Challenges and Limitations of Artificial Intelligence Implementation in Modern Power Grid

Abstract

Ongoing global decarbonization and energy transition have led to significant changes in the traditional power grid, resulting in new challenges for grid operators. These challenges, which are exacerbated by the increased penetration of distributed energy resources (DER), include power system stability, protection and control system malfunction, and supply and demand balance. To better understand the implications of high DER penetration, we proposed a classification of modern grids based on the DER integration level and identified the specific challenges faced by each category. We defined eight key challenges posed by DER on the grid and examined recent academic research on the application of AI techniques to modern power systems. Despite the potential benefits of AI in terms of power system stability and protection, there are currently no commercial applications of this research. Our study analyzed the limitations and barriers to practical AI implementation in power system protection and stability, including the use of synthetic data, insufficient real measurement data, protection selectivity, and the black-box nature of the models. We emphasize the importance of further research and development in the fields of Explainability AI and physics-informed ML to overcome these challenges and enable the successful implementation of AI in modern power grid stability and protection.