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Research Papers: Energy Systems Analysis

A Novel Inexact Two-Stage Stochastic Robust-Compensation Model for Electric Supply Environmental Management Under Uncertainty

[+] Author and Article Information
W. Li

Professor
China-Canada Energy and
Environmental Research Center,
North China Electric Power University,
Beijing 102206, China
e-mail: weili1027@gmail.com

S. X. Liu

China-Canada Energy and
Environmental Research Center,
North China Electric Power University,
Beijing 102206, China
e-mail: sanxing1113@163.com

Z. H. Fu

China-Canada Energy and
Environmental Research Center,
North China Electric Power University,
Beijing 102206, China
e-mail: fzhncepu@gmail.com

H. D. Shi

China Academy of Environmental Science,
Beijing 100012, China
e-mail: shihd@craes.org.cn

Y. L. Xie

China-Canada Energy and
Environmental Research Center,
North China Electric Power University,
Beijing 102206, China
e-mail: xieyulei850228@gmail.com

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received July 10, 2014; final manuscript received June 10, 2015; published online June 30, 2015. Assoc. Editor: Kau-Fui Wong.

J. Energy Resour. Technol 137(6), 062001 (Jun 30, 2015) (10 pages) Paper No: JERT-14-1210; doi: 10.1115/1.4030844 History: Received July 10, 2014

In this study, a novel inexact two-stage stochastic robust-compensation programming (ITSP-RC) model is developed for CO2 emission reduction management under uncertainties. This model is attempted to integrate ITSP and stochastic RC programming into a general framework and apply the ITSP-RC for power management and CO2 emission reduction management, such that the developed model can tackle uncertainties described in terms of interval values and probability distributions over a two-stage context. Moreover, it can reflect dynamic and randomness of the energy systems during the planning horizon. The developed method has been applied to a case to solve CO2 emission management problem in electric supply environmental management. A number of scenarios corresponding to different adoption rate levels of carbon capture, utilization, and storage technology are examined. With the RC programming, regional energy systems would have a stable financial budget. The result suggests that the methodology is applicable for reflecting complexities of large-scale energy management systems and addressing CO2 emissions reduction issue with the planning period.

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References

Figures

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Fig. 3

Amount of optimal power generation under 20% carbon treatment: (a) low demand level, (b) medium demand level, and (c) high demand level

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Fig. 1

Framework of ITSP-RC model

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Fig. 2

Optimized power generation amount without considering carbon treatment: (a) low demand level, (b) medium demand level, and (c) high demand level. (“T,” “H,” “W,” and “S” denote “thermal power,” “hydropower,” “wind power,” and “solar power,” respectively.)

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Fig. 4

Solutions of power generation under different scenarios in September

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Fig. 5

Optimal amount of CO2 emission under different scenarios

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Fig. 6

Total costs under different scenarios

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