Thinking fast and slow -- a cognitive inspired framework for decision intelligence for power systems

TL;DR

This paper proposes a decision intelligence framework inspired by cognitive psychology and distributed intelligence models to enhance power system decision-making across multiple timescales and locations.

Contribution

It introduces a novel cognitive-inspired architecture for power systems that integrates edge and central decision-making across diverse timescales.

Findings

Framework maps decision-making in power systems to System 1 and 2 models.

Highlights trade-offs between speed, accuracy, and robustness in decision architectures.

Lays foundation for autonomous, trustworthy power systems.

Abstract

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.