In the rapidly evolving world of artificial intelligence, a new paradigm known as “agentic AI” is gaining traction, particularly in the realm of electrical power systems engineering. Researchers Soham Ghosh and Gaurav Mittal, affiliated with the University of Texas at Austin, have recently published a comprehensive review that aims to define and categorize agentic AI, distinguishing it from traditional AI models. Their work, published in the IEEE Transactions on Power Systems, explores the practical applications of this innovative approach within the energy sector.
Agentic AI systems are designed to perform tasks with a higher degree of autonomy and adaptability compared to conventional AI models. Ghosh and Mittal’s paper introduces the concept of agentic AI and provides a detailed taxonomy to differentiate it from previous AI paradigms. The researchers highlight the diverse applications of agentic AI across various engineering fields, with a specific focus on electrical engineering.
The paper presents four detailed case studies that demonstrate the practical impact of agentic AI in electrical power systems. One of the use cases involves an advanced agentic framework designed to streamline complex power system studies and benchmarking. Another case study introduces a novel system developed for survival analysis of dynamic pricing strategies in battery swapping stations. These examples illustrate how agentic AI can enhance the efficiency and reliability of electrical power systems.
To ensure the robust deployment of agentic AI systems, the researchers conducted detailed failure mode investigations. Based on their findings, they provide actionable recommendations for the design and implementation of safe, reliable, and accountable agentic AI systems. This work serves as a critical resource for both researchers and practitioners in the field of electrical engineering.
The practical applications of agentic AI in the energy sector are vast. For instance, the advanced framework for power system studies can help utilities and grid operators optimize their operations and improve system reliability. The survival analysis system for dynamic pricing strategies can assist in developing more effective and sustainable business models for battery swapping stations, which are becoming increasingly important with the rise of electric vehicles.
In conclusion, Ghosh and Mittal’s research provides a clear and concise definition of agentic AI and demonstrates its potential to transform the electrical power systems engineering landscape. By offering practical use cases and actionable recommendations, the paper serves as a valuable guide for those looking to harness the power of agentic AI in the energy sector. The research was published in the IEEE Transactions on Power Systems, a leading journal in the field of electrical engineering.
This article is based on research available at arXiv.