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.

翻译：电力系统中的决策涉及多个时间尺度——从毫秒级防止电涌，到秒级平衡频率并保护电网资产，再到分钟级实时能量平衡，直至日前、季节性和长期规划。由间歇性可再生能源和分布式能源资源（DER）引发的日益增长的不确定性和复杂性，亟需新的电力系统智能与架构方法。丹尼尔·卡尼曼描述了人类决策中两种系统的相互作用：系统1快速、直觉化、基于经验且反应性，系统2缓慢、审慎且分析性。类似地，章鱼智能体现了一种在中心智能与边缘智能之间分布式且协调的决策模型。未来电力系统必须嵌入协调智能，使其能够在不同时间尺度上运行，并部署在边缘和中心层面。本文基于决策中固有的权衡（如速度/延迟、能量成本/计算、准确性和鲁棒性），将电力系统中的决策智能映射到系统1和系统2以及边缘-中心架构范式中。该框架启发了一种代理智能架构——为未来可信、自治的电力系统奠定基础。