AI Data Center Power Supply
In AI Data Center Power Supply, you'll learn ...
- The structure and operational purpose of modern AI data centers and their role in large-scale machine learning and model training
- The major drivers of electrical demand in AI data centers, including GPU density, cooling systems, and power redundancy requirements
- The available power supply strategies for AI data centers, including grid interconnection, on-site generation, and energy storage
- How grid infrastructure, policy, and market dynamics influence the feasibility and timing of large-scale AI data center development
Overview
This course takes a detailed look at the booming data center growth in the U.S. The course focuses on the typical power supply needs of AI data centers and how data center developers are addressing that challenge. Different options are discussed including transmission grid supply and on-site power options including fossil fuel generation, battery energy storage, fuel cells, SMRs, and solar. Related to grid supply, the course will give a perspective on how utilities are attempting to address the overwhelming power demand growth associated with data centers. The course will also provide current policy developments that will influence the data center market.
Specific Knowledge or Skill Obtained
This course teaches the following specific knowledge and skills:
- The core physical and technical components of AI data centers, including buildings, racks, computing hardware, cooling systems, and power infrastructure
- The relationship between GPU architectures, rack density, and rapidly increasing power and thermal loads
- The principles of air-based and liquid-based cooling systems and their impact on efficiency, water use, and Power Usage Effectiveness (PUE)
- The configuration and function of data center power infrastructure elements such as substations, transformers, UPS systems, and backup generators
- The evaluation of grid connection options, including large-load interconnection processes, transmission constraints, and utility planning requirements
- The characteristics of AI data center load profiles and how load variability affects grid stability and power system design
- The role of on-site generation technologies, including natural gas turbines, battery storage, and hybrid power systems, in supporting reliability and scalability
- The economic scale and financing considerations associated with hyper-scale AI data centers and their power supply investments
- The influence of regional generation mixes, renewable energy procurement, and virtual power purchase agreements on data center power strategies
- How future advances in hardware efficiency, cooling technology, and policy frameworks may shape the evolution of AI data center power supply solutions
Certificate of Completion
You will be able to immediately print a certificate of completion after passing a multiple-choice quiz consisting of 10 questions. PDH credits are not awarded until the course is completed and quiz is passed.
| This course is applicable to professional engineers in: | ||
| Alabama (P.E.) | Alaska (P.E.) | Arkansas (P.E.) |
| Delaware (P.E.) | District of Columbia (P.E.) | Florida (P.E. Area of Practice) |
| Georgia (P.E.) | Idaho (P.E.) | Illinois (P.E.) |
| Illinois (S.E.) | Indiana (P.E.) | Iowa (P.E.) |
| Kansas (P.E.) | Kentucky (P.E.) | Louisiana (P.E.) |
| Maine (P.E.) | Maryland (P.E.) | Michigan (P.E.) |
| Minnesota (P.E.) | Mississippi (P.E.) | Missouri (P.E.) |
| Montana (P.E.) | Nebraska (P.E.) | Nevada (P.E.) |
| New Hampshire (P.E.) | New Jersey (P.E.) | New Mexico (P.E.) |
| New York (P.E.) | North Carolina (P.E.) | North Dakota (P.E.) |
| Ohio (P.E. Self-Paced) | Oklahoma (P.E.) | Oregon (P.E.) |
| Pennsylvania (P.E.) | South Carolina (P.E.) | South Dakota (P.E.) |
| Tennessee (P.E.) | Texas (P.E.) | Utah (P.E.) |
| Vermont (P.E.) | Virginia (P.E.) | West Virginia (P.E.) |
| Wisconsin (P.E.) | Wyoming (P.E.) | |

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