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

Energy Science & Engineering

Mohammad Dehghanimadvar¹  Mohammad Hossein Ahmadi²  Hamed Soleimani³  Mahsa Saghaei⁴ 

[1] Department of Renewable Energy and Environment Faculty of New Sciences and Technologies University of Tehran Tehran Iran;Faculty of Mechanical Engineering Shahrood University of Technology Shahrood Iran;School of Mathematics and Statistics University of Melbourne Parkville VIC Australia;Young Researchers and Elites Club Qazvin Branch Islamic Azad University (IAU) Qazvin Iran;

关键词: biomass;    carbon regulation;    CCS;    disruption;    optimization;    Stochastic programming;   

DOI  :  10.1002/ese3.716

来源: DOAJ

【 摘 要 】

Abstract Increasing 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 1 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.

【 授权许可】

Unknown