Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges

Abstract

Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive review that establishes a precise definition and taxonomy for "agentic AI," with the aim of distinguishing it from previous AI paradigms. The concepts are gradually introduced, starting with a highlight of its diverse applications across the broader field of engineering. The paper then presents four detailed, state-of-the-art use-case applications within electrical power systems engineering, a domain where the impact of agentic AI systems is expected to be particularly significant. The high impact of agentic AI systems in the field of electrical power systems is primarily driven by global trends toward clean energy transition and higher levels of grid automations, all of which create an environment where agentic AI can be readily deployed and effectively leveraged. These case studies demonstrate current and innovative state-of-the-art, ranging from an advanced agentic framework for streamlining complex power system studies and benchmarking to a novel agentic AI system developed for survival analysis of dynamic pricing strategies in battery swapping stations. Finally, robust deployment of these autonomous agents brings a unique set of challenges that are discussed in this manuscript through detailed failure mode investigations. From these findings, we derive actionable recommendations for the design and implementation of safe, reliable, and accountable agentic AI systems, offering a critical resource for researchers and practitioners.