A correct counting of greenhouse gas emissions, mainly CO2, is crucial in energy system optimization models, widely used to assess the effectiveness of decarbonization strategies. Sectorial emissions are typically computed at each modeled time period using commodity-specific factors based on a given static fuel composition. For fuels generated by combining fossil and low-carbon commodities, however, the share of the low-carbon component may change over time. Under certain fractions, the blending with hydrogen, biofuels, and synfuels, constitutes a viable decarbonization alternative, without the need for retrofitting the existing infrastructure. This work proposes a dynamic accounting method for the avoided emissions thanks to blending low-carbon fuels with fossil fuels as an alternative to the traditional static evaluation in energy system models. The proposed methodology is based on the application of negative process-specific factors to account for avoided emissions. This new scheme is integrated and tested in the TEMOA-Italy open model. The dynamic methodology is first compared to the static one, showing that the latter provides an overestimation of the emission levels. Then, it is proven to work properly in a very stringent decarbonization scenario for a large range of blending fractions. Finally, the results of the decarbonization scenario are deeply analyzed to provide valuable insights for future policy-relevant assessments. Even if the high penetration of blended low-carbon fuels in the energy mix quantitatively differs from the evolution foreseen by national, European, and global energy policies, such penetration reflects the crucial role of hydrogen, biofuels, and synfuels depicted in those policies, to fulfill the intermediate and long-term emission reduction targets.

A dynamic accounting method for CO2 emissions to assess the penetration of low-carbon fuels: application to the TEMOA-Italy energy system optimization model

Lerede D.;Nicoli M.;
2023-01-01

Abstract

A correct counting of greenhouse gas emissions, mainly CO2, is crucial in energy system optimization models, widely used to assess the effectiveness of decarbonization strategies. Sectorial emissions are typically computed at each modeled time period using commodity-specific factors based on a given static fuel composition. For fuels generated by combining fossil and low-carbon commodities, however, the share of the low-carbon component may change over time. Under certain fractions, the blending with hydrogen, biofuels, and synfuels, constitutes a viable decarbonization alternative, without the need for retrofitting the existing infrastructure. This work proposes a dynamic accounting method for the avoided emissions thanks to blending low-carbon fuels with fossil fuels as an alternative to the traditional static evaluation in energy system models. The proposed methodology is based on the application of negative process-specific factors to account for avoided emissions. This new scheme is integrated and tested in the TEMOA-Italy open model. The dynamic methodology is first compared to the static one, showing that the latter provides an overestimation of the emission levels. Then, it is proven to work properly in a very stringent decarbonization scenario for a large range of blending fractions. Finally, the results of the decarbonization scenario are deeply analyzed to provide valuable insights for future policy-relevant assessments. Even if the high penetration of blended low-carbon fuels in the energy mix quantitatively differs from the evolution foreseen by national, European, and global energy policies, such penetration reflects the crucial role of hydrogen, biofuels, and synfuels depicted in those policies, to fulfill the intermediate and long-term emission reduction targets.
2023
352
1
22
https://doi.org/10.1016/j.apenergy.2023.121951
Biofuels; CO2 emissions; Energy system optimization models; Hydrogen; Synthetic fuels
Colucci G.; Lerede D.; Nicoli M.; Savoldi L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2031427
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