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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

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  • Zhang, Xiaoyue
  • Huang, Guohe
  • Xie, Yulei
  • Liu, Lirong
  • Song, Tangnyu

Abstract

In this study, a coupled non-deterministic optimization and mixed-level factorial analysis model (NOMFA) is proposed for supporting power generation expansion planning. By integrating interval-parameter programming, multistage stochastic programming and mixed-level factorial analysis within a general system optimization framework, this model can not only help determine the optimized schemes for power generation and capacity expansion under various uncertainties, but also reflect dynamic variations of system conditions; moreover, it can take into account the effects of external interferences and their interactions on the system outputs in the decision-making process. A case study of Jing-Jin-Ji (JJJ) metropolitan region is provided to demonstrate the effectiveness of the proposed approach. The results indicate that although renewable technologies are becoming increasingly significant, electricity generated by fossil fuels would still dominateJJJ’s power systems during the planning horizon. It is also found that the import price of electricity is the most influential factor on both total system cost and total CO2 emissions; meanwhile, the interaction between the import price of electricity and the price of gas impacts CO2 emissions to a certain extent. It is expected that the modeling results will provide solid bases for formulating power generation expansion plans for JJJ Metropolitan Region.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922000952
    DOI: 10.1016/j.apenergy.2022.118621
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    References listed on IDEAS

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    1. Zhang, Xiaoyue & Huang, Guohe & Liu, Lirong & Li, Kailong, 2022. "Development of a stochastic multistage lifecycle programming model for electric power system planning – A case study for the Province of Saskatchewan, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Zhou, Yang & Huang, Guo H. & Yang, Boting, 2013. "Water resources management under multi-parameter interactions: A factorial multi-stage stochastic programming approach," Omega, Elsevier, vol. 41(3), pages 559-573.
    3. Xie, Y.L. & Li, Y.P. & Huang, G.H. & Li, Y.F., 2010. "An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty," Energy, Elsevier, vol. 35(12), pages 4627-4644.
    4. Handayani, Kamia & Filatova, Tatiana & Krozer, Yoram & Anugrah, Pinto, 2020. "Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion," Applied Energy, Elsevier, vol. 262(C).
    5. 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.
    6. Wang, S. & Huang, G.H., 2015. "A multi-level Taguchi-factorial two-stage stochastic programming approach for characterization of parameter uncertainties and their interactions: An application to water resources management," European Journal of Operational Research, Elsevier, vol. 240(2), pages 572-581.
    7. Roberts, Justo José & Marotta Cassula, Agnelo & Silveira, José Luz & da Costa Bortoni, Edson & Mendiburu, Andrés Z., 2018. "Robust multi-objective optimization of a renewable based hybrid power system," Applied Energy, Elsevier, vol. 223(C), pages 52-68.
    8. Li, Y.F. & Huang, G.H. & Li, Y.P. & Xu, Y. & Chen, W.T., 2010. "Regional-scale electric power system planning under uncertainty--A multistage interval-stochastic integer linear programming approach," Energy Policy, Elsevier, vol. 38(1), pages 475-490, January.
    9. 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).
    10. Huang, G. H., 1998. "A hybrid inexact-stochastic water management model," European Journal of Operational Research, Elsevier, vol. 107(1), pages 137-158, May.
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