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Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China

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  • Yin, J.N.
  • Huang, G.H.
  • Xie, Y.L.
  • An, Y.K.

Abstract

Inter-regional electricity transmission inevitably brings about unbalanced carbon emission and air pollution among different regions. It is vital to compensate for the economic and environmental loss of power output region by taking the carbon subsidy strategy into account. In this study, a risk explicit interval multistage stochastic programming model considering inter-regional carbon subsidy is developed to support an optimal energy system planning in Inner Mongolia, China. This model can not only deal with uncertainties embedded in the complex energy system described as interval values and random variables but also reflect dynamic linkage between multiple stages over a time series. It advanced the existing optimization methods by introducing the cost-risk tradeoff information based on the risk preferences of decision-makers. The obtained crisp solutions are more feasible, effective and optimum for the practical policymaking process. Different scenarios associated with carbon subsidy policy implementation, renewable energy share adjustment and carbon emission reduction strategy are employed to project the optimal power generation scheme, net system cost, installed electricity capacity, carbon intensity and pollution emission in the future. The results indicate that the amount of exported electricity will keep increasing, and the carbon subsidy strategy will prominently promote the economic growth of the electric power sector by 3.05%. The further power supply pattern is going to transform from fossil fuel to renewable energy, by which wind and solar power will occupy around 44% of total power generation capacity in West Inner Mongolia. Raising the share of renewable energy will be the most effective approach to adjust the power generation scheme; meanwhile, reducing carbon intensity will significantly contribute to enlarge green energy capacity, enhance low-carbon emission and mitigate air pollution. These findings will help decision-makers gain insights into a more scientific energy system planning under various uncertainties.

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  • Yin, J.N. & Huang, G.H. & Xie, Y.L. & An, Y.K., 2021. "Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    • Handle: RePEc:eee:rensus:v:135:y:2021:i:c:s1364032120307267
    DOI: 10.1016/j.rser.2020.110439
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    as
    1. Zhu, Y. & Li, Y.P. & Huang, G.H., 2013. "Planning carbon emission trading for Beijing's electric power systems under dual uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 113-128.
    2. Chen, F. & Huang, G.H. & Fan, Y.R. & Chen, J.P., 2017. "A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning," Applied Energy, Elsevier, vol. 187(C), pages 291-309.
    3. Ming, Zeng & Lilin, Peng & Qiannan, Fan & Yingjie, Zhang, 2016. "Trans-regional electricity transmission in China: Status, issues and strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 572-583.
    4. Ji, Ling & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao & Song, Yi-Hang, 2017. "Explicit cost-risk tradeoff for renewable portfolio standard constrained regional power system expansion: A case study of Guangdong Province, China," Energy, Elsevier, vol. 131(C), pages 125-136.
    5. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "Explicit cost-risk tradeoff for optimal energy management in CCHP microgrid system under fuzzy-risk preferences," Energy Economics, Elsevier, vol. 70(C), pages 525-535.
    6. Xie, Y.L. & Xia, D.H. & Ji, L. & Zhou, W.N. & Huang, G.H., 2017. "An inexact cost-risk balanced model for regional energy structure adjustment management and resources environmental effect analysis-a case study of Shandong province, China," Energy, Elsevier, vol. 126(C), pages 374-391.
    7. Wu, C.B. & Huang, G.H. & Li, W. & Xie, Y.L. & Xu, Y., 2015. "Multistage stochastic inexact chance-constraint programming for an integrated biomass-municipal solid waste power supply management under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1244-1254.
    8. Xin, Li & Feng, Kuishuang & Siu, Yim Ling & Hubacek, Klaus, 2015. "Challenges faced when energy meets water: CO2 and water implications of power generation in inner Mongolia of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 419-430.
    9. Lin, Boqiang & Wu, Wei, 2017. "Cost of long distance electricity transmission in China," Energy Policy, Elsevier, vol. 109(C), pages 132-140.
    10. Chen, Siyuan & Liu, Pei & Li, Zheng, 2019. "Multi-regional power generation expansion planning with air pollutants emission constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 382-394.
    11. Zhai, Mengyu & Huang, Guohe & Liu, Lirong & Zheng, Boyue & Guan, Yuru, 2020. "Inter-regional carbon flows embodied in electricity transmission: network simulation for energy-carbon nexus," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    12. Yao, Yao & Huang, Gordon & An, Chunjiang & Chen, Xiujuan & Zhang, Peng & Xin, Xiaying & Jian Shen, & Agnew, Joy, 2020. "Anaerobic digestion of livestock manure in cold regions: Technological advancements and global impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    13. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
    14. Chen, C. & Li, Y.P. & Huang, G.H., 2013. "An inexact robust optimization method for supporting carbon dioxide emissions management in regional electric-power systems," Energy Economics, Elsevier, vol. 40(C), pages 441-456.
    15. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    16. Zeng, Bo & Zeng, Ming & Xue, Song & Cheng, Min & Wang, Yuejin & Feng, Junjie, 2014. "Overall review of wind power development in Inner Mongolia: Status quo, barriers and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 614-624.
    17. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
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    3. Wang, Ni & Verzijlbergh, Remco A. & Heijnen, Petra W. & Herder, Paulien M., 2023. "Incorporating indirect costs into energy system optimization models: Application to the Dutch national program Regional Energy Strategies," Energy, Elsevier, vol. 276(C).
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    5. Zhang, Xiaoyue & Huang, Guohe & Xie, Yulei & Liu, Lirong & Song, Tangnyu, 2022. "A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China," Applied Energy, Elsevier, vol. 311(C).
    6. Ma, Ning & Fan, Lurong, 2023. "Double recovery strategy of carbon for coal-to-power based on a multi-energy system with tradable green certificates," Energy, Elsevier, vol. 273(C).
    7. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    8. Jixian Cui & Chenghao Liao & Ling Ji & Yulei Xie & Yangping Yu & Jianguang Yin, 2021. "A Short-Term Hybrid Energy System Robust Optimization Model for Regional Electric-Power Capacity Development Planning under Different Pollutant Control Pressures," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    9. Govindan, Kannan, 2023. "Pathways to low carbon energy transition through multi criteria assessment of offshore wind energy barriers," Technological Forecasting and Social Change, Elsevier, vol. 187(C).

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