NDLI: Optimization of distribution network incorporating microgrid using vaccine-AIS

Content Provider	IEEE Xplore Digital Library
Author	Sicong Tan Jianxin Xu Panda, S.K.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Computer Engineering (ECE), National University of Singapore, Singapore (Jianxin Xu; Panda, S.K.) \|\| NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore (Sicong Tan)
Abstract	Previous studies focused only on the optimization of either microgrid load dispatch or reconfiguration power loss. Micorgrid economic load dispatch approach normally assumed that it could draw unlimited power from the utility grid. Network reconfiguration usually did not take microgrid into consideration. Thus, it is necessary to integrate these two sub-problems together in order to benefit the whole network. In this paper, an integrated solution that takes care of both microgrid load dispatch and network reconfiguration is proposed. The stochastic nature of wind, PV and load is taken into consideration. The forecasting of the wind, PV and load data are considered. The bio-inspired optimization scheme Vaccine-AIS is adopted to solve the problem. The results obtained have shown that the optimization technique is capable for this problem. By using the integration approach, microgrid can be incorporated into the network more effectively. The network can adjust itself more efficiently to allow the utilization of the renewable energy resources.
Starting Page	1381
Ending Page	1386
File Size	804136
Page Count	6
File Format	PDF
ISBN	9781467324199
ISSN	1553572X
e-ISBN	9781467324212
e-ISBN	9781467324205
DOI	10.1109/IECON.2012.6388539
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-25
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logic gates Optimization Meteorology History Generators Loading Forecasting
Content Type	Text
Resource Type	Article

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