According to a research report released by Shenwan Hongyuan Group Co.,Ltd. (ASX: 000166), computing-power and energy synergy is being developed along three main tracks: facility upgrades, resource operations, and dispatch with interfaces. The report emphasizes distinguishing between order fulfillment and recurring service opportunities. On the facility side, focus is on power supply and distribution, liquid cooling, and energy storage demand; on the operations side, attention centers on power grid access, customer onboarding, and capital returns; and on the software side, key metrics include production system integration, paying customers, and cross-project scalability. In the near term, investors should prioritize companies with existing customer bases and order backlogs, while in the medium-to-long term, the focus should shift to recurring revenue potential from routine dispatch and energy operations, along with separating overall corporate performance from computing-power-related business contributions.

The primary viewpoint from Shenwan Hongyuan Group Co.,Ltd. (ASX: 000166) is that growth in AI electricity consumption, combined with the need for renewable energy absorption, is jointly driving the computing-power and energy synergy. Policy has evolved from spatial layout planning to operational mechanisms. Data centers require expanding electricity capacity and high supply continuity, while western renewable energy sources face output volatility and limited local absorption capacity. Computing-power and energy synergy is expected to improve the temporal and spatial matching between these two sides. Domestic policy has progressed from the national "East Data, West Computing" hub layout to green power direct connections, bidirectional coordination between computing and power, and market-based trading. Green power share, PUE, and rack utilization rates respectively constrain power sourcing, facility efficiency, and asset utilization, pushing the industry to optimize both resource allocation and operational models concurrently.

Electricity price differentials create operational income, while the facility investments required to secure low-cost power ultimately determine project returns. Taking a 10MW IDC as an example, under assumptions of a PUE of 1.2 and a stable-period load factor of 90%, reducing the electricity price from RMB 0.35 per kWh to RMB 0.25 per kWh would save approximately RMB 9.46 million in annual electricity costs. After fixing conditions such as 100% direct connection coverage, the IRR scenarios for reusing existing grid connections, adding new 35kV projects, and adding 110kV projects are 10.8%, 8.4%, and 5.9% respectively. The advantage of low electricity pricing must be assessed together with investments in dedicated lines, substations, and energy storage. Reusing existing facilities holds significant economic value.

Computing-power dispatch enhances effective output and load flexibility, while computing-power interfaces drive cross-system coordination and the separation of commercial inference phases. Optimization of resource orchestration and data access reduces computing idle time, and power-aware dispatch further leverages task delivery slack, adjusting execution schedules based on electricity prices, renewable output, and grid requirements. Offline inference tasks offer substantial adjustment flexibility, whereas online interactive services and large-scale synchronous training are constrained by latency and communication limitations. Interfaces connect computing and energy systems through unified metering, control authorization, and execution feedback. Commercial revenue covers system construction, forecasting and maintenance operations, and trading assistance, with demand-response compensation serving as an additional revenue stream.

The stable low-carbon power supply goals of China and the United States are converging, but differences in resource conditions and industrial advantages shape distinct implementation paths. China possesses renewable energy resources, cross-regional transmission capabilities, and a strong equipment manufacturing base, with the key challenge being the conversion of these advantages into deliverable computing capacity and project returns. Certain AI clusters in the United States face constraints from localized grid connection bottlenecks and equipment delivery timelines, simultaneously assessing grid expansion, long-term power purchase agreements, and on-site power generation. High-density server racks are driving power distribution and energy storage upgrades in both countries. Leading U.S. chip and cloud companies exert significant influence on architecture and verification requirements, while China's power equipment and battery supply chains provide manufacturing advantages. Ultimately, competition will come down to system reliability, customer certification, and large-scale delivery capability.

Risk warnings include: AI demand and project implementation falling short of expectations; narrowing electricity price differentials and volatility in green power supply; supporting investment exceeding forecasts and grid connection delays; load regulation impacting business continuity; uncertainty in market rules and revenue realization; slower-than-expected commercialization of new technologies; market competition; and overseas operational risks.