Analyzing the Disruption Resilience of Microalgal Multi-functional Bioenergy Systems using Dynamic Inoperability Input-output Modeling

Abstract

Bioenergy parks are low-carbon industrial symbiosis (IS) networks that are also characterized as having a higher resource efficiency and economic sustainability compared to stand-alone bioenergy plants. A microalgal multi-functional bioenergy system (MMBS) is an example of such network, which is specifically developed for the sustainability of algal biofuels. However, such highly integrated energy system is inherently vulnerable to capacity disruptions resulting in a less resilient network. The strong interdependence between component plants in a bioenergy park decreases system resilience due to cascading failure effect. The consequence of such disruption is even greater if the critical components are damaged. Resilience is defined in this work as the ability of an energy system to withstand a disruption and be able to recover to normal operating conditions. Most risk analysis focus on the vulnerability or robustness (i.e., static resilience) of bioenergy parks and lack significant discussions on the recovery rates aspect (i.e., dynamic resilience). In this work, a disruption resilience framework is developed to analyze the resilience of bioenergy parks against an array of capacity disruption scenarios. This study is primarily focused on the effect of single-plant disruption scenarios. The proposed framework is derived from the concepts of dynamic inoperability input-output modelling (DIIM) used in economic systems. The method shows that the resilience of the bioenergy park is influenced by the recovery time of bioenergy plants and their degree of connectivity within the network. The insights from this work can be used for planning and developing more disruption-resilient bioenergy parks. An MMBS case study is presented to demonstrate the applicability of the resilience framework.