Michael Francis D. Benjamin

A methodology for criticality analysis in integrated energy systems

Integrated energy systems (IES) such as polygeneration plants and bioenergy-based industrial symbiosis (BBIS) networks offer the prospect of increased efficiency and reduced carbon emissions. However, these highly-integrated systems are also characterized by the strong interdependence among component units. This interdependency results in the risk of propagation of cascading failures within such networks, where disturbances in the operation of one component results in ripple effects that affect the other units in the system. In this work, a novel criticality index is proposed to quantify the effects of a component unit’s failure to run at full capacity within an IES. This index is defined as the ratio of the fractional change in the net output to the fractional change in capacity of the component causing the failure. The component units in the entire system can then be ranked based on this index. Such risk-based information can thus be used as an important input for developing risk mitigation measures and policies. Without this information, risk management based only on network topology could result to counterintuitive results. A simple polygeneration plant and two BBIS case studies are presented to demonstrate the computation of the criticality index.

Analyzing the disruption resilience of bioenergy parks using dynamic inoperability input–output modeling

Bioenergy parks are low-carbon industrial symbiosis networks that are comprised of biomass processing plants. However, such highly integrated energy systems are inherently vulnerable to capacity disruptions. The strong interdependencies among component plants in a bioenergy park decrease system resilience due to cascading failure effect. The consequences of such disruptions are even greater if the critical components are damaged. Resilience is defined as the ability of an energy system to withstand a disruption and subsequently recover to its normal state. In this work, a disruption resilience framework is developed to analyze the resilience of bioenergy parks against an array of capacity disruption scenarios. This framework is derived from dynamic inoperability input–output modeling previously used in economic and critical infrastructure systems. A microalgal multi-functional bioenergy system case study is presented to demonstrate the applicability of the resilience framework. The example shows that the resilience of a bioenergy park is influenced by both the recovery time of component plants and their degree of connectivity within the network; such insights can be used for planning more disruption-resilient bioenergy parks.

P-graph approach to criticality analysis in integrated bioenergy systems

The use of integrated bioenergy systems (IBS) is a prospective solution to address the emergent global demand for clean energy. The sustainability of IBS compared to stand-alone biomass processing facilities is achieved through integration of process units or component plants via their bioenergy products, by-products, wastes, and common utilities. However, such increased component interdependency makes the resulting integrated energy system vulnerable to capacity disruptions. IBS in particular are vulnerable to climate change-induced events (e.g., drought) that reduce the availability of biomass feedstocks in bioenergy production. Cascading failure due to such supply-side disruptive event is an inherent risk in IBS and may pose a barrier to the commercial-scale adoption of such systems. A previous study developed a risk-based criticality index to quantify the effect of a component’s disruption within integrated energy systems. This index is used to rank the component’s relative risk in the network based on the ripple effects of its disruption. In this work, a novel P-graph approach is proposed as an alternative methodology for criticality analysis of component units or plants in an IBS. This risk-based metric can be used for developing risk management polices to protect critical facilities, thereby increasing the robustness of IBS against disruptions. Two case studies on determining the criticality index of process units in an integrated biorefinery and component plants in a bioenergy park are used to demonstrate the effectiveness of this method.