Design of the energy transition
Designing the energy transition, in terms of ‘comprehensive engineering’ is fundamental for the functioning of our society in the coming decades. However, no comprehensive methods exist to come to appropriate designs of such complex, large/multi-scale, multi-carrier, multi-actor, multi-infrastructure, value-laden systems. Very many socio-technical scenarios are feasible under different sets of assumptions. Different kinds of uncertainties (policy, decision, systemic, normative etc.) limit, on a fundamental level, what comprehensive designs are ‘good’. “It depends”, no longer suffices, we need to be precise and specific about what are promising strategies for the energy transition.
In this research theme, we take a value perspective. Values refer to enduring or long-lasting (plural) beliefs about what is good or desirable. Taking a value perspective therefore offers a comprehensive view of the range of challenges caused by the energy transition. Quantitative methods (e.g. simulations models, text-mining approaches) can help to design for values. These methods are all present at TPM but so far, developed in silos. We aim to integrate these methods in a multi-modelling design framework. This framework, including the supporting tools (hardware, software), enable us to set up, execute, and study testable designs that integrates those methods within a comprehensive view on the energy transition.
The theme leader for this theme is Lukas Schubotz. For more information or questions about this theme, please contact him.
About Lukas Schubotz
Bio: I hold a bachelor’s degree in mathematics and a minor in psychology from Heidelberg University and a master’s degree in innovation science from Utrecht University.
Project description: I am a second-year PhD student at the TPM’s own Energy Transition Lab. My work is at the intersection of agent-based modelling, behavioural and social theory, and energy transition. My efforts focus on the exploration of novel, data-driven modelling techniques like inverse modelling and inverse generative social science to deal with the complexity and chaos of agent-based models. The aim is to produce reusable building blocks of code that can be used for modelling for generative social science.
