A Monte-Carlo approach for quality of supply simulation

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CIRED 2019 - 1002.pdf (1.28 MB)

Paper number

1002

Working Group Number

Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

Short title

Convener

Authors

Farah Semlali, Hicham, Enedis, France
Robin, Florence, Enedis, France
Donde, Cecile, EDF R&D, France
Chaudonneret, Thomas, EDF R&D, France

Abstract

Enedis is required to ensure a high level of continuity of supply to its customers. However, the quality of supply is not uniform throughout the network, especially in remote areas where quality can be much lower than average. At the same time, indexes such as SAIDI can vary significantly on a year-to-year basis as a result of climatic hazards.In response to this context, to complement traditional observations of the outages undergone by customers, a new software model QAT “Qualité d’Alimentation des Territoires” («Power Supply Quality of Territories»), based on a probabilistic approach to quality of supply assessment, has been developed by ENEDIS. QAT allows the DSO to measure objectively the improvement of the SAIDI following an evaluation of the expectation and the variance of this metric. QAT provides a probabilistic forecast of the SAIDI regarding projected investments and maintenance programs. This tool has been designed to be implemented within the commercial network planning software Power Factory used by several DSOs. It was developed in DPL (Digsilient Programming Language).A four-year in-field experimentation has validated the results provided by QAT and shown a great potential diversity in its use. The model is now integrated within decision process thanks to the restitution of new indexes aiming at evaluating distribution networks on different criteria such as reliability, quality of structure and reactivity of the operational teams. Many evolutions of this model are under consideration and should improve the simulation of climatic hazards’ impact on distribution networks.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/241
http://dx.doi.org/10.34890/471

ISSN

2032-9644

ISBN

978-2-9602415-0-1