Sustainable Grid through Distributed Data Centers: Spinning AI Demand for Grid Stabilization and Optimization
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917977326092288 |
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| author | Evans, Scott C Dahlin, Nathan Ndiaye, Ibrahima Ekanayake, Sachini Piyoni Duncan, Alexander Rose, Blake Huang, Hao |
| author_facet | Evans, Scott C Dahlin, Nathan Ndiaye, Ibrahima Ekanayake, Sachini Piyoni Duncan, Alexander Rose, Blake Huang, Hao |
| contents | We propose a disruptive paradigm to actively place and schedule TWhrs of parallel AI jobs strategically on the grid, at distributed, grid-aware high performance compute data centers (HPC) capable of using their massive power and energy load to stabilize the grid while reducing grid build-out requirements, maximizing use of renewable energy, and reducing Green House Gas (GHG) emissions. Our approach will enable the creation of new, value adding markets for spinning compute demand, providing market based incentives that will drive the joint optimization of energy and learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sustainable Grid through Distributed Data Centers: Spinning AI Demand for Grid Stabilization and Optimization Evans, Scott C Dahlin, Nathan Ndiaye, Ibrahima Ekanayake, Sachini Piyoni Duncan, Alexander Rose, Blake Huang, Hao Distributed, Parallel, and Cluster Computing We propose a disruptive paradigm to actively place and schedule TWhrs of parallel AI jobs strategically on the grid, at distributed, grid-aware high performance compute data centers (HPC) capable of using their massive power and energy load to stabilize the grid while reducing grid build-out requirements, maximizing use of renewable energy, and reducing Green House Gas (GHG) emissions. Our approach will enable the creation of new, value adding markets for spinning compute demand, providing market based incentives that will drive the joint optimization of energy and learning. |
| title | Sustainable Grid through Distributed Data Centers: Spinning AI Demand for Grid Stabilization and Optimization |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.03663 |