Possibilistic-scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment

Files in This Item:
File Description SizeFormat 
mix.PDF266.59 kBAdobe PDFDownload
Title: Possibilistic-scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment
Authors: Soroudi, Alireza
Permanent link: http://hdl.handle.net/10197/6176
Date: Aug-2012
Online since: 2014-11-18T15:35:50Z
Abstract: The distribution network operators (DNOs) are responsible for securing a diverse and viable energy supply for their customers so the technical and economical impacts of distributed generation (DG) units are of great concerns. Traditionally, the DNOs try to maximize the technical performance of the distribution network, but it is evident that the first step in optimizing a quantity is being able to calculate it. The DG investment/operation which is performed by distributed generation operators/owners (DGOs) (under unbundling rules) has made this task more complicated. This is mainly because the DNO is faced with the uncertainties related to the decisions of DG investors/operators where some of them can be probabilistically modeled while the others are possibilistically treated. This paper proposes a hybrid possibilistic-probabilistic DG impact assessment tool which takes into account the uncertainties associated with investment and operation of renewable and conventional DG units on distribution networks. This tool would be useful for DNOs to deal with the uncertainties which some of them can be modeled probabilistically and some of them are described possibilistically. The proposed method has been tested on a test system and a large-scale real distribution network to demonstrate its strength and flexibility.
Type of material: Journal Article
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE Transactions on Power Systems
Volume: 27
Issue: 3
Start page: 1283
End page: 1293
Copyright (published version): 2012 Institute of Electrical and Electronics Engineers
Keywords: Distributed generationFuzzy setsStochastic approximationUncertaintyRisk analysisWind energy
DOI: 10.1109/TPWRS.2011.2180933
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

Show full item record

Citations 5

Last Week
Last month
checked on Feb 20, 2019

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.