Data-driven Demand Response Characterization and Quantification

Guillaume Le Ray, Pierre Pinson, Emil Mahler Larsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Analysis of load behavior in demand response (DR) schemes is important to evaluate the performance of participants. Very few real-world experiments have been carried out and quantification and characterization of the response is a difficult task. Nevertheless it will be a necessary tool for portfolio management of consumers in a DR framework. In this paper we develop methods to quantify and characterize the amount of DR in a load. The contribution to the aggregated load from each household is quantified on a daily basis, showing the potential variability of the response in time. Clustering on the average values and standard deviation of the contribution regroups households with the same average response. Independent Component Analysis (ICA) is used to characterize different DR delivery profiles.
Original languageEnglish
Title of host publicationProceedings of 12th IEEE Power and Energy Society PowerTech Conference
Number of pages6
PublisherIEEE
Publication date2017
Publication statusPublished - 2017
Event12th IEEE Power and Energy Society PowerTech Conference: Towards and Beyond Sustainable Energy Systems - University Place, University of Manchester., Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017

Conference

Conference12th IEEE Power and Energy Society PowerTech Conference
LocationUniversity Place, University of Manchester.
CountryUnited Kingdom
CityManchester
Period18/06/201722/06/2017

Keywords

  • Demand Response (DR)
  • DR characterization
  • DR quantification
  • Smart grid
  • Energy analytics

Cite this

Le Ray, G., Pinson, P., & Larsen, E. M. (2017). Data-driven Demand Response Characterization and Quantification. In Proceedings of 12th IEEE Power and Energy Society PowerTech Conference IEEE.
Le Ray, Guillaume ; Pinson, Pierre ; Larsen, Emil Mahler. / Data-driven Demand Response Characterization and Quantification. Proceedings of 12th IEEE Power and Energy Society PowerTech Conference. IEEE, 2017.
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Le Ray, G, Pinson, P & Larsen, EM 2017, Data-driven Demand Response Characterization and Quantification. in Proceedings of 12th IEEE Power and Energy Society PowerTech Conference. IEEE, 12th IEEE Power and Energy Society PowerTech Conference, Manchester, United Kingdom, 18/06/2017.

Data-driven Demand Response Characterization and Quantification. / Le Ray, Guillaume; Pinson, Pierre; Larsen, Emil Mahler.

Proceedings of 12th IEEE Power and Energy Society PowerTech Conference. IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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AU - Pinson, Pierre

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AB - Analysis of load behavior in demand response (DR) schemes is important to evaluate the performance of participants. Very few real-world experiments have been carried out and quantification and characterization of the response is a difficult task. Nevertheless it will be a necessary tool for portfolio management of consumers in a DR framework. In this paper we develop methods to quantify and characterize the amount of DR in a load. The contribution to the aggregated load from each household is quantified on a daily basis, showing the potential variability of the response in time. Clustering on the average values and standard deviation of the contribution regroups households with the same average response. Independent Component Analysis (ICA) is used to characterize different DR delivery profiles.

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KW - DR characterization

KW - DR quantification

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KW - Energy analytics

M3 - Article in proceedings

BT - Proceedings of 12th IEEE Power and Energy Society PowerTech Conference

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Le Ray G, Pinson P, Larsen EM. Data-driven Demand Response Characterization and Quantification. In Proceedings of 12th IEEE Power and Energy Society PowerTech Conference. IEEE. 2017