Opinion & Commentary

Navigating key challenges in data center development: Location, sustainability, and hardware

Navigating key challenges in data center development: Location, sustainability, and hardware
A Microsoft data center in Wyoming (Credit: Microsoft)

Contributed by Supratik Chaudhuri, Utilities Lead at Publicis Sapient

In recent years, AI has transformed numerous industries, streamlining operations and fostering innovations at an unprecedented scale. However, this rapid advancement of AI comes with significant environmental costs, primarily due to the substantial energy required for powering AI systems. Data centers need a lot of energy to keep their facilities up and running at peak capacity around the clock. Electricity consumption from data centers, AI, and the cryptocurrency sector could double by 2026, with AI alone potentially accounting for 3% to 4% of global power demand by 2030.

As AI and computing power use continue to rise, data centers face the daunting task of meeting increasing power demands without compromising energy sustainability. Failure to address this issue could lead to increased strain on utility infrastructure. While legislation like the Inflation Reduction Act offers opportunities for AI data centers to operate sustainably, more is needed to address the challenge of increasing power demand. The increased power needs of AI technologies necessitate smarter and more adaptable ways to build and operate data centers efficiently and sustainably.

Challenges associated with building AI data centers

Optimal locations for data centers are often near major cities where existing power systems have spare capacity. These locations are critical for minimizing latency and ensuring robust connectivity but usually face challenges in providing adequate spare capacity in their electric and telecom infrastructure. Urban areas typically are not ideal for accessing renewable energy sources, and solar and wind developers face long interconnection queues that struggle to keep pace with the construction timelines of data center developers.

Moreover, data centers must contend with local regulatory environments, which can vary significantly and impact the speed and cost of development. Urban sites may have stricter zoning laws, higher real estate costs, and more rigorous environmental regulations. These factors can delay projects and increase operational costs, further complicating the establishment of new data centers. Additionally, data centers must navigate grid reliability complexities, as cities, despite having robust power infrastructure, are more susceptible to grid congestion and outages. Operators must invest in advanced energy management systems and backup power solutions to mitigate these risks.

Balancing the demands for high-capacity, resilient infrastructure while adopting sustainable energy practices can inspire innovative solutions and strategic planning. Hybrid models that combine traditional and renewable energy sources, energy storage technologies, and partnerships with local utilities to enhance grid capabilities are promising avenues. By addressing these multifaceted challenges, data centers can position themselves to meet the growing demands of the digital age while advancing sustainability goals.

Innovative ways to power up data centers sustainably

By 2026, the International Energy Agency (IEA) projects that data centers, driven by AI demand, may consume as much electricity as Japan, with a population of 125 million people. The main strategy for lessening environmental impact lies in securing Power Purchase Agreements (PPAs) with renewable energy providers and innovations “behind the meter,” like advanced cooling systems and thermal storage. PPAs, which are long-term contracts between data center operators and renewable energy providers, ensure a consistent supply of green energy, help stabilize energy costs, and guarantee renewable energy procurement, reducing reliance on fossil fuels.

In addition to PPAs, data centers can invest in on-site renewable energy generation, such as solar panels or wind turbines, to reduce dependence on external power sources and provide energy security and cost savings in the long run. This can be complemented by battery storage systems, ensuring a stable energy supply even when renewable sources are intermittent. By adopting these strategies, data centers can mitigate their environmental impact, align with global sustainability goals, and support the growing demand for AI-driven technologies without compromising energy efficiency or operational performance.

Furthermore, data centers should explore innovations to reduce their carbon footprint and enhance energy efficiency “behind the meter.” One key solution is to transform cooling systems, which are among the largest energy consumers. Innovations like liquid cooling, which uses fluids to dissipate heat more efficiently, and immersion cooling, which submerges servers in a thermally conductive liquid, offer substantial energy savings while enhancing hardware performance and lifespan.

Another significant innovation is thermal storage systems, which capture excess heat from data center operations and store it for later use. This stored energy can heat nearby buildings or generate electricity during peak demand periods, enhancing energy efficiency, reducing waste, and contributing to a more resilient grid.

Lastly, modular data center designs offer a scalable and flexible construction approach, allowing operators to add capacity as needed without committing to full-scale facilities up front. These units can be deployed quicker and with less initial capital investment, incorporating the latest sustainable technologies as they become available. Modular designs can also be optimized for energy efficiency, incorporating improved insulation, efficient airflow management, and renewable energy integration from the outset.

AI-driven strategies for data center hardware optimization

The demand for advanced GPUs from top manufacturers is insatiable, complicating the sourcing process, capital costs, and delivery timelines. These high-performance GPUs are essential for training complex AI models and handling the immense computational loads required for AI-driven applications. However, the surge in demand has led to supply chain constraints, making it challenging for data centers to acquire the necessary hardware promptly and within budget. Developers with established relationships with GPU manufacturers before the AI boom have a significant advantage in securing the hardware needed to operate their facilities. These pre-existing connections can expedite procurement processes, allowing for quicker deployment of AI infrastructure.

Training AI models requires immense computational power, often utilizing thousands of GPUs for extended periods, while deployment generally requires less but still substantial resources. A study from OpenAI shows the exponential growth in the computational power required to train AI models, indicating that the amount of computing used in the largest AI training runs has been doubling approximately every 3.4 months.

Leveraging predictive analytics and AI-driven insights, data centers can anticipate demand fluctuations, adjust hardware resources, and implement dynamic resource allocation. By forecasting high and low-demand periods through historical data, real-time usage, and external events, AI enables data centers to optimize load distribution, maximize utilization, and minimize idle time. Additionally, AI can implement auto-scaling and energy optimization strategies, reducing energy consumption and operational costs. On the supply side, AI improves procurement and inventory management by enabling accurate forecasting of hardware needs. AI also identifies alternative sourcing strategies during supply chain disruptions, ensuring continuous operation.

The future of data centers lies in balancing the growing demands of AI-driven technologies with sustainable operational practices. As AI continues to transform industries, the energy demands of data centers will increase. By adopting strategies like securing Power Purchase Agreements (PPAs), investing in on-site renewable energy, and leveraging advanced cooling technologies, data centers can reduce their environmental impact while meeting power needs. Integrating AI-driven predictive analytics and resource management will be crucial for optimizing hardware usage, energy consumption, and managing supply chain constraints. By embracing these solutions, data centers can support AI growth while advancing global sustainability goals, ensuring a future that benefits the industry and the environment.

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