Optimization and analysis of a bioelectricity generation supply chain under routine and disruptive uncertainty and carbon mitigation policies

Mahsa Saghaei,Mohammad Dehghanimadvar,Mohammad Hossein Ahmadi,Hamed Soleimani

doi:10.1002/ese3.716

Abstract

AbstractIncreasing greenhouse gas emissions and negative environmental consequences have raised worldwide attention to ecological issues. The development of carbon regulations (CRs) beside carbon capture and storage (CCS) systems is part of carbon mitigation policies (CMPs), which are following in recent years to control and manage carbon liberation. Along with environemtal policies, the utilization of renewable energy resources have been promoted significantly. However, the economic opportunities for renewable energy development considering CMPs have not addressed extensively. In this study, a stochastic mathematical programming model has been presented to minimize cost and downside risk (DSR) of the bioelectricity generation supply chain considering the pre‐ and postdisaster conditions. The role of several CMPs on the economic behavior of the system has been analyzed by investigating the potential uncertainties on material availability, material quality, and consumer demand. To consider disruption effects, the postdisaster stage has been classified into several substages including damage, recovery, and back to the sustainability stages. Mississippi State after the Katrina Hurricane is addressed as a case study to examine the performance of the proposed model. The results demonstrated that the occurrence of disruptive uncertainties creates 8,978,502 $, 8,864,335 $ and 8,884,055 $ as the DSR, under carbon tax policy (CTP), carbon offset policy (COP), and CCS, respectively. The effect of disruptive scenario with a 15% reduction of resource has led to the greatest postdisaster supply chain costs in comparison with other scenarios. Although the financial analysis showed CTP has the greatest DSR after the occurrence of disaster, this policy has the most investment attractions, as well as COP, with the internal rate of return (IRR) of 9%. While implementing the CCS policy with the IRR of 2% creates 7% missed opportunity costs compared with other CMPs.

Highlights

Increasing global carbon emissions and the negative environmental consequences have led to greater adoption and commitment to environmental policies to reduce CO2 emissions
This study presents a two-stage stochastic programming model to minimize the costs of a bioenergy generation supply chain under uncertainty and several carbon mitigation policies (CMPs)
We have considered the performance of a natural disaster (Katrina hurricane), as the start point of disruptions, in a real-world case study in the Mississippi State

Summary

Introduction

Increasing global carbon emissions and the negative environmental consequences have led to greater adoption and commitment to environmental policies to reduce CO2 emissions. The rate of carbon emission during woody biomass combustion is significantly higher than the rate of carbon absorption over its growth cycle. This phenomenon, on a large scale, can affect the balance between the amounts of released and absorbed carbon in the atmosphere. This challenge causes a barrier for utilizing the forest biomass for energy production.[11,12]

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Results

Conclusion