Resilient and sustainable supply: Early warning and adaptive capacity
Reliable statements about future risks are usually difficult to make. We do not know exactly how climate change will affect us in the coming decades, how the population will develop, what the demand for electricity, heating, cooling and mobility will be. How do we deal with these uncertainties when planning future-proof systems?
Resilient systems are characterized by properties that enable them to predict problems in time, maintain essential functions as well as possible under high loads or despite disruptions, and restore normal operation as early as possible. Smart forms of early warning and real-time adaptivity are the focus of research in the department RESIS (Resilient and Smart Infrastructure Systems) at the Institute for Thermal Energy Technology and Safety (ITES), which is part of the CEDIM team. These are topics that have gained in relevance and explosiveness with the energy and mobility transition.
For example, as part of the BMBF CLIENT II project DAMAST (Dams and Induced Seismicity Technologies for Risk Reduction), RESIS, in collaboration with others from CEDIM, has developed a new concept for an AI-based early warning system that provides the dam or hydropower plant operator with short- and medium-term risk forecasts in the context of extreme precipitation events, thus enabling appropriate measures to be taken in good time to maintain safe operations.
Vulnerability and systemic impact
It is clear that a complete qualitative understanding on emerging vulnerabilities, potential systemic impacts of individual events, and endogenous dynamics and interactions, is a necessary prerequisite to effectively invest in the resilience of complex systems alongside robustness of components. In this context, RESIS has developed the FRAMESS (FRamework for Analysing systeMic risks and Exploring Sustainable Solutions) platform, which allows integrative, interdisciplinary and systemic investigations of complex interactions based on different stress scenarios and thus enables the identification of resilience-enhancing measures. This makes it possible to distinguish between low- and high-impact risks on the basis of identified hazards and to develop new metrics that can be operationalized for the planning and operation of resilient as well as sustainable and thus future-proof systems.
Guiding questions using the example of relevant critical infrastructures: uncertainties, complexity and innovation
Can congestion be detected early and even avoided and do new resilience concepts provide safer and more energy-efficient traffic? The development of robust systems for short-term and high-quality traffic forecasts as a basis for adaptive traffic management is very challenging due to many uncertainties.
What forms of preventive energy system planning exist and how can smart and adaptive management look like? The optimization of sustainable and future-proof socio-technical energy systems takes into account very many degrees of freedom and is therefore extremely complex.
Using mathematics, AI and simulations, the RESIS department is developing new decision support systems and technologies for early warning, planning and real-time management in the context of sustainable energy and transportation systems, among others.
Associated institute at KIT: Institute for Thermal Energy Technology and Safety (ITES)
Author: Sadeeb Simon Ottenburger (Juli 2021)