Synergies between AI Computing and Power Systems: Metrics, Scheduling, and Resilience
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909947994832896 |
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| author | Pourahmadi, Farzaneh Corradi, Olivier Pinson, Pierre |
| author_facet | Pourahmadi, Farzaneh Corradi, Olivier Pinson, Pierre |
| contents | In this paper, we first clarify the concepts of green AI versus frugal AI, positioning frugality as efficiency by design and green AI as transparency and accountability. We then argue that these approaches, while complementary, are insufficient without a shared quantitative foundation that links AI computing to power system contexts. This motivates the development of standardized carbon metrics as a bridge between algorithmic decisions and their physical consequences. We next embed these signals into scheduling and planning frameworks, presenting two architectures: (i) an iterative signal-response loop for real-time operations, and (ii) an integrated optimization that learns and encodes flexible-load behavior for long-term planning. Finally, we show how the same coordination stack supports resilience, enabling signals to shift from emissions-first to stability-first during stress events, providing targeted relief and faster restoration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Synergies between AI Computing and Power Systems: Metrics, Scheduling, and Resilience Pourahmadi, Farzaneh Corradi, Olivier Pinson, Pierre Systems and Control In this paper, we first clarify the concepts of green AI versus frugal AI, positioning frugality as efficiency by design and green AI as transparency and accountability. We then argue that these approaches, while complementary, are insufficient without a shared quantitative foundation that links AI computing to power system contexts. This motivates the development of standardized carbon metrics as a bridge between algorithmic decisions and their physical consequences. We next embed these signals into scheduling and planning frameworks, presenting two architectures: (i) an iterative signal-response loop for real-time operations, and (ii) an integrated optimization that learns and encodes flexible-load behavior for long-term planning. Finally, we show how the same coordination stack supports resilience, enabling signals to shift from emissions-first to stability-first during stress events, providing targeted relief and faster restoration. |
| title | Synergies between AI Computing and Power Systems: Metrics, Scheduling, and Resilience |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.07001 |