Biomass has been considered one of the most promising feedstock as an alternative primary source to substitute traditional fuels in the transport sectors. However, both biomass intrinsic variability and the fact that several conversion technologies have not reached full maturity make the economic assessment of the production system performance rather difficult. This contribution proposes a quantitative approach for the strategic design and optimisation of biomass-based supply chains under uncertainty on the technology conversion efficiency. The methodology is based on regret theory and is applied to quantify both risk and regret with respect to benchmark economic outputs. A Mixed Integer Linear Programming approach is employed to represent and optimise the profitability of a multi-echelon, multi-period and spatially explicit biomass-based supply chain for bioethanol and bioelectricity production where several conversion technologies are simultaneously taken into account. The entire supply chain is optimised in terms of maximum industrial financial result, while constraining the expected values for risk and regret by placing a bound on them through the risk aversion attitude by investors. Results demonstrate how the approach can help policy-makers and investors assessing technological options according to their risk aversion attitude.

Assessing technological options in biomass-based energy supply chains through a quantitative methodology for risk and regret evaluation

d'AMORE, FEDERICO;BEZZO, FABRIZIO
2017

Abstract

Biomass has been considered one of the most promising feedstock as an alternative primary source to substitute traditional fuels in the transport sectors. However, both biomass intrinsic variability and the fact that several conversion technologies have not reached full maturity make the economic assessment of the production system performance rather difficult. This contribution proposes a quantitative approach for the strategic design and optimisation of biomass-based supply chains under uncertainty on the technology conversion efficiency. The methodology is based on regret theory and is applied to quantify both risk and regret with respect to benchmark economic outputs. A Mixed Integer Linear Programming approach is employed to represent and optimise the profitability of a multi-echelon, multi-period and spatially explicit biomass-based supply chain for bioethanol and bioelectricity production where several conversion technologies are simultaneously taken into account. The entire supply chain is optimised in terms of maximum industrial financial result, while constraining the expected values for risk and regret by placing a bound on them through the risk aversion attitude by investors. Results demonstrate how the approach can help policy-makers and investors assessing technological options according to their risk aversion attitude.
2017
Computer-Aided Chemical Engineering, Proceedings of the 27th European Symposium on Computer Aided Process Engineering
978-0-444-63965-3
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3241775
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