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A chance-constrained small modular reactor siting model -- a case study for the Province of Saskatchewan, Canada

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  • Luo, Bin
  • Huang, Guohe
  • Chen, Jiapei
  • Zhang, Xiaoyue
  • Zhao, Kai

Abstract

In this study, a chance-constrained small modular reactor siting (CCSS) model has been first proposed for planning regional electric power systems in the Province of Saskatchewan under the pressure of greenhouse gas emission mitigation. Through incorporating three programming methods (interval linear programming, chance-constrained programming, and mixed-integer programming) in an optimization framework, the CCSS model could effectively deal with multiple uncertainties expressed as probability distribution and intervals in constraints and objectives. Since locations of retired coal-fired power stations have been proposed as new sites for small modular reactor (SMR), this model could provide the construction planning for SMR in terms of its sizes and sites. Various solutions to power generation and capacity expansion with different risk levels were obtained under the objective of minimal system costs. Results are helpful for identifying optimized strategies to increase the proportion of renewable energy under emission constraints. In addition, model without introducing the technology of SMR has been comprehensively compared with the CCSS model to recognise the effects of SMR on system costs and greenhouse gas emissions. Results indicate that the CCSS model is effective for supporting long-term clean and renewable energy utilization as well as emission reduction policy formulation in Saskatchewan.

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  • Luo, Bin & Huang, Guohe & Chen, Jiapei & Zhang, Xiaoyue & Zhao, Kai, 2021. "A chance-constrained small modular reactor siting model -- a case study for the Province of Saskatchewan, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:rensus:v:148:y:2021:i:c:s1364032121006079
    DOI: 10.1016/j.rser.2021.111320
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    References listed on IDEAS

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    1. Luo, Bin & Huang, Guohe & Chen, Leian & Liu, Lirong & Zhao, Kai, 2024. "Factorial optimization-driven input-output analysis for socio-economic and environmental effects of GHG emission reduction in electric power systems – A Canadian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).

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