Designing for human-AI teaming in power system control room decision support

Abstract

The integration of renewable energy sources fundamentally alters the operating environment of transmission system operators (TSOs). While essential for achieving a sustainable and low-carbon energy system, their volatility leads to more frequent congestion events, narrower safety margins, and rising information demands for grid operators across multiple distributed systems. In this context, timely and effective decision-making becomes increasingly challenging. AI-based decision support tools (DSTs) have been deployed in TSO control rooms, for example, the GridOptions tool at TenneT TSO. However, these DSTs still offer only one-way assistance, providing context and recommendations without true human-AI collaboration. In contrast, adaptive decision-making and human cognitive needs require human-AI teaming, that is, bi-directional communication through synergetic interactions with ongoing refinements. This study takes a first step towards human-AI teaming for decision support in power-system control rooms. Following a Scenario-Based Design approach, utilizing the Joint Control Framework, we (i) perform a requirements analysis to identify how human-AI teaming requirements shift across different timeframes, (ii) design cognitive human-AI collaboration patterns specific for each timeframe, and (iii) formulate corresponding design guidelines for user interfaces for grid operators. Ultimately, this research seeks to contribute to the design of adaptive DSTs that enhance the resilience of grid control strategies via effective human–AI collaboration.

Keywords: Human-AI Teaming, Hypervision, Scenario-Based Design, Joint Control Framework, Decision Support Tool, Control Room Operators, Artificial Intelligence, GridOptions Tool

DOI: 10.54941/ahfe1007180