NDLI: Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Carrano, E.G. Guimaraes, F.G. Takahashi, R.H.C. Neto, O.M. Campelo, F.
Copyright Year	1969
Abstract	This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties
Sponsorship	IEEE Power Engineering Society
Starting Page	851
Ending Page	861
Page Count	11
File Size	330768
File Format	PDF
ISSN	08858950
Volume Number	22
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Immune system Costs Network topology Conductors Performance evaluation Performance analysis Robustness Sensitivity analysis Power distribution power distribution planning Artificial immune systems load evolution uncertainty multiobjective sensitivity analysis network optimization
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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