How underground systems can contribute to meet the challenges of energy transition

The paper provides an overview of the several scientific and technical issues and challenges to be addressed for underground storage of carbon dioxide, hydrogen and mixtures of hydrogen and natural gas. The experience gained on underground energy systems and materials is complemented by new competences to adequately respond to the new needs raised by transition from fossil fuels to renewables. The experimental characterization and modeling of geological formations (including geochemical and microbiological issues), fluids and fluid-flow behavior and mutual interactions of all the systems components at the thermodynamic conditions typical of underground systems as well as the assessment and monitoring of safety conditions of surface facilities and infrastructures require a deeply integrated teamwork and fit-for-purpose laboratories to support theoretical research. The group dealing with large-scale underground energy storage systems of Politecnico di Torino has joined forces with the researchers of the Center for Sustainable Future Technologies of the Italian Institute of Technology, also based in Torino, to meet these new challenges of the energy transition era, and evidence of the ongoing investigations is provided in this paper

The danger of mapping risk from multiple natural hazards

In recent decades, society has been greatly affected by natural disasters (e.g. floods, droughts, earthquakes), losses and effects caused by these disasters have been increasing. Conventionally, risk assessment focuses on individual hazards, but the importance of addressing multiple hazards is now recognised. Two approaches exist to assess risk from multiple-hazards; the risk index (addressing hazards, and the exposure and vulnerability of people or property at risk) and the mathematical statistics method (which integrates observations of past losses attributed to each hazard type). These approaches have not previously been compared. Our application of both to China clearly illustrates their inconsistency. For example, from 31 Chinese provinces assessed for multi-hazard risk, Gansu and Sichuan provinces are at low risk of life loss with the risk index approach, but high risk using the mathematical statistics approach. Similarly, Tibet is identified as being at almost the highest risk of economic loss using the risk index, but lowest risk under the mathematical statistics approach. Such inconsistency should be recognised if risk is to be managed effectively, whilst the practice of multi-hazard risk assessment needs to incorporate the relative advantages of both approaches

A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation

The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach

ISSN:0921-030XISSN:1573-084