The change towards future distribution networks requires new and updated monitoring and control functions, which involve a large amount of information. The treatment of a significant part of this information can be simplified by introducing the concept of Load Area, a group of prosumers whose power injection has a similar impact on the distribution grid operating conditions. Thanks to the Load Area concept, a compact yet accurate representation of distribution networks can be obtained. The Load Areas allow to select the relevant data for both monitoring and control functionalities of distribution systems, as well as to represent the relationships between such data in a compact representation. In this paper, the Load Area concept is recalled, and specialized methods are applied to get a compact representation that exploit the radiality. The results from two test cases confirm the viability of the proposed methods and highlight the computational gains deriving from their adoption.

Specialized Methods for the Implementation of Load Areas in Radial Distribution Networks

CASOLINO, Giovanni Mercurio;LOSI, Arturo
2016-01-01

Abstract

The change towards future distribution networks requires new and updated monitoring and control functions, which involve a large amount of information. The treatment of a significant part of this information can be simplified by introducing the concept of Load Area, a group of prosumers whose power injection has a similar impact on the distribution grid operating conditions. Thanks to the Load Area concept, a compact yet accurate representation of distribution networks can be obtained. The Load Areas allow to select the relevant data for both monitoring and control functionalities of distribution systems, as well as to represent the relationships between such data in a compact representation. In this paper, the Load Area concept is recalled, and specialized methods are applied to get a compact representation that exploit the radiality. The results from two test cases confirm the viability of the proposed methods and highlight the computational gains deriving from their adoption.
2016
978-889410512-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/57272
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