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An optimized low-carbon production planning model for power industry in coal-dependent regions - A case study of Shandong, China

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  • Wu, C.B.
  • Guan, P.B.
  • Zhong, L.N.
  • Lv, J.
  • Hu, X.F.
  • Huang, G.H.
  • Li, C.C.

Abstract

Due to the coal-dominated power generation structure, Shandong is facing severe power source structural contradiction and tremendous pressure to reduce carbon emissions. Therefore, considering the elimination of coal power overcapacity and introduction of carbon capture and storage technology, this study established an optimization model to identify the low-carbon production pathways for Shandong’s power industry. The results indicated that nuclear power, wind power and complementary energy power would be the overriding clean energy power generation technologies. As to coal power, less than 300 MW-level generating units would be eliminated totally at the end of 2021, then followed by 300 MW-level generating units. And no doubt, 1000 MW-level generating units would become the primary coal-fired power generation technology gradually. Moreover, in 2021, clean energy power would account for about 52.33% of the total installed capacity, surpassing coal power for the first time and undertaking the main task of power generation. Hence, while striving to develop clean energy, those acquired achievements refer to elimination of coal power overcapacity should be further consolidated. Furthermore, when promoting carbon capture and storage technology, governments should not only make a trade-off analysis between its cost and environmental benefit, but also take the provincial actual situation and economic affordability into account.

Suggested Citation

  • Wu, C.B. & Guan, P.B. & Zhong, L.N. & Lv, J. & Hu, X.F. & Huang, G.H. & Li, C.C., 2020. "An optimized low-carbon production planning model for power industry in coal-dependent regions - A case study of Shandong, China," Energy, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:energy:v:192:y:2020:i:c:s036054421932331x
    DOI: 10.1016/j.energy.2019.116636
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