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Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

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  • Valdes, Javier
  • Masip Macia, Yunesky
  • Dorner, Wolfgang
  • Ramirez Camargo, Luis

Abstract

Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.

Suggested Citation

  • Valdes, Javier & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2021. "Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications," Energy, Elsevier, vol. 215(PA).
    • Handle: RePEc:eee:energy:v:215:y:2021:i:pa:s0360544220320697
    DOI: 10.1016/j.energy.2020.118962
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    Cited by:

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    2. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    3. Schmeling, Lucas & Schönfeldt, Patrik & Klement, Peter & Vorspel, Lena & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2022. "A generalised optimal design methodology for distributed energy systems," Renewable Energy, Elsevier, vol. 200(C), pages 1223-1239.
    4. Ahir, Rajesh K. & Chakraborty, Basab, 2023. "A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs," Energy, Elsevier, vol. 282(C).
    5. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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