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A multi-period superstructure optimisation model for the optimal planning of China's power sector considering carbon dioxide mitigation

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  • Zhang, Dongjie
  • Ma, Linwei
  • Liu, Pei
  • Zhang, Lili
  • Li, Zheng

Abstract

Power sector is the largest CO2 emitter in China. To mitigate CO2 emissions for the power sector is a tough task, which requires implementation of targeted carbon mitigation policies. There might be multiple forms for carbon mitigation policies and it is still unclear which one is the best for China. Applying a superstructure optimisation model for optimal planning of China's power sector built by the authors previously, which was based on real-life plants composition data of China's power sector in 2009, and could incorporate all possible actions of the power sector, including plants construction, decommission, and application of carbon capture and sequestration (CCS) on coal-fuelled plants, the implementation effects of three carbon mitigation policies were studied quantitatively, achieving a conclusion that the so-called “Surplus-Punishment & Deficit-Award” carbon tax policy is the best from the viewpoint of increasing CO2 reduction effect and also reducing the accumulated total cost. Based on this conclusion, the corresponding relationships between CO2 reduction objectives (including the accumulated total emissions reduction by the objective year and the annual emissions reduction in the objective year) were presented in detail. This work provides both directional and quantitative suggestions for China to make carbon mitigation policies in the future.

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  • Zhang, Dongjie & Ma, Linwei & Liu, Pei & Zhang, Lili & Li, Zheng, 2012. "A multi-period superstructure optimisation model for the optimal planning of China's power sector considering carbon dioxide mitigation," Energy Policy, Elsevier, vol. 41(C), pages 173-183.
    • Handle: RePEc:eee:enepol:v:41:y:2012:i:c:p:173-183
    DOI: 10.1016/j.enpol.2011.10.031
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    References listed on IDEAS

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    1. Chen, Hao & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2016. "A multi-period power generation planning model incorporating the non-carbon external costs: A case study of China," Applied Energy, Elsevier, vol. 183(C), pages 1333-1345.
    2. 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).
    3. Xiao, Jin & Li, Guohao & Xie, Ling & Wang, Shouyang & Yu, Lean, 2021. "Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission," Energy Policy, Elsevier, vol. 150(C).
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    5. Liu, Xi & Du, Huibin & Brown, Marilyn A. & Zuo, Jian & Zhang, Ning & Rong, Qian & Mao, Guozhu, 2018. "Low-carbon technology diffusion in the decarbonization of the power sector: Policy implications," Energy Policy, Elsevier, vol. 116(C), pages 344-356.
    6. Eto, R. & Murata, A. & Uchiyama, Y. & Okajima, K., 2013. "Co-benefits of including CCS projects in the CDM in India's power sector," Energy Policy, Elsevier, vol. 58(C), pages 260-268.
    7. Yuan, Jiahai & Xu, Yan & Kang, Junjie & Zhang, Xingping & Hu, Zheng, 2014. "Nonlinear integrated resource strategic planning model and case study in China's power sector planning," Energy, Elsevier, vol. 67(C), pages 27-40.
    8. Hui, Jingxuan & Cai, Wenjia & Wang, Can & Ye, Minhua, 2017. "Analyzing the penetration barriers of clean generation technologies in China’s power sector using a multi-region optimization model," Applied Energy, Elsevier, vol. 185(P2), pages 1809-1820.
    9. Almansoori, Ali & Betancourt-Torcat, Alberto, 2015. "Design optimization model for the integration of renewable and nuclear energy in the United Arab Emirates’ power system," Applied Energy, Elsevier, vol. 148(C), pages 234-251.
    10. Cristóbal, Jorge & Guillén-Gosálbez, Gonzalo & Jiménez, Laureano & Irabien, Angel, 2012. "MINLP model for optimizing electricity production from coal-fired power plants considering carbon management," Energy Policy, Elsevier, vol. 51(C), pages 493-501.
    11. Wang, Can & Ye, Minhua & Cai, Wenjia & Chen, Jining, 2014. "The value of a clear, long-term climate policy agenda: A case study of China’s power sector using a multi-region optimization model," Applied Energy, Elsevier, vol. 125(C), pages 276-288.
    12. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    13. Maitri Verma & Alok Kumar Verma & A. K. Misra, 2021. "Mathematical modeling and optimal control of carbon dioxide emissions from energy sector," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13919-13944, September.
    14. Zhang, Xiaodong & Duncan, Ian J. & Huang, Gordon & Li, Gongchen, 2014. "Identification of management strategies for CO2 capture and sequestration under uncertainty through inexact modeling," Applied Energy, Elsevier, vol. 113(C), pages 310-317.
    15. Li, Weiqi & Dai, Yaping & Ma, Linwei & Hao, Han & Lu, Haiyan & Albinson, Rosemary & Li, Zheng, 2015. "Oil-saving pathways until 2030 for road freight transportation in China based on a cost-optimization model," Energy, Elsevier, vol. 86(C), pages 369-384.

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