The integration of renewable energy sources has fundamentally altered the operating environment of transmission system operators (TSOs). While essential for achieving a sustainable and low-carbon energy system, their volatility introduces significant uncertainty and volatility into the power grid. For operators in control rooms, this has led to more frequent congestion events, narrower safety margins, and rising information demands across multiple fragmented systems. In this context, timely and effective decision-making becomes increasingly challenging.

The first AI-based decision support tools (DSTs) have been deployed in TSO control rooms, for example, the GridOptions tool at TenneT TSO. These DSTs remain in the assistance mode of decision support by providing context and recommendations to the human operator in a one-directional fashion. However, timely and effective decision-making under uncertainty requires bi-directional human-AI communication, feedback, and co-learning. Consequently, this study investigates how AI-based DSTs can move from an assistance mode to joint AI-human decision making.

By employing novel concepts like the Supportive AI Framework and the Joint Control Framework, this study examines how human-AI teaming can evolve across different decision-making contexts and how interfaces can dynamically adapt to situational demands in both time-critical and less urgent scenarios. In particular, human cognitive needs figure prominently in how adaptable AI-powered interfaces can support operators in maintaining grid stability under uncertainty.

Drawing from observation, collaborative interaction patterns were developed to describe how human-AI teamwork can evolve across congestion operation timeframes. These patterns reveal opportunities and challenges in dynamically allocating initiative between humans and AI, maintaining situational awareness, and safeguarding human agency in safety-critical contexts.

Ultimately, this research seeks to contribute to the design of adaptive and supportive DSTs that unify data, reduce cognitive load, and facilitate reflection and learning. By addressing the dual challenge of information overload and uncertainty, the work aims to enhance the resilience of grid control strategies, stimulating effective human-AI collaboration, and enable the continued integration of renewable energy sources into the power system.