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Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

Author

Listed:
  • Jessica Hermanns

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Marcel Modemann

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Kamil Korotkiewicz

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Frederik Paulat

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Kevin Kotthaus

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Sven Pack

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

  • Markus Zdrallek

    (Institute of Power System Engineering, University of Wuppertal, 42119 Wuppertal, Germany)

Abstract

The number of renewable energy systems is still increasing. To reduce the worldwide CO 2 emissions, there will be even more challenges in the distribution grids like currently upcoming charging stations or heat pumps. All these new electric systems in the low voltage (LV) and medium voltage (MV) levels are characterized by an unsteady behavior. To monitor and predict the behavior of these new flexible systems, a grid state forecast is needed. This software tool calculates wind, photovoltaic, and load forecasts. These power forecasts are already in the focus of research, but there are some specific use cases, which require a more specific solution. To get a variously applicable software tool, different new functions to improve an already existing grid state forecast tool were developed and evaluated. For example, it will be proofed if a grid state forecast tool can be improved by calculating the number or the base load of the loads in grid areas by just one available measurement. Another big subject exists in the exchange of forecast information between different voltage levels. How this can be realized and how big the effect on the forecast quality is, will be analyzed. The results of these evaluations will be shown in this paper.

Suggested Citation

  • Jessica Hermanns & Marcel Modemann & Kamil Korotkiewicz & Frederik Paulat & Kevin Kotthaus & Sven Pack & Markus Zdrallek, 2020. "Evaluation of Different Development Possibilities of Distribution Grid State Forecasts," Energies, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1891-:d:344927
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    References listed on IDEAS

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    1. Baris Yuce & Monjur Mourshed & Yacine Rezgui, 2017. "A Smart Forecasting Approach to District Energy Management," Energies, MDPI, vol. 10(8), pages 1-22, July.
    2. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
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    Cited by:

    1. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

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