Future Resilient Systems (FRS)

The Future Resilient Systems program (FRS) develops a framework, concepts, and tools to make interconnected infrastructure systems more robust and resilient. FRS is the second research program under the Singapore-ETH Centre (SEC), established by ETH Zurich and Singapore's National Research Foundation (NRF). PSI’s Technology Assessment group is involved in research activities of cluster 2 on “Energy Systems and Comparative Assessment”. Specific goals include the investigation of accident risks due to technical failures and natural hazards of energy systems, and the development of methods to quantitatively assess security of supply and resilience aspects.

Description:
Energy systems are critical, highly inter-connected infrastructures, forming the backbone of modern society. Consequently, energy is a prerequisite for most goods and services produced, and it also facilitates productivity, trade and economic growth. Disruptions and breakdowns of the energy supply may cause serious economic, social and political consequences. This indicates the urgency and importance to build more resilient energy systems to mitigate potential impacts of natural disasters, technical failures and intentional attacks. Within comparative assessment of energy systems, the following tasks and activities are carried out: (1) performing comparative assessment of accident risks across a broad range of current and future energy supply chains, based on available historical experience and probabilistic approaches, complemented by hybrid approaches including use of expert judgment; (2) establishing a comprehensive set of resilience indicators with emphasis on security of supply; and (3) developing tools to support decision-making and to contribute to improving possibly conflicting energy planning processes.

• Cinelli, M., Spada, M., Zhang, Y., Kim, W., Burgherr, P. 2018. MCDA Index Tool. An interactive software to develop indices and rankings. Link to the MCDA Index tool
• Linden, D., Cinelli, M., Spada, M., Becker, W. and Burgherr, P. (2018). Composite Indicator Analysis and Optimization (CIAO) Tool. Link to the CIAO tool