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This paper analyzes power system decision-making through the lens of Kahneman's "Thinking Fast and Slow" framework, mapping system operations to System 1 (fast, reactive) and System 2 (slow, analytical) processes. It argues that future power grids require a coordinated, agentic intelligence architecture distributed between edge and centralized levels to handle increasing complexity from renewables and DERs. The paper proposes a framework considering trade-offs like speed, energy cost, accuracy, and robustness to inform the design of trustworthy, autonomous power systems.
Future power grids can learn from human cognition and octopus intelligence to build more robust and responsive decision-making systems.
Decision-making in power systems spans multiple timescales - from milliseconds to prevent surges, to seconds to balance frequency and protect grid assets, to minutes for real-time energy balancing, to day-ahead, seasonal, and long-term planning. Growing uncertainty and complexity, driven by intermittent renewables and distributed energy resources (DER), demand fresh approaches to power system intelligence and architecture. Daniel Kahneman describes the interplay of two systems of human decision-making: System 1 that is fast, intuitive, experience based, reactive, and System 2 that is slow, deliberate, analytical. Similarly, octopus intelligence illustrates a model for distributed yet coordinated decision-making between central and edge intelligence. Future power systems must embed coordinated intelligence that operates across diverse timescales and with placement at both edge and centralized levels. This paper maps decision-intelligence in power systems against System 1 and 2 and edge-central architecture paradigms based on the trade-offs inherent in decision making such as speed/latency, energy cost/compute, accuracy, and robustness. The framework inspires an agentic intelligence architecture - laying the foundation for trustworthy, autonomous power systems of the future.
