China’s AI Energy Overhaul

China’s ambitious push to decarbonize its energy sector is increasingly leveraging artificial intelligence, transforming not just policy discussions but the very fabric of daily power operations. From production to transmission and consumption, AI is becoming an indispensable tool in managing the complexities of a cleaner energy future.

A prime example of this integration can be observed in Chifeng, a city in northern China. Here, a forward-thinking factory exemplifies the potential of renewable energy by producing hydrogen and ammonia. This facility operates on a closed-loop system, powered exclusively by electricity generated from adjacent wind and solar farms. While this approach offers the environmental benefits of clean energy, it also presents a significant challenge: the inherent intermittency of renewable resources, which fluctuate with weather patterns.

To maintain stable production amidst this variability, the factory employs an AI-driven control system developed by its owner, Envision. This intelligent software transcends static operational schedules, continuously adjusting output in real-time based on dynamic changes in wind speed and solar irradiation. Envision’s chief engineer for hydrogen energy, Zhang Jian, aptly described the system as a conductor, orchestrating the delicate balance between electricity supply and industrial demand. When wind speeds surge, production automatically escalates to capitalize on the abundant clean power. Conversely, when conditions wane, the system swiftly curtails electricity consumption to prevent strain on the network. This sophisticated management allows the plant to achieve high operational efficiency despite the inherent volatility of renewable energy sources.

Initiatives like these are foundational to China’s strategic objectives for hydrogen and ammonia, fuels pivotal for reducing emissions in demanding sectors such as steel manufacturing and maritime shipping. More broadly, they underscore a comprehensive strategy to harness AI for managing the escalating complexity associated with integrating greater volumes of renewable energy into the national grid.

Academics and researchers posit that AI will play a crucial role in China’s ability to meet its climate targets. Zheng Saina, an associate professor at Southeast University specializing in low-carbon transitions, highlights AI’s capacity to support a wide array of tasks, from precise emissions tracking to sophisticated forecasting of electricity supply and demand. However, she also sounds a note of caution, acknowledging that the rapid growth of AI itself, particularly through energy-intensive data centers, is a significant contributor to escalating power consumption.

China leads the world in installing new wind and solar capacity, yet the efficient absorption of this power remains a persistent challenge. Cory Combs, associate director at the Beijing-based research firm Trivium China, points out that AI is increasingly recognized as a key enabler for enhancing grid flexibility and responsiveness.

This recognition was formalized in September with the introduction of Beijing’s “AI+ Energy” strategy. This national plan emphasizes deepening the synergy between AI systems and the energy sector, including the development of specialized AI models tailored for grid operations, power generation, and industrial applications. By 2027, the government aims to launch numerous pilot projects and explore AI applications across over 100 use cases. Within an additional three years, the objective is for China to achieve a globally leading level of AI integration within its energy infrastructure.

Combs further elaborates that the focus is on developing highly specialized AI tools designed for specific tasks, such as optimizing wind farm operations, managing nuclear power plants, or balancing the grid, rather than pursuing general-purpose AI. This contrasts with the approach in the United States, where significant investment has been directed towards the creation of advanced large-language models, according to Hu Guangzhou, a professor at the China Europe International Business School.

Demand forecasting represents an area where AI can yield immediate and substantial impact. Fang Lurui, an assistant professor at Xi’an Jiaotong-Liverpool University, explains that power grids operate under the critical imperative to match supply and demand instantaneously to avert blackouts. Accurate predictions of renewable energy output and electricity consumption empower grid operators to proactively plan, facilitating energy storage in batteries during surplus periods and reducing reliance on carbon-intensive coal-fired backup plants.

Several cities are already embracing these innovative solutions. Shanghai, for instance, has launched a city-wide virtual power plant that aggregates diverse energy resources – including data centers, building management systems, and electric vehicle charging infrastructure – into a single, coordinated network. During a trial conducted last August, this system successfully reduced peak demand by over 160 megawatts, an amount comparable to the output of a small coal-fired power station. Combs underscores the significance of such systems in the context of modern power generation, which is increasingly characterized by decentralized and intermittent sources. He notes, “You need something very robust that is able to be predictive and account for new information very quickly.”

Beyond grid management, China is also exploring AI’s application within its national carbon market. This market encompasses over 3,000 companies operating in emissions-intensive industries such as power generation, steel, cement, and aluminum – sectors collectively responsible for more than 60% of the country’s carbon emissions. Chen Zhibin, a senior manager at the Berlin-based think tank adelphi, suggests that AI can significantly assist regulators in verifying emissions data, refining the allocation of carbon allowances, and providing companies with enhanced clarity on their production costs.

However, the expansion of AI’s role in the energy sector is accompanied by growing environmental concerns. Studies project that by 2030, China’s AI data centers could collectively consume over 1,000 terawatt-hours of electricity annually, a figure comparable to Japan’s current total annual electricity usage. Furthermore, lifecycle emissions associated with the AI sector are anticipated to rise sharply, peaking well beyond China’s 2030 emissions reduction target.

Xiong Qiyang, a doctoral researcher at Renmin University of China who contributed to relevant studies, points out that these findings reflect the enduring dominance of coal in China’s energy mix. He cautions that the rapid proliferation of AI could complicate the nation’s climate objectives if the transition to cleaner energy sources does not accelerate commensurately.

In response to these challenges, regulatory bodies are implementing stricter measures. A 2024 action plan mandates data centers to enhance their energy efficiency and increase their reliance on renewable power by 10% year-over-year. Other initiatives encourage the development of new data center facilities in western regions, areas endowed with abundant wind and solar resources.

On the eastern coast, operators are also pioneering novel solutions. Near Shanghai, an innovative underwater data center is slated for operation. This facility will utilize seawater for cooling, significantly reducing energy and water consumption. Its developer, Hailanyun, states that the data center will primarily draw power from an offshore wind farm, with potential for replication should the project prove successful.

Despite the escalating energy demands driven by AI, Xiong maintains that its overall impact on emissions could still be positive if implemented judiciously. By optimizing heavy industries, power systems, and carbon markets, AI has the potential to remain an indispensable component of China’s emission reduction efforts, even as it introduces new complexities that policymakers must adeptly manage.