AI-focused HPC Data Centers Can Provide More Power Grid Flexibility and at Lower Cost
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| Format: | Preprint |
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2024
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| _version_ | 1866916522844225536 |
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| author | Zhou, Yihong Paredes, Angel Essayeh, Chaimaa Morstyn, Thomas |
| author_facet | Zhou, Yihong Paredes, Angel Essayeh, Chaimaa Morstyn, Thomas |
| contents | The recent growth of Artificial Intelligence (AI), particularly large language models, requires energy-demanding high-performance computing (HPC) data centers, which poses a significant burden on power system capacity. Scheduling data center computing jobs to manage power demand can alleviate network stress with minimal infrastructure investment and contribute to fast time-scale power system balancing. This study, for the first time, comprehensively analyzes the capability and cost of grid flexibility provision by GPU-heavy AI-focused HPC data centers, along with a comparison with CPU-heavy general-purpose HPC data centers traditionally used for scientific computing. A data center flexibility cost model is proposed that accounts for the value of computing. Using real-world computing traces from 7 AI-focused HPC data centers and 7 general-purpose HPC data centers, along with computing prices from 3 cloud platforms, we find that AI-focused HPC data centers can offer greater flexibility at 50% lower cost compared to general-purpose HPC data centers for a range of power system services. By comparing the cost to flexibility market prices, we illustrate the financial profitability of flexibility provision for AI-focused HPC data centers. Finally, our flexibility and cost estimates can be scaled using parameters of other data centers through algebraic operations, avoiding the need for re-optimization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_17435 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-focused HPC Data Centers Can Provide More Power Grid Flexibility and at Lower Cost Zhou, Yihong Paredes, Angel Essayeh, Chaimaa Morstyn, Thomas Systems and Control Distributed, Parallel, and Cluster Computing The recent growth of Artificial Intelligence (AI), particularly large language models, requires energy-demanding high-performance computing (HPC) data centers, which poses a significant burden on power system capacity. Scheduling data center computing jobs to manage power demand can alleviate network stress with minimal infrastructure investment and contribute to fast time-scale power system balancing. This study, for the first time, comprehensively analyzes the capability and cost of grid flexibility provision by GPU-heavy AI-focused HPC data centers, along with a comparison with CPU-heavy general-purpose HPC data centers traditionally used for scientific computing. A data center flexibility cost model is proposed that accounts for the value of computing. Using real-world computing traces from 7 AI-focused HPC data centers and 7 general-purpose HPC data centers, along with computing prices from 3 cloud platforms, we find that AI-focused HPC data centers can offer greater flexibility at 50% lower cost compared to general-purpose HPC data centers for a range of power system services. By comparing the cost to flexibility market prices, we illustrate the financial profitability of flexibility provision for AI-focused HPC data centers. Finally, our flexibility and cost estimates can be scaled using parameters of other data centers through algebraic operations, avoiding the need for re-optimization. |
| title | AI-focused HPC Data Centers Can Provide More Power Grid Flexibility and at Lower Cost |
| topic | Systems and Control Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2410.17435 |