Capacity of Low-Voltage Grids for Distributed Generation: Classification by Means of Stochastic Simulations

Without appropriate counteraction, the high amount of installed distributed generators (DG) at the low-voltage distribution level may cause overloading of electrical equipments and violation of voltage limits in many grids. Because of the historically grown low-voltage grids and their local and geog...

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Bibliographic Details
Published in:IEEE transactions on power systems 2015-03, Vol.30 (2), p.689-700
Main Authors: Breker, Sebastian, Claudi, Albert, Sick, Bernhard
Format: Article
Language:English
Subjects:
Assessments
Classification
Computational modeling
Computer simulation
Data models
Distributed generation
Distribution system simulation
Electric potential
Electric power generation
Electricity distribution
Generators
grid classification
Load modeling
Loading
low-voltage grids
power distribution
power distribution economics
power distribution planning
power system modeling
Probabilistic logic
Stochastic processes
Stochasticity
Weibull distribution
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Summary:Without appropriate counteraction, the high amount of installed distributed generators (DG) at the low-voltage distribution level may cause overloading of electrical equipments and violation of voltage limits in many grids. Because of the historically grown low-voltage grids and their local and geographic dependencies, complex grid structures can be found. Thus, the discrimination of grids concerning their DG capacity is a difficult task. We propose a novel three-step classification strategy to distinguish various kinds of low-voltage grids regarding their DG capacity. Our method is based on a stochastic simulation procedure and a subsequent parametric stochastic modeling, which allows for a probability based classification approach. The classification results can be regarded as probabilistic class memberships or, if sharp memberships are required, the class with the maximum probability can be selected. The proposed approach will not only help distribution system operators to face the challenges in future grid planning and focus their work on further enhancement of weak grid structures, but it will also be valuable in choosing relevant grids for detailed surveys. To demonstrate that our approach actually leads to meaningful classification results for real low-voltage grids, we empirically evaluate the results for 300 real rural and suburban grids by comparing them to classification assessments of experts from distribution grid planning practice.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2014.2332361