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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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