Evaluation of Different Development Possibilities of Distribution Grid State Forecasts

Jessica Hermanns, Marcel Modemann, Kamil Korotkiewicz, Frederik Paulat, Kevin Kotthaus, Sven Pack and Markus Zdrallek
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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

Energies, 2020, vol. 13, issue 8, 1-17

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.

Keywords: grid state forecast; smart grids; distribution grids; congestion management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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