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Development of a stochastic multistage lifecycle programming model for electric power system planning – A case study for the Province of Saskatchewan, Canada

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  • Zhang, Xiaoyue
  • Huang, Guohe
  • Liu, Lirong
  • Li, Kailong

Abstract

In this study, a stochastic multistage lifecycle programming model (SMLP) for supporting the management of power systems and the associated economic and environmental risks is proposed. A two-fold objective is pursued. First, information on lifecycle GHG emissions from 10 forms of power generation technologies is comprehensively reviewed and characterized. Second, optimized pathways for the electricity system transition with specific consideration of lifecycle GHG emissions and lifecycle costs are identified. This work presents methodological advances in integrating the lifecycle concept with a power system optimization model, which can enhance the robustness of the resulting decision support. A case study for the Province of Saskatchewan is undertaken, where various uncertainties and risks are quantified and trade-offs among a number of system objectives/criteria are analyzed. According to the review, the lowest average emission per unit power generation is hydropower (12.8 g CO2-eq/kWh), closely followed by offshore wind power (14.6 g CO2-eq/kWh) and on-shore wind power (15.3 g CO2-eq/kWh). According to the modeling results, on-shore wind power is likely to become the dominant form of power generation in Saskatchewan by the end of the planning horizon; import power would play a big part in securing the province's electricity supply in the future. It is expected that the modeling results can help support the formulation of regional energy and relevant socio-economic and environmental policies.

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  • 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).
    • Handle: RePEc:eee:rensus:v:158:y:2022:i:c:s136403212101306x
    DOI: 10.1016/j.rser.2021.112044
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    1. 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).

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