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Nov 27, 2017

Quick load simulation on a ­distribution grid

Physical Sciences, Energy and Environment

  • Electrical distribution grid simulation
  • Extremely small conputational time
  • Versatile implementation (small and big grids)

Your contact

Dr. Tobia Mancabelli

E-Mail:
tmancabelli@baypat.de
Phone:
+49 (0) 89 5480177 - 11
Reference Number:
B75231

Challenge

Delocalized electricity generators, usually consisting of renewable energy sources, are ­becoming more and more common. In order to reasonably estimate the load of the grid, the electric network is simulated with an appropriate model including the all energy sources and users. Such a simulation can become particullary difficult, given the increasing number of the delocalized renovable energy sources, in particular with relation to their temporal ­fluctuation. Normally the many different possible senarios are estimated through a Monte Carlo ­Simulation (MCS). Given the complexity of the problem and the high number of variables a MCS can be unfeasable in an acceptable time frame.

Innovation

For the design of the grid it is not necessary to consider the full commulative density function, but it is usually enough to consider the 5%, 50% and 95%-quantiles. Based on the Common Rank Approxima-tion (CRA) the problem is solved as follows:

  • The possible senarios are simulated through a simplified model;
  • The 5%, 50% and 95%-quantiles are identified;
  • Exact results are calculated running the correct model only for the relevant quantiles.

According to this method, the time needed for a simulation is drastically reduced. Despite the appro-ximation, the obtained results are highly accurate.

Commercial Opportunities

The invention can be implemented in a software dedicated to the design of distribution grids. The mathematical model is extremely flexible and can be applied to the design of small and big distribution grids as well.

Development Status

The model has been tested for different scenarios, leading in any of the analysed cases to reliable results and always showing drastically reduced computational time.

References

    • IEEE PES Conference and Exposition, Dallas 978-1-5090-2157-4/16 (2016)
    • EP 3 411 812 A1 US 2019/0042960 A1

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