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IOP Conference Series : Earth and Environmental Science
Article . 2018 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Research on Multi-objective Optimization Linear Programming Problem of Power Generation under Carbon Emission Trading

Authors: Guoying Wu; Erfeng Xu; Dunnan Liu; Yingjing He;

Research on Multi-objective Optimization Linear Programming Problem of Power Generation under Carbon Emission Trading

Abstract

Carbon emissions trading is an important means to achieve the target of low carbon and energy saving, the construction and promotion of which has been widely concerned in recent years. As carbon emitters, electricity industry is shouldering more pressure to reduce emissions and will definitely participate in carbon emissions trading. Energy saving dispatching is one of the most important ways to realize energy saving and emission reduction for electricity power companies. One kind of multi-objective optimization linear programming model for generating units under carbon emissions trading is constructed in this paper. The model is verified by a practical case analysis, which ensures the optimal allocation of power resources with multi-objective of Energy saving, emission reduction and economy, and sustainable development of electricity power companies.

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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!
0
Average
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