Assessing the Reliability of Integrated Bioenergy Systems to Capacity Disruptions via Monte Carlo Simulation

Developing integrated bioenergy systems (IBS) is important for achieving a sustainable energy source, maximizing biomass waste, and reducing a country’s heavy reliance on imported fuels. IBS are implemented via integrated biorefineries (i.e., plant level) or bioenergy parks (i.e., industrial park level). Both systems are already proven to operate efficiently by utilizing possible synergies between process units or component plants through material and energy integration. However, the drawback of interconnected systems is the ripple and amplification effect of disruption when one component experiences failure. For IBS, capacity disruptions can result from biomass feedstock supply chain bottlenecks or internal process inoperability. These disruption scenarios can cause partial system inoperability or even network failure. In this work, a risk analysis and Monte Carlo simulation (MCS) based framework to assess and determine the reliability of IBS against variable capacity disruptions was developed. The MCS method is known to determine failure rates by introducing a probabilistic input to a model. Two case studies are considered in this work to illustrate the applicability of the model to a range of IBS networks. Results show that the proposed method is able to determine capacity and stream reliability against multiple disruption scenarios. This work contributes to designing and implementing robust IBS networks at any scale.