China Releases AI + Energy Action Plan, Benefiting Nuclear Power and Energy Storage

Overview

This report interprets the 'Action Plan on Promoting Mutual Empowerment between Artificial Intelligence and Energy' jointly issued by the NDRC, NEA, and other departments. The core view of the research report is that the policy sends an important signal: despite global concerns that AI-driven electricity demand growth may hinder clean energy progress, China adheres to the decarbonization agenda and chooses to accommodate AI growth by increasing renewable energy penetration, enhancing grid flexibility, and deepening power market reforms, rather than restarting large-scale coal power. This 'Green AI' path constitutes a substantial positive for the nuclear power, energy storage, grid equipment, and power digitalization sectors.

Core views

China chooses 'Green AI' over 'Thermal AI'. Faced with soaring power demand brought by AI computing power, signs of delaying renewable energy integration or extending fossil fuel life appear in some parts of the world, but China's policy explicitly emphasizes resolving supply and demand contradictions through higher proportions of renewable energy absorption, flexible demand-side management, and deeper participation in power markets. The document does not use AI electricity demand as a reason to slow down renewable energy deployment, but instead strengthens regulatory expectations such as 80% renewable energy usage ratio for new data centers, ensuring AI expansion serves long-term decarbonization goals. Nuclear power enters AI power supply discussion, benefiting low-carbon baseload IPPs. Policies explicitly mention exploring direct supply of nuclear power and hydrogen to AI infrastructure for the first time. Although China is unlikely to see extreme price spikes like the US market, long-term green power purchase agreements (PPAs), direct clean energy supply models, and demand for high-reliability power will enhance the strategic position of nuclear power in the electricity market. This helps improve contract quality for Independent Power Producers (IPPs), including longer terms, stronger counterparties, and moderate green premiums, constituting incremental benefits for CGN Power and China National Nuclear Power. AI Data Centers become part of the power system. The policy marks a conceptual shift, redefining AI data centers from traditional passive loads to flexible and dispatchable grid resources. Through price-response calculation optimization, cross-regional workload scheduling, and participating in Virtual Power Plants (VPP), AI loads can dynamically respond to renewable energy fluctuations. In addition, the policy encourages replacing traditional diesel backup generators with grid-forming storage; the former can actively provide voltage and frequency support, which has a positive driving effect on Battery Energy Storage Systems (BESS), advanced power conversion equipment, and power digitalization providers (such as Sungrow, CATL, BYD, NARI Technology, Sino-Electrics).

Analysis framework

The research report adopts a method combining policy text analysis and industrial chain transmission logic. First, by comparing different strategies in the US, Europe, and China in responding to AI electricity demand (US focuses on gas/nuclear power lifespan extension, Europe constrained by grid bottlenecks, China focuses on system flexibility reform), establish China's 'Green AI' policy tone. Second, deconstruct the 29 specific measures in the policy to identify key levers such as 'direct clean power supply', 'grid-forming storage', 'demand-side participation'. Finally, map these policy levers to specific electric power sub-sectors (nuclear power, storage, grid equipment), analyze their impact on business models (e.g., PPA contract quality, ancillary service revenue) and technology routes (e.g., grid-forming technology replacing diesel generators), thereby deriving beneficiary targets.

Methodology notes

• Industry/Industrial Analysis FrameworkSupply-demand framework

  Supply-side response analysis under AI electricity demand shock

  The research report analyzes how the supply side chooses to increase fossil energy (traditional path) or improve system flexibility and renewable energy share (innovative path) to meet when AI brings new electricity demand (demand-side shock), thereby judging industry technology routes and policy orientation.

• Industry/Industrial Analysis FrameworkSupply Chain Upstream-Midstream-Downstream Transmission

  Value redistribution in the power industry chain driven by policy

  The research report tracks how policy transmits from top-level design (Action Plan) to mid-stream equipment (storage, grid equipment) and upstream generation (nuclear power, renewable energy), identifying which links gain value improvement due to technical upgrades (such as grid-forming storage) or model innovation (such as green PPA).

• Valuation Method

  Green Premium and Contract Quality Valuation

  When evaluating nuclear power and renewable IPPs, the research report looks beyond electricity volume and focuses more on 'contract quality' (term, counterparty, green premium), believing that stable green cash flows can enhance the strategic valuation of assets. This is a cash flow quality analysis combining ESG attributes.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• CGN Power (1816.HK)

  Beneficiary: Nuclear Power Independent Producer, benefiting from AI demand for stable clean baseload power and green PPA support.

  Strengths

  Possesses coastal nuclear assets, fitting AI data center requirements for high-reliability power; Policy elevates nuclear power strategic status.

  Comparison

  Same as China National Nuclear Power are nuclear leaders, both benefit from accelerated coastal province AI demand.

• China National Nuclear Power (601985.SS)

  Beneficiary: Nuclear Power Independent Producer, benefiting from AI demand for stable clean baseload power and green PPA support.

  Strengths

  Domestic nuclear power operation leader, large asset scale, benefits from long-term green power agreements.

  Comparison

  Similar to CGN Power, benefiting from the reshaping of nuclear power's role in AI power supply.

• Sungrow Power Supply (300274.SZ)

  Beneficiary: Energy Storage System and Inverter Leader, benefiting from grid-forming storage deployment and grid digitalization demand.

  Strengths

  Has technological advantages in energy storage systems and power conversion equipment, fitting the trend of grid-forming storage promotion.

  Comparison

  Same as CATL and BYD as beneficiaries of storage, Sungrow leads in inverters and system integration.

• CATL (300750.SZ)

  Beneficiary: Core Supplier of Battery Energy Storage Systems (BESS), benefiting from accelerated storage deployment.

  Strengths

  Global battery leader, rapid growth in storage business, benefits from data center backup power replacement and grid-side storage demand.

  Comparison

  Significant advantages in battery cost and scale.

• BYD (1211.HK)

  Beneficiary: Energy Storage System and EV Leader, benefiting from storage deployment and green energy ecosystem.

  Strengths

  Possess full industry chain advantages in batteries, energy storage systems, and vehicle manufacturing.

  Comparison

  Diversified business layout, storage sector benefits from policy support.

• NARI Technology (600406.SS)

  Beneficiary: Grid Automation and Digitalization Leader, benefiting from AI-powered grid and virtual power plant construction.

  Strengths

  Leading position in grid dispatch, automation, and digitalization, fitting the direction of AI-powered power systems.

  Comparison

  Leader in secondary grid equipment, directly benefits from grid intelligent upgrade.

Key data

• Policy Target Year2027 / 2030Preliminary construction of a safe, green, and efficient AI energy support system by early 2027; construction of globally leading clean energy-driven AI infrastructure by 2030.
• Data Center Renewable Energy Usage Ratio80%Policy guidance promotes new data centers to reach this ratio, strengthening green power constraints.
• Action Plan Measure Count29 MeasuresCovers multiple dimensions including AI power supply, renewable energy integration, and power market participation.
• US Nuclear Power Agreement Reference Price~90-115 USD/MWhFor comparison, showing the scarce value of reliable clean baseload power, but the report believes China will not completely replicate this high price.

Impact & implications

The research report believes the policy has a profound impact on several sub-segments. For nuclear power and renewable independent power producers (IPPs), the policy-supported multi-year green PPA and direct power supply models will improve their revenue visibility and contract quality, enhancing their strategic position in the AI supply chain. For storage and grid equipment manufacturers, the promotion of grid-forming storage and replacement of diesel generators will bring new incremental markets, while the flexible regulation capability of AI data centers also provides commercial implementation scenarios for virtual power plants and power digitalization platforms. Overall, the policy accelerates the transformation of the power system towards intelligence, flexibility, and greenness, benefiting top equipment manufacturers with technological advantages and operators with high-quality clean assets.

Risks

• Lack of unified standards in ESG field definitions may cause investment theme importance to fluctuate over time.
• Uncertainty in policy specific implementation intensity and power market reform progress.
• If AI electricity demand growth exceeds expectations, it may cause short-term pressure on grid stability.

What to watch

• Specific implementation details for subsequent provincial and municipal levels regarding AI data center renewable energy usage ratios.
• Commercial pilot progress of grid-forming storage technology in data center backup power replacement.
• Case landing situation of nuclear power enterprises signing long-term green power agreements (PPA) with large tech companies.
• Demand response incentives for AI data center participation regarding auxiliary service varieties and pricing mechanisms in the power market.