Foundation Twins: A New Generation of Power Systems Digital Twins using Foundation AI Models

TL;DR

This paper presents a vision for next-generation power systems digital twins, called Foundation Twins, which leverage foundation AI models and reinforcement learning to enhance multi-timescale decision-making and simulation.

Contribution

It introduces the concept of Foundation Twins, integrating foundation models and reinforcement learning for advanced power system digital twins.

Findings

Conceptual framework for Foundation Twins

Potential to improve multi-timescale decision-making

Bridges foundation models with power system simulation

Abstract

Power systems are inherently multi-timescale systems, with different physical phenomena and decision-making processes spanning multiple timescales, time horizons, and geographic scopes. I envision power systems digital twins (DTs) as powerful modeling and simulation tools that can accelerate and improve decision-making across different time scales and geographic scopes. However, until now, research has not delivered such a vision, and power systems DTs remain a concept distant from implementation. This is not a regular research paper. This is a position paper that outlines my vision for developing a new generation of power systems DTs that leverage recent advances in artificial intelligence (AI) and machine learning (ML). I call these Foundation Twins. Foundation Twins combines the generalization features of foundation models with the decision-making capabilities of reinforcement learning (RL) architectures to deliver the envisioned power systems DTs.