Artificial intelligence is rapidly transforming the global data center landscape. What was once primarily an IT infrastructure challenge is now becoming an energy-system challenge. As large AI models expand and computing clusters grow more power-intensive, AI data centers are emerging as a new class of load: highly concentrated, highly dynamic and exceptionally sensitive to power interruptions.
According to the International Energy Agency, global electricity consumption from data centers stood at about 415 TWh in 2024 and could rise to around 945 TWh by 2030, with AI serving as a major driver of that growth. The IEA also notes a widening mismatch between the speed of data center construction and the longer development cycles required for power infrastructure.
This shift is redefining the foundations of AI infrastructure. For hyperscale data centers and AI computing campuses, power is no longer a back-end utility. It is a strategic determinant of project viability, operating efficiency, system resilience and long-term sustainability. The central question is no longer simply how to procure more electricity, but how to deliver stable, efficient, low-carbon and scalable power from the grid all the way to the chip.
This is the context in which Sungrow’s recent Global Renewable Energy Summit, or GRES 2026, should be understood. Held in Hefei under the theme “Value-Driven, Scenario-Proven,” the event showcased Sungrow’s growing ability to design integrated energy architectures for complex, high-value industrial scenarios, from PV-storage integration and mining microgrids to EV charging, hydrogen applications and AI data centers.
Among these scenarios, the AI data center — or AIDC — stands out as one of the most strategically significant. At GRES 2026, Sungrow released its Future-Ready AI Data Center Power Solutions White Paper, outlining a grid-specific security and stability solution designed to support MW-level racks and GW-scale clusters while improving the safety, stability and low-carbon performance of AIDC systems. The company also said it plans to launch a new generation of high-efficiency solid-state transformers, or SST, for data center applications later this year.
Sungrow’s experience in large-scale energy storage provides an important foundation for its AIDC strategy. At the product level, PowerTitan 3.0 is positioned as a core storage platform for high-reliability, high-density power scenarios such as AI data centers. The 7.14 MW utility-scale ESS integrates 600+ Ah stacked battery cells with a fully liquid-cooled SiC PCS, achieving a round-trip efficiency of up to 92%, while also incorporating grid-forming capabilities to support voltage stability, fast response and resilient operation under complex grid conditions. The system can enable a 1 GWh project to be deployed within 12 days, offering a reference for fast delivery in power-constrained AI infrastructure projects.
This product positioning is supported by Sungrow’s track record with the PowerTitan series in utility-scale projects. In Saudi Arabia, the company has highlighted a 7.8 GWh energy storage project with ALGIHAZ, using more than 1,500 PowerTitan 2.0 AC block units. The system’s all-in-one AC-DC block design integrates PCS, pre-assembled battery containers, medium-voltage transformers and RMU, with factory testing to reduce on-site installation time. Rather than serving as a direct AIDC case, the project demonstrates Sungrow’s ability to deliver standardized, high-density and rapidly deployable storage systems at scale — capabilities that are directly relevant as AI data centers place higher demands on power reliability, grid support and deployment speed.
The logic behind Sungrow’s AIDC strategy is clear. AI data centers require a new power architecture because their load profile differs sharply from that of traditional data centers. AI clusters demand higher rack power density, faster load response, stricter power-quality control and stronger grid-interconnection capabilities. Electricity must travel through multiple stages — grid connection, transformation, distribution, energy storage, backup systems, rack-level delivery and ultimately chip-level consumption. Every conversion stage creates potential losses, complexity and reliability risks.
Dr. Cai, General Manager of the Product Business Center at Sungrow ESS BU, said Sungrow is addressing this challenge by extending its long-standing strengths in power electronics, energy storage and grid-support technologies into the AI infrastructure sector. At the grid and campus level, he said PV-storage integration and energy management can improve the availability and utilization of clean power. At the resilience level, energy storage can evolve beyond backup power to become a fast-response buffer between the grid and mission-critical computing loads.
Grid-forming technology is central to this vision, Dr. Liu, General Manager of the Microgrid and Grid Solutions Center at Sungrow noted that Sungrow will integrate grid-forming technology into AIDC energy storage systems to mitigate grid disturbances and enhance system stability. In practice, this means storage systems can provide voltage support, frequency response, disturbance damping and fault ride-through — capabilities that are becoming increasingly important as AI data centers connect to grids with higher renewable penetration or weaker local stability.
Safety is another cornerstone of Sungrow’s AIDC power architecture. Dr. Cai emphasized that AI data centers concentrate high-value computing assets in dense spaces, making energy storage safety essential. Sungrow has also used large-scale burn testing to support its safety claims. In a 20 MWh PowerTitan burn test, the company said the system demonstrated no thermal runaway propagation, with plant-level safety verified. It also highlights 1,385°C thermal insulation, more than 25 hours of fire resistance and support for 15 cm back-to-back installation, features designed to strengthen safety while reducing spacing requirements in large-scale deployments.
The rise of AI is pushing the infrastructure race beyond servers, chips and software. It is becoming a contest over power availability, system efficiency, delivery speed, resilience and low-carbon integration. Sungrow’s “grid-to-chip” approach positions power electronics and energy storage not as auxiliary infrastructure, but as the energy backbone of the AI era.
As AI computing demand accelerates, the energy system will increasingly determine how fast, how safely and how sustainably data centers can scale. With its integrated portfolio across PV, storage, grid-forming control and high-efficiency power conversion, Sungrow is building a new pathway for AI infrastructure — one that connects renewable energy, resilient grids and intelligent computing through a more powerful, efficient and future-ready electricity architecture.
