Options
Probabilistic Security Constrained Fuzzy Power Flow Models
Date Issued
09 September 2016
Date Available
16T17:59:30Z November 2016
Abstract
In restructured power systems, generation and commercialization activities became market activities, while transmission and distribution activities continue as regulated monopolies. As a result, the adequacy of transmission network should be evaluated independent of generation system. After introducing the constrained fuzzy power flow (CFPF) as a suitable tool to quantify the adequacy of transmission network to satisfy 'reasonable demands for the transmission of electricity' (as stated, for instance, at European Directive 2009/72/EC), the aim is now showing how this approach can be used in conjunction with probabilistic criteria in security analysis. In classical security analysis models of power systems are considered the composite system (generation plus transmission). The state of system components is usually modeled with probabilities and loads (and generation) are modeled by crisp numbers, probability distributions or fuzzy numbers. In the case of CFPF the component’s failure of the transmission network have been investigated. In this framework, probabilistic methods are used for failures modeling of the transmission system components and possibility models are used to deal with 'reasonable demands'. The enhanced version of the CFPF model is applied to an illustrative case.
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2016 IEEE
Language
English
Status of Item
Not peer reviewed
Part of
2016 51st International Universities Power Engineering Conference (UPEC)
Conference Details
UPEC 2016 - 51st International Universities Power Engineering Conference, Coimbra, Portugal, 6-9 September 2016
This item is made available under a Creative Commons License
File(s)
Owning collection
Scopus© citations
1
Acquisition Date
Dec 11, 2023
Dec 11, 2023
Views
1352
Last Month
2
2
Acquisition Date
Dec 11, 2023
Dec 11, 2023
Downloads
369
Last Month
3
3
Acquisition Date
Dec 11, 2023
Dec 11, 2023