Power Electronics & Distribution
Sequential Operating Simulation of Solid State Transformer-Driven Next-Generation 800 VDC Data Center
This paper details the development of an SST-driven 800 VDC architecture, including a three-phase H-bridge AC/DC stage cascaded with a dual-active-bridge (DAB) DC/DC stage, and a coordinated closed-loop control scheme for DC-bus voltage stability. Implemented on a real-time digital simulation (RTDS) platform and evaluated with real-world operating profiles, the system demonstrates tight 800 VDC regulation, reduced energy consumption compared to UPS baselines, and satisfactory power-quality performance. A capacitance sensitivity test provides practical design guidance. This work offers a reproducible evaluation workflow and actionable insights for next-generation AI data centers.
Executive Impact & Key Findings
The research reveals crucial advancements in data center power distribution, directly impacting operational efficiency and reliability for enterprise AI infrastructure.
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Improved Energy Efficiency
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Reduced Input Losses
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Stable DC-Bus Voltage Regulation
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explores an SST-driven 800 VDC architecture for AI data centers, detailing the system's components, control, and performance compared to traditional UPS systems.
Details a three-phase H-bridge AC/DC stage cascaded with a dual-active-bridge (DAB) DC/DC stage, converting 10 kV MVAC to 800V LVDC with coordinated closed-loop control.
Evaluates the system using RTDS with real-world data center operating profiles, demonstrating tight voltage regulation, reduced energy consumption, and superior power quality.
Analyzes the impact of low-voltage-side capacitance on DC-bus ripple and low-frequency input-power oscillations, providing design guidance.
8.5%
Energy Savings vs. Traditional UPS
The SST-driven architecture significantly reduces input-side energy consumption by 8.5% compared to conventional UPS systems, offering substantial operational cost savings for AI data centers.
Key Stages of SST-Driven Data Center Power Flow
MVAC Grid Input
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SST Conversion (10kV AC to 800V DC)
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800V DC Bus
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AI Computing & Cooling Loads
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±1%
800 VDC Bus Voltage Regulation
The coordinated closed-loop control ensures stable 800 VDC regulation with minimal deviation (±1%), crucial for sensitive AI hardware.
RTDS Simulation for Real-World Validation
The proposed SST system was implemented on a Real-Time Digital Simulator (RTDS) platform, utilizing real-world day- and month-scale operating profiles of data centers. This approach allows for robust validation against dynamic AI power profiles, demonstrating its practical feasibility and superior performance compared to traditional UPS supply chains in realistic conditions.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Infrastructure Implementation Roadmap
A typical phased approach to integrating advanced power solutions, designed for minimal disruption and maximum impact.
Phase 1: Feasibility Study & System Design
Comprehensive assessment of current infrastructure, AI workload requirements, and detailed design of the SST-driven 800 VDC architecture tailored to your enterprise needs.
Phase 2: Component Procurement & Integration
Sourcing of high-efficiency SSTs, DAB converters, and related power electronics, followed by careful integration into existing data center facilities.
Phase 3: RTDS Simulation & Control Tuning
Deployment of the proposed system on a Real-Time Digital Simulator (RTDS) for rigorous testing, control scheme optimization, and performance validation under diverse operating scenarios.
Phase 4: Pilot Deployment & Performance Validation
Installation and testing of the new architecture in a controlled pilot environment, gathering real-world performance data and fine-tuning for optimal energy efficiency and power quality.
Phase 5: Full-Scale Rollout & Optimization
Scalable deployment across your data centers, coupled with continuous monitoring and optimization to ensure long-term stability, efficiency, and adaptability to evolving AI demands.
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