AI DRIVEN NET ZERO ENERGY SMART GRID 2.0 REVOLUTIONIZES WITH 90 MVA TRANSFORMERS AND RENEWABLES

Abstract

The worldwide trend towards Net Zero Energy has intensified the requirement for smart, resilient and sustainable power systems that can effectively leverage renewable energy sources at a large scale. However, the variable and uncertain characteristic of solar (S) and wind (W) resources still makes it difficult for these power sources to operate under the framework, resulting in frequency mismatches, peak load disparity and instability. Traditional grid and old Smart Grid 1.0 technologies (defined by little more than digital monitoring and simple automation) are not capable of enabling the predictive intelligence and flexible control for power system transition today. To address this lack, the current work suggests a conceptual foundation for the Smart Grid 2.0, transformed through combined utilization of AI (Artificial Intelligence), 90 MVA high-capacity transformers and renewable energy sources.

The proposed architecture places AI at the center for demand forecasting via machine learning, adaptive grid optimization based on reinforcement learning, and fault finding and maintenance through predictive analytics. At the same time, AI-based optimization methods are used for the integration of solar and wind to make the reliability and scalability as well as the dependence to fossil-fuel backups as minimal as possible. Its scalable design is based on high-capacity 90MVA transformers as the foundation, which provide it with voltage stability, load scalability, as well as a set of predictive maintenance functionalities when up leveled with AI-driven predictive maintenance. Combined, these elements form a closed-loop optimization loop which provides a renewed forecast, allocation and energy flow balance at all times while providing increased robustness and adaptability to perturbations.