Analysis of rebound effect modelling for flexible electrical consumers

Giulia De Zotti, Daniela Guericke, Seyyed Ali Pourmousavi Kani, Juan Miguel Morales, Henrik Madsen, Niels Kjølstad Poulsen

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate flexibility provided. The stochastic nature of consumers’ price response is also considered in this study using chanceconstrained (CC) programming.
Original languageEnglish
Book seriesI F A C Workshop Series
Volume52
Issue number4
Pages (from-to)6-11
ISSN1474-6670
DOIs
Publication statusPublished - 2019
EventIFAC Workshop on Control of Smart Grid and Renewable Energy Systems - Hyatt Regency Jeju, Jeju, Korea, Republic of
Duration: 10 Jun 201912 Jun 2019
http://csgres2019.com/

Conference

ConferenceIFAC Workshop on Control of Smart Grid and Renewable Energy Systems
LocationHyatt Regency Jeju
CountryKorea, Republic of
CityJeju
Period10/06/201912/06/2019
Internet address

Keywords

  • Rebound effect
  • Flexible consumers
  • Mixed-integer linear program
  • Chanced-constrained programming
  • Demand response
  • Dynamic prices

Cite this

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title = "Analysis of rebound effect modelling for flexible electrical consumers",
abstract = "Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate flexibility provided. The stochastic nature of consumers’ price response is also considered in this study using chanceconstrained (CC) programming.",
keywords = "Rebound effect, Flexible consumers, Mixed-integer linear program, Chanced-constrained programming, Demand response, Dynamic prices",
author = "{De Zotti}, Giulia and Daniela Guericke and Kani, {Seyyed Ali Pourmousavi} and Morales, {Juan Miguel} and Henrik Madsen and Poulsen, {Niels Kj{\o}lstad}",
year = "2019",
doi = "10.1016/j.ifacol.2019.08.146",
language = "English",
volume = "52",
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issn = "1474-6670",
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Analysis of rebound effect modelling for flexible electrical consumers. / De Zotti, Giulia; Guericke, Daniela; Kani, Seyyed Ali Pourmousavi; Morales, Juan Miguel; Madsen, Henrik; Poulsen, Niels Kjølstad.

In: I F A C Workshop Series, Vol. 52, No. 4, 2019, p. 6-11.

Research output: Contribution to journalConference articleResearchpeer-review

TY - GEN

T1 - Analysis of rebound effect modelling for flexible electrical consumers

AU - De Zotti, Giulia

AU - Guericke, Daniela

AU - Kani, Seyyed Ali Pourmousavi

AU - Morales, Juan Miguel

AU - Madsen, Henrik

AU - Poulsen, Niels Kjølstad

PY - 2019

Y1 - 2019

N2 - Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate flexibility provided. The stochastic nature of consumers’ price response is also considered in this study using chanceconstrained (CC) programming.

AB - Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential flexibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate flexibility provided. The stochastic nature of consumers’ price response is also considered in this study using chanceconstrained (CC) programming.

KW - Rebound effect

KW - Flexible consumers

KW - Mixed-integer linear program

KW - Chanced-constrained programming

KW - Demand response

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