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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 Applied Energyarrow_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
Applied Energy
Article . 2010 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Optimal energy planning models with carbon footprint constraints

Authors: Dominic C. Y. Foo; Raymond R. Tan; Łukasz M. Pękala; Jacek Jeżowski;

Optimal energy planning models with carbon footprint constraints

Abstract

Abstract This paper describes a general modeling approach for optimal planning of energy systems subject to carbon and land footprint constraints. The methodology makes use of the source–sink framework derived from the analogies with resource conservation networks used in process integration. Two variants of the modeling approach are developed for some of the important technologies for carbon emissions abatement: liquid biofuels in transportation, and carbon dioxide capture and storage in power generation. Despite the positive impact on environment, widespread use of these technologies has certain disadvantages. In case of biofuels, their production may strain agricultural resources, that are needed also for satisfying food demands. At the same time, carbon capture and storage is rather expensive technology and its practical implementation in power facilities must be carefully considered and planned. Optimum utilization of both technologies is identified with flexible and expandable mathematical modeling framework. Case studies are used to illustrate the variants of the methodology.

  • BIP!
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    citations
    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).
    109
    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.
    Top 10%
    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 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
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!
109
Top 10%
Top 1%
Top 1%