he question of using artificial intelligence (AI) and machine learning (ML) for smart grid development has become part of the public debate on the energy transition (“Energiewende”). However, the focus of technology assessment (TA) and social science research has long been on the intended transformation of energy consumers into “prosumers” (consumers + producers) through AI-/ML-based measures. The dimension of grid operation, both at the supra-regional level of transmission system operation (TSO) and the regional level of Distribution System Operation (DSO), has been rather unspecified – despite the fundamental reconfigurations and related implications of the envisioned transition within and between these domains.

This dissertation addresses these so far underexplored dimensions by examining the challenges arising from the implementation of AI- and ML-assisted practices. Specifically, it investigates the implications for established standards of decision-making comprehensibility and traceability, the increased complexity and need for coordination between the TSO and DSO levels, as well as the necessity of understanding and managing behavior profiles of the growing number of actors as matters of trust for grid operation. To underscore the significance of trust as a crucial factor in the envisioned changes to the energy grid as socio-technical configuration, this dissertation draws on literature from energy policy, technology assessment, and related disciplines. The conceptual framework includes perspectives from technology assessment, science technology studies, and the sociology of expectations.

Methodologically, the research adopts a cumulative approach against the backdrop of the Socio-technical Integration Research (STIR) framework with its decision protocols, qualitative expert interviews, and document analysis. The empirical investigation focuses on the future energy systems research conducted at the Karlsruhe Institute of Technology, as well as on energy production/demand foresight, flexibility modeling, and responsive system design undertaken by operators at the TSO and DSO levels.

This cumulative dissertation project emphasizes the importance of considering trust in the transformation of the energy production, dissemination, and consumption system. It stresses that this transformative endeavor is not a (purely) technical, but socio-technical undertaking with profound implications for our collective future.

The following questions are guiding this dissertation project:

• What is the role of trust in the development and embedding of AI- and ML-assisted socio-technical energy grid configurations and corresponding practices (“smart grid”), in particular at the grid operation levels?
• How are conceptions of trust, of AI- and ML-assisted practices, and of flexibility linked and corresponding to each other as pivotal resources within the discourse on the energy grid as AI- and ML-assisted socio-technical system?
• Which dimensions of trust identified in the first project phase of the ITAS project “Social trust in learning systems” are particularly salient and/or contested within the discourse on the energy grid as AI- and ML-assisted socio-technical system?
• How is trust reflected in the way in which university-based research conceptualizes, researches, and analyzes the interactions between the components of future energy systems to develop solutions for a successful incorporation of the fluctuating renewable energy sources?
• How is trust embedded in the AI- and ML-assisted energy production/demand foresight, flexibility modeling, and responsive system building by grid operators, in particular in relation to the grid operation requirements and expected grid operator responsibilities resulting from the development and incorporation of AI- and ML-assisted socio-technical energy grid configurations and corresponding practices (“smart grid”)?

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