, the 1 MW flexibility could be achieved.…”

Section: B Customer Activationmentioning

confidence: 99%

Behind-the-Meter Energy Flexibility Modelling for Aggregator Operation with a Focus on Uncertainty

Blomgren¹,

Ebrahimy²,

Kani

et al. 2021

2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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Aggregators are expected to become an inevitable entity in future power system operation, playing a key role in unlocking flexibility at the edge of the grid. One of the main barriers to aggregators entering the market is the lack of appropriate models for the price elasticity of flexible demand, which can properly address time dependent uncertainty as well as non-linear and stochastic behavior of end-users in response to time varying prices. In this paper, we develop a probabilistic price elasticity model utilizing quantile regression and B-splines with penalties. The proposed model is tested using data from residential and industrial customers by assuming automation through energy management systems. Additionally, we show an application of the proposed method in quantifying the number of consumers needed to achieve a certain amount of flexibility through a set of simulation studies.

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Section: B Customer Activationmentioning

confidence: 99%

Behind-the-Meter Energy Flexibility Modelling for Aggregator Operation with a Focus on Uncertainty

Blomgren¹,

Ebrahimy²,

Kani

et al. 2021

2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)

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“…Price‐based demand response (DR) and incentive‐based DR, such as TOU pricing, critical peak pricing, and interruptible load contract, can also be regarded as electricity retail products. These products are employed to adjust end‐users’ consumption (usually to shift loads from high price periods to other periods), thereby reducing the energy procurement costs [6–8] of electricity retailers or mitigating their short‐term financial risks in the electricity spot market [9]. However, most residential and small commercial consumers equipped with smart meters are still charged with flat tariffs, and electricity retailers are unable to directly control the loads of residential consumers as they do for large‐scale industrial users [10].…”

Section: Introductionmentioning

confidence: 99%

Time‐phased electricity package design for electricity retailers considering bounded rationality of consumers

Zhi

Lin

Wen

et al. 2021

Energy Conversion and Econom

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The ever‐increasing competition in electricity retail markets has become one of the pivotal causes for electricity retailers to provide attractive packages to consumers for enhancing their market shares. Based on the current user‐side time‐of‐use electricity price mechanism in China, a bi‐level optimization model for designing a time‐phased and quantity‐incentive electricity package is proposed for electricity retailers considering bounded rationality of consumers. In the upper‐level model, the optimization objective is to maximize the electricity purchase and sale revenue of a specified retailer. In the lower‐level model, consumers are modelled as bounded rational decision‐makers whose objective is to purchase the electricity package with the highest value, considering the value of the excess electricity of the package. In addition, consumer psychology theory is introduced to describe the bounded rational responses of consumers to electricity packages. Simulations based on data of residential consumers in a city of China illustrate that by properly determining package quotas in different time periods, electricity retailers can guide consumers to change consumption time and volume, and hence, increase their revenues by increasing trading volume or reducing electricity procurement costs in forward bilateral transactions and spot markets.

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“…As an important intermediary agent between the wholesale market and their consumers, retailers are responsible for obtaining electricity from various sources of energy and selling electricity to end-users [15,16]. During the last years, growing attention has been paid to electricity procurement strategies for electricity retailers.…”

Section: Introductionmentioning

confidence: 99%

Bi‐layer portfolio selection model for electricity retailers based on behavioural portfolio theory under quota obligation of RPS

Jiang

Liu

Yang

et al. 2020

IET Generation, Transmission & Distribution

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Controlling a retailer's short‐term financial risk exposure using demand response

Cited by 5 publications

References 52 publications

Behind-the-Meter Energy Flexibility Modelling for Aggregator Operation with a Focus on Uncertainty

Behind-the-Meter Energy Flexibility Modelling for Aggregator Operation with a Focus on Uncertainty

Time‐phased electricity package design for electricity retailers considering bounded rationality of consumers

Bi‐layer portfolio selection model for electricity retailers based on behavioural portfolio theory under quota obligation of RPS

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