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Methodology for Generating Synthetic Load Profiles for Different Industry Types

Author

Listed:
  • Anna Sandhaas

    (Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany)

  • Hanhee Kim

    (Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany)

  • Niklas Hartmann

    (Institute of Sustainable Energy Systems, Offenburg University of Applied Sciences, 77652 Offenburg, Germany)

Abstract

To achieve its climate goals, the German industry has to undergo a transformation toward renewable energies. To analyze this transformation in energy system models, the industry’s electricity demands have to be provided in a high temporal and sectoral resolution, which, to date, is not the case due to a lack of open-source data. In this paper, a methodology for the generation of synthetic electricity load profiles is described; it was applied to 11 industry types. The modeling was based on the normalized daily load profiles for eight electrical end-use applications. The profiles were then further refined by using the mechanical processes of different branches. Finally, a fluctuation was applied to the profiles as a stochastic attribute. A quantitative RMSE comparison between real and synthetic load profiles showed that the developed method is especially accurate for the representation of loads from three-shift industrial plants. A procedure of how to apply the synthetic load profiles to a regional distribution of the industry sector completes the methodology.

Suggested Citation

  • Anna Sandhaas & Hanhee Kim & Niklas Hartmann, 2022. "Methodology for Generating Synthetic Load Profiles for Different Industry Types," Energies, MDPI, vol. 15(10), pages 1-29, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3683-:d:817900
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    References listed on IDEAS

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    1. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    2. Abuzayed, Anas & Hartmann, Niklas, 2022. "MyPyPSA-Ger: Introducing CO2 taxes on a multi-regional myopic roadmap of the German electricity system towards achieving the 1.5 °C target by 2050," Applied Energy, Elsevier, vol. 310(C).
    3. Wietze Lise & Jos Sijm & Benjamin Hobbs, 2010. "The Impact of the EU ETS on Prices, Profits and Emissions in the Power Sector: Simulation Results with the COMPETES EU20 Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(1), pages 23-44, September.
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    Cited by:

    1. Andrii Radchenko & Mykola Radchenko & Hanna Koshlak & Roman Radchenko & Serhiy Forduy, 2022. "Enhancing the Efficiency of Integrated Energy Systems by the Redistribution of Heat Based on Monitoring Data," Energies, MDPI, vol. 15(22), pages 1-18, November.
    2. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
    3. Silva, Walquiria N. & Bandória, Luís H.T. & Dias, Bruno H. & de Almeida, Madson C. & de Oliveira, Leonardo W., 2023. "Generating realistic load profiles in smart grids: An approach based on nonlinear independent component estimation (NICE) and convolutional layers," Applied Energy, Elsevier, vol. 351(C).

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