An optimization model to provide electric power systems’ net zero-carbon emission pathways considering diverse measures under double-side randomness and vagueness

J. Liu, J.W. Li, X. Li, Y.P. Li, P.P. Gao and L. Jin

Renewable Energy, 2026, vol. 256, issue PI

Abstract: This study develops an interval fractional double-sides stochastic-fuzzy net zero-carbon emission electric power system planning model (IFDS-ZCEM) to detect the optimal decarbonization pathways. Extending previous studies, the model's objective is improved from total cost minimization to total carbon emission intensity minimization, considering more aspects of constraints; introducing diverse measures to synergistically mitigate carbon emissions in high emission regions; handling inherent interactions of multiple random and vague information to control decision-making risks. It is applied to Fujian Province (in China), where the system's intensity was 9.09 kg/CNY in 2023. The model provides potential planning schemes regarding to decarbonization under 25 uncertain scenarios during 2026–2060. Main findings are: The total intensity would obviously vary with the changed scenarios; revealing the significance of uncertainties' effects. The annual intensity would decrease from {[[3.56, 3.91], [6.13, 7.87]], [[4.37, 5.91], [9.45, 10.66]]} kg/CNY to 0 (carbon neutrality in 2060); disclosing the importance of adopting diverse measures. The system benefit, water utilization and land requirement would annually increase by [-0.18, 0.83] %, decrease by [0.88, 1.09] % and increase by [1.36, 1.98] %; revealing the model's effectiveness in balancing multiple aspects. Quantified low carbon energy transitions, production technique improvements, negative-emission technology adoptions and economic penalty actions would support policy making.

Keywords: Carbon gas/air capture; Carbon sink; Energy hybrid; Intensity minimization; Net-zero carbon emissions; Policy recommendation (search for similar items in EconPapers)
Date: 2026
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:256:y:2026:i:pi:s0960148125023018

DOI: 10.1016/j.renene.2025.124637

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