Inside China’s Ambitious Drive to Revolutionize Its Energy System with AI

China’s ambitious push to clean up its energy system is no longer just about policies or targets. Artificial intelligence (AI) is now reshaping how power is produced, distributed, and consumed in everyday operations, driving efficiency and stability in a rapidly changing energy landscape.

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AI in Action: The Chifeng Renewable Factory

A clear example of this transformation is found in Chifeng, a northern Chinese city. Here, a renewable-powered factory produces hydrogen and ammonia using electricity exclusively from nearby wind and solar farms. Unlike traditional plants connected to the wider grid, this facility operates as a closed system.

While self-contained renewable energy ensures low emissions, it introduces volatility. Power levels fluctuate with weather conditions, challenging consistent production. To tackle this, the plant uses an AI-driven control system developed by its owner, Envision.

Rather than relying on fixed schedules, the AI continuously adjusts output to match changes in wind and sunlight. Zhang Jian, Envision’s chief engineer for hydrogen energy, compares the system to a conductor, coordinating electricity supply with industrial demand in real time. When wind speeds rise, production ramps up automatically; when conditions weaken, electricity use drops to prevent strain. This approach allows the plant to operate at peak efficiency despite renewable energy’s natural fluctuations.

Hydrogen and Ammonia: Fuels of the Future

Projects like the Chifeng factory are central to China’s vision for hydrogen and ammonia. These fuels are considered crucial for reducing emissions in heavy industries, including steelmaking and shipping. Beyond individual plants, such initiatives reflect a broader strategy: using AI to manage the growing complexity of integrating renewables into the national grid.

AI is not just a tool for operational efficiency—it’s a cornerstone of China’s low-carbon strategy. Experts say it can optimize energy production, track emissions, forecast electricity demand, and support decision-making across industries.

AI and China’s Climate Goals

Zheng Saina, an associate professor at Southeast University, emphasizes AI’s potential in supporting China’s climate ambitions. From real-time monitoring to predictive analytics, AI can enhance decision-making in energy systems. However, she also warns that AI is energy-intensive, particularly through power-hungry data centres, highlighting a need for careful planning.

China now leads the world in installing wind and solar capacity, but efficiently absorbing this energy remains a challenge. According to Cory Combs, associate director at Beijing-based Trivium China, AI is increasingly viewed as the solution to make the grid more flexible, responsive, and reliable.

The “AI+ Energy” Strategy

In September, Beijing formalized this vision with the “AI+ energy” strategy. The plan aims to deepen the integration of AI in power generation, grid operations, and industrial energy management. By 2027, the government intends to launch dozens of pilot projects, testing AI across more than 100 use cases. Within a few years, officials hope China will reach a world-leading level of AI integration in the energy sector.

The focus is on specialized AI systems tailored to specific energy applications, such as wind farms, nuclear plants, and grid balancing. This contrasts with approaches in the United States, where investment often emphasizes large, general-purpose AI models.

AI for Demand Forecasting

One of AI’s most immediate impacts is in demand forecasting. Fang Lurui, an assistant professor at Xi’an Jiaotong-Liverpool University, notes that power grids must match supply and demand at every moment to avoid outages. Accurate predictions of renewable output and energy use enable operators to store excess energy in batteries, reduce reliance on coal-fired backup plants, and maintain system stability.

Shanghai offers a practical example. The city has developed a virtual power plant linking dozens of operators—including data centres, building systems, and electric vehicle chargers—into a single coordinated network. During a trial, the system reduced peak demand by over 160 megawatts, equivalent to a small coal plant.

Managing a Complex, Distributed Grid

Modern power generation is increasingly decentralized and intermittent, making AI-driven coordination essential. Combs explains, “You need something very robust that is predictive and can quickly respond to new information.” AI tools provide this capability, enabling operators to react to fluctuations in supply and demand almost in real time.

AI and China’s Carbon Market

Beyond electricity management, AI is being applied to China’s national carbon market, which covers more than 3,000 high-emission companies in sectors like power, steel, cement, and aluminium. Together, these industries account for over 60% of the country’s carbon emissions.

AI can enhance emissions monitoring, optimize the allocation of carbon allowances, and provide companies with better insights into production costs. Chen Zhibin, a senior manager at think tank adelphi, believes these tools will strengthen regulatory oversight and improve market efficiency.

Energy Demands and Risks of AI

The rise of AI also brings challenges. By 2030, China’s AI data centres could consume over 1,000 terawatt-hours of electricity annually—comparable to Japan’s total current usage. Lifecycle emissions from AI infrastructure are projected to peak well after China’s 2030 climate target, creating potential conflicts between digital growth and sustainability.

Xiong Qiyang, a doctoral researcher at Renmin University, warns that the country’s reliance on coal complicates the picture. Rapid AI expansion could undermine emission reduction efforts unless power sources shift quickly toward renewables.

Policy Measures and Innovations

To mitigate these risks, regulators are tightening standards. A 2024 action plan mandates annual improvements in data centre energy efficiency and increased use of renewable power. New facilities are encouraged to locate in western China, where wind and solar resources are abundant.

Innovative projects are also emerging on the east coast. Near Shanghai, an underwater data centre will use seawater for cooling, reducing energy and water use. The facility will primarily draw power from an offshore wind farm, serving as a potential model for future sustainable data centres.

Balancing AI Growth and Emission Reduction

Despite the rising energy demands of AI, experts like Xiong believe its overall impact can remain positive. When applied strategically to industrial processes, power systems, and carbon markets, AI can reduce emissions while improving operational efficiency. The key is balancing growth with careful energy planning and continued adoption of renewables.

Frequently Asked Questions:

What is China’s AI+ Energy strategy?

China’s AI+ Energy strategy aims to integrate artificial intelligence into power generation, grid management, and industrial energy use. By 2027, the country plans dozens of pilot projects and over 100 use cases to optimize efficiency and renewable energy adoption.

How is AI improving renewable energy management in China?

AI helps balance fluctuating wind and solar power by predicting supply and adjusting energy use in real time. For example, AI-controlled factories ramp up or reduce production based on renewable output, improving stability and efficiency.

Which industries benefit most from AI in China’s energy system?

Heavy industries like steel, cement, aluminum, and chemicals, as well as energy-intensive facilities like data centres, benefit from AI through optimized operations, better demand forecasting, and reduced emissions.

How does AI support China’s climate goals?

AI improves emissions tracking, demand forecasting, and energy efficiency, allowing the country to reduce reliance on coal and increase renewable usage. It also aids carbon market management, helping regulators and companies monitor and lower carbon output.

What are the risks of using AI in energy systems?

AI itself consumes large amounts of electricity, particularly in data centres, which could offset some emission reductions if energy sources are not predominantly renewable. Lifecycle emissions from AI are projected to rise significantly by 2030.

Are there examples of AI in action in China’s energy sector?

Yes. In Chifeng, an AI-powered factory produces hydrogen and ammonia using renewable energy. Shanghai operates a virtual power plant linking buildings, EV chargers, and data centres to reduce peak energy demand.

How does China’s approach differ from other countries like the US?

China focuses on specialized AI tools tailored for specific energy applications, such as grid balancing or wind farm management. In contrast, the US invests heavily in general-purpose AI models like large-language models.

Conclusion

China’s integration of AI into its energy system marks a bold step toward a cleaner, smarter, and more efficient future. From stabilizing renewable-powered factories to coordinating citywide virtual power plants, AI is transforming how electricity is produced, distributed, and consumed. While the technology brings new energy demands and potential risks, careful planning, regulatory oversight, and innovative solutions ensure that AI remains a powerful tool in reducing emissions and achieving climate goals. As China continues to scale AI-driven energy solutions, the country is not just managing complexity—it is redefining the very way energy systems operate, setting a model for the world to follow.