Keynote Title: Medium-Voltage Solid-State Transformers for AI Data Centers: Enabling Onsite Generation, Storage, and Intelligent Energy Management

Abstract: AI data centers are projected to consume hundreds of terawatt-hours annually by 2035, demanding power conversion technologies that are not only energy-efficient but also cost-effective, scalable, and reliable. It is estimated that the global market for power supplies, distribution, and management systems serving AI data centers and cloud computing is expected to reach approximately $145–150 billion. Conventional line-frequency transformers and multi-stage AC-DC architectures suffer from low efficiency, bulkiness, high cost, low reliability, and limited dynamic response. To support the rapid growth of AI workloads and comply with emerging energy and environmental standards, innovative power electronics solutions are urgently needed. This presentation explores the development of a novel megawatt-scale, medium-voltage (MV) power conversion system based on a Variable Air Gap Solid-State Transformer architecture. The approach aims to reduce energy loss, footprint, and cost while enabling flexible, modular deployment, and achieving more reliable operation in large-scale AI data centers.