Probabilistic multi-disruption risk analysis in bioenergy parks via physical input–output modeling and analytic hierarchy process

Abstract

Bioenergy parks are integrated energy systems developed based on material and energy synergies among bioenergy and auxiliary plants to increase efficiency and reduce carbon emissions. However, the resulting high interdependence between component units results to a vulnerable network upon capacity disruptions (i.e., plant inoperability). Inoperability of one or more plants within a bioenergy park results in a deviation from an initial network configuration because of failure propagation. The consequences of such disruptions depend upon which component units caused the failure. In this work, a probabilistic multi-disruption risk index is developed to measure the net output change of a bioenergy park based on exogenously-defined plant disruption scenarios, whose probabilities are estimated using the analytic hierarchy process (AHP). This network index is an important measure of the system’s robustness to an array of probabilistic perturbation scenarios. Such risk-based information can be used for developing risk management measures to reduce network vulnerability through increasing system redundancy and diversity. A bioenergy park case study is presented to demonstrate the computation of the multi-disruption risk index.