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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE Intelligent Sys...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
IEEE Intelligent Systems
Article . 2008 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
DBLP
Article . 2008
Data sources: DBLP
UQ eSpace
Article . 2008
Data sources: UQ eSpace
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Managing Household Wind-Energy Generation

Authors: Geoff James; Wei Peng 0011; Ke Deng;

Managing Household Wind-Energy Generation

Abstract

The use of intelligent-agent technology to aggregate wind-energy generation installed at several households and battery storage installed in similarly large number of households is described. This situation creates a virtual generator that can be dispatched on the electricity grid in a similar manner to to centralized generation. The purpose of aggregation is to sell renewable generation to the electricity network and at a price matching its value. Australia and other countries are also considering large-scale deployments of renewable-energy technology, such as solar photovoltaic generation or wind turbines for households. It is observed that changing technology, new market mechanisms, new funding mechanisms, and environmental concerns are making such deployments an attractive proposition. Some deployments are also taking the form of large farms of wind turbines or photovoltaic cells.

Country
Australia
Keywords

690, Artificial Intelligence, Computer Networks and Communications, 1702 Cognitive Sciences, 1705 Computer Networks and Communications

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    7
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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