Governance of Data Centers’ Electricity Consumption

In its latest report, the International Renewable Energy Agency (IRENA) described 2024 as a “record year for renewable energy,” after the share of newly added capacity exceeded 91% of total global electricity generation growth—amounting to 582 gigawatts, most of which came from wind and solar power.

By the end of 2024, total installed renewable energy capacity had reached about 4,440 gigawatts, representing 46.2% of total electricity generation capacity from various sources, which stands at 9,600 gigawatts. However, COP28, held in the United Arab Emirates in 2023, had already approved a target of raising installed renewable capacity to more than 11,000 gigawatts by 2030.

Integrating variable energy sources (solar and wind) into the energy system remains one of the main challenges, where energy storage technologies will play a vital role, especially given that their prices have dropped by more than 92% over the past decade. This positive trend has also driven the boom in electric mobility and the rise in demand for electric vehicles. Within this context, the analysis seeks to shed light on the role of renewable energy in meeting the electricity needs of data centers worldwide, along with the issues surrounding it, including government incentives to support the sustainability of their energy demand.

Challenges on the Road

The continuous decline in electricity costs from renewables—3.4 US cents per kilowatt-hour for wind and 4.3 US cents for solar—has motivated many sectors to set goals for covering part of their future consumption with renewable electricity. Among the most prominent is the data center sector, which currently accounts for 2% of global electricity use. This share is expected to double, reaching 4–6% within five years, with artificial intelligence consuming 10–20% of that total.

AI research dates back more than fifty years, but the past few years have seen breakthroughs that turned it into a core component of many fields of life and work—making it difficult to predict its future role. This also reshapes social and lifestyle patterns on the one hand, and defines its demand for rare minerals and energy resources on the other.

To illustrate AI’s energy demand challenges, consider that a single Google search consumes 0.3 watt-hours, but this rises 30-fold when the same task is assigned to an AI system—9 watt-hours—along with 50 milliliters of freshwater per operation. This pushes total cooling water demand estimates by 2027 to between 4.2 and 6.6 billion liters. Overall, the International Energy Agency expects data centers’ electricity consumption to double, reaching 945 terawatt-hours by 2030.

The Role of Renewable Energy

As renewable energy demand rises, so does the need for supply stability. Accordingly, energy storage technologies will play a central role in the coming decades, driven by two dimensions:

• Technical: production depends on solar radiation during the day and wind speeds throughout the day.
• Economic: battery storage costs have fallen to $80–90 per kilowatt-hour, making them an attractive option today.

Thus, future electricity grid planning requires an integrated energy approach. Combining a gas turbine or combined-cycle plant using natural gas with a wind farm, a solar PV plant, and a concentrated solar power (CSP) plant operating during daylight hours creates new opportunities for AI to provide scenarios for optimal operation. These scenarios account for factors such as the lowest operating cost, maximum use of natural resources, and minimal carbon emissions—allowing grid operators to choose and prioritize accordingly.

The concept of energy integration requires identifying and evaluating resources, assessing availability and reserves, and understanding their roles to build a diverse and balanced mix that meets energy needs while maximizing renewables’ contribution. Forecasting programs—covering one to three days—offer a clear picture of wind speeds and solar radiation, helping determine the thermal capacity needed to compensate for renewable fluctuations. Yet, before adding new sources to supply data centers, several urgent considerations must be addressed:

• Reviewing data center consumption patterns (peak and off-peak times), linking them with time-of-use electricity tariffs, promoting smart load shifting, leveraging demand-side management tools to reduce peaks, improve renewable reliance, and utilize excess heat.
• Scheduling flexible loads, particularly for large AI model training, during periods of abundant renewable electricity.
• Developing highly efficient air-cooling technologies to significantly reduce both water and energy consumption, given that data centers use nearly 30% of their energy for cooling alone.
• Maximizing AI’s role in managing energy use inside data centers—not only for its primary functions but also for predicting and balancing demand, cooling loads, and electricity consumption.
• Integrating energy storage technologies, since AI is expected to play dual roles in both energy system management and service provision. Likewise, storage plays a parallel role—turning intermittent resources into reliable supply while giving planners flexibility in designing electricity and heat systems for data centers.

The Governing Framework

While engineers trust their ability to face challenges and design energy systems that meet rising consumer demand—particularly the surging needs of data centers—funding authorities remain focused on project feasibility and profitability to ensure sustainability and shareholder satisfaction. This brings up the issue of fair pricing, which is guided by the following trends:

1. Tariff Pricing: Setting higher tariffs ensures better-quality networks by covering costs and profits, but it could also hinder data center and AI growth by making services more expensive. Energy costs account for around 50% of operational expenses in data centers and AI, plus another 7% for cooling.
2. Tariff Fairness: Advocates of higher energy prices for data centers and AI argue that their high consumption intensity and strong revenues justify their contribution to renewable energy and grid infrastructure. However, this approach overlooks fair cost-sharing and may create distortions, pushing investments toward markets seen as more equitable. This is often framed as “solidarity support,” meaning that heavy users subsidize lighter users—effectively providing financial support to lower-consumption groups.
3. Carbon Regulations: Environmental considerations depend on energy sources. A balanced view requires each sector to bear part of the costs of reducing fossil fuel impacts (gas, oil, coal). This highlights the importance of data centers actively participating in voluntary carbon markets and gradually shifting to sustainable energy sources.

Global Market Trends

In recent years, decision-makers worldwide have shown clear interest in governing electricity consumption in data centers, as follows:

1. European Union: In June 2025, the European Commission announced plans to issue measures limiting data centers’ energy consumption. These focus on flexible pricing that considers peak demand and Power Usage Effectiveness (PUE) to encourage renewable adoption and avoid peak loads. While details remain limited, they suggest forthcoming policies likely to take effect next year. Notably, both the Netherlands and Germany introduced local policies linking data center licenses to renewable energy consumption targets, while France offers reduced tariffs for centers meeting stricter efficiency and environmental standards.
2. United States: States such as California, Texas, and Northern Virginia have begun imposing higher peak charges on large data centers. Utilities sign “special tariff agreements” with data centers, featuring higher rates during peak hours and lower rates off-peak. These policies aim to manage grid demand while encouraging storage adoption or renewable power purchase agreements.
3. China: Since 2019, China has applied special pricing mechanisms to cryptocurrency mining—especially Bitcoin—by imposing higher tariffs than traditional industries to curb excessive energy use from non-productive activities and redirect it to priority sectors. Currently, pricing systems tied to carbon intensity are being tested.

Conclusion

Undoubtedly, the accelerating interconnection between data centers and AI on one side, and the energy sector on the other, is shaping a new era that goes beyond technological development to redefine economic systems and environmental governance. While rising demand from data centers strains electricity and water networks, renewable energy and storage technologies provide vast opportunities to ensure supply sustainability.

The real challenge lies in whether global policymakers can design fair policies that balance investment incentives, environmental protection, and equitable energy access.

Success in striking this balance will determine countries’ positions in the race for dominance in the global AI economy—where clean energy and smart technologies become two sides of the same coin, driving a constructive partnership for humanity’s future.