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Substation Related Forecasts of Electrical Energy Storage Systems: Transmission System Operator Requirements

Author

Listed:
  • Tamara Schröter

    (Chair Electric Power Systems and Renewable Energy Sources, Otto Institute of Electric Power Systems, von Guericke University, 39106 Magdeburg, Germany)

  • André Richter

    (Chair Electric Power Systems and Renewable Energy Sources, Otto Institute of Electric Power Systems, von Guericke University, 39106 Magdeburg, Germany)

  • Jens Götze

    (Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany)

  • André Naumann

    (Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany)

  • Jenny Gronau

    (50Hertz Transmission GmbH, 15366 Neuenhagen bei Berlin, Germany)

  • Martin Wolter

    (Chair Electric Power Systems and Renewable Energy Sources, Otto Institute of Electric Power Systems, von Guericke University, 39106 Magdeburg, Germany)

Abstract

The growth in volatile renewable energy (RE) generation is accompanied by an increasing network load and an increasing demand for storage units. Household storage systems and micro power plants, in particular, represent an uncertainty factor for distribution networks, as well as transmission networks. Due to missing data exchanges, transmission system operators cannot take into account the impact of household storage systems in their network load and generation forecasts. Thus, neglecting the increasing number of household storage systems leads to increasing forecast inaccuracies. To consider the impact of the storage systems on forecasting, this paper presents a new approach to calculate a substation-specific storage forecast, which includes both substation-specific RE generation and load forecasts. For the storage forecast, storage systems and micro power plants are assigned to substations. Based on their aggregated behavior, the impact on the forecasted RE generation and load is determined. The load and generation are forecasted by combining several optimization approaches to minimize the forecasting errors. The concept is validated using data from the German transmission system operator, 50 Hertz Transmission GmbH. This investigation demonstrates the significance of using a battery storage forecast with an integrated load and generation forecast.

Suggested Citation

  • Tamara Schröter & André Richter & Jens Götze & André Naumann & Jenny Gronau & Martin Wolter, 2020. "Substation Related Forecasts of Electrical Energy Storage Systems: Transmission System Operator Requirements," Energies, MDPI, vol. 13(23), pages 1-26, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6207-:d:451105
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    References listed on IDEAS

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    1. van der Meer, D.W. & Shepero, M. & Svensson, A. & Widén, J. & Munkhammar, J., 2018. "Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes," Applied Energy, Elsevier, vol. 213(C), pages 195-207.
    2. Azuatalam, Donald & Paridari, Kaveh & Ma, Yiju & Förstl, Markus & Chapman, Archie C. & Verbič, Gregor, 2019. "Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 555-570.
    3. Ulf Philipp Müller & Birgit Schachler & Malte Scharf & Wolf-Dieter Bunke & Stephan Günther & Julian Bartels & Guido Pleßmann, 2019. "Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids," Energies, MDPI, vol. 12(11), pages 1-30, May.
    4. Reimuth, Andrea & Prasch, Monika & Locherer, Veronika & Danner, Martin & Mauser, Wolfram, 2019. "Influence of different battery charging strategies on residual grid power flows and self-consumption rates at regional scale," Applied Energy, Elsevier, vol. 238(C), pages 572-581.
    5. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
    6. Xiao, Ling & Wang, Jianzhou & Dong, Yao & Wu, Jie, 2015. "Combined forecasting models for wind energy forecasting: A case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 271-288.
    7. Luburić, Zora & Pandžić, Hrvoje & Plavšić, Tomislav & Teklić, Ljupko & Valentić, Vladimir, 2018. "Role of energy storage in ensuring transmission system adequacy and security," Energy, Elsevier, vol. 156(C), pages 229-239.
    8. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
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