Carbon-Aware Data Center Workload Allocation: Emission Disclosure, Capacity Leasing, and Contract Reshuffling

[2602.19310] Carbon-Aware Data Center Workload Allocation: Emission Disclosure, Capacity Leasing, and Contract Reshuffling Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Systems and Control arXiv:2602.19310 (eess) [Submitted on 22 Feb 2026 ( v1 ), last revised 24 Aug 2026 (this version, v3)] Title: Carbon-Aware Data Center Workload Allocation: Emission Disclosure, Capacity Leasing, and Contract Reshuffling Authors: Yihsu Chen , Abel Souza , Fargol Nematkhah , Andrew L. Liu View PDF HTML (experimental) Abstract: The rapid adoption of AI has driven rapid growth in computational demand, with large language models (LLMs) at the forefront since ChatGPT's debut in 2022. Meanwhile, large amounts of renewable energy are ultimately curtailed due to transmission congestion and inadequate demand. This work develops a power market model that allows hyperscalers to spatially migrate LLM inference workloads to geo-distributed modular datacenters (MDCs) co-located with renewable generation at the edge of the network. We introduce the optimization problems faced by the hyperscaler and MDCs in addition to consumers, producers, and the electric grid operator, where the hyperscaler leases MDC capacity while ensuring that required service level objectives (SLOs) are met. The overall market model is formulated as a complementarity problem, for which we establish equilibrium existence and uniqueness of certain aggregate market quantities. We further show that bilateral contract allocations can vary while preserving the same physical market outcome, so cleaner contract-attributed procurement need not imply additional clean generation. Applying the model to the IEEE RTS-24 bus system, we find that even when MDCs disclose the CO$_2$ emissions associated with their energy supply, renting less polluting MDCs yields limited system emission reductions because of \textit{contract reshuffling}. This effect can be mitigated when conventional loads are supplied through forward contracts such as power purchase agreements. Interestingly, this also reduces system congestion as the hyperscaler becomes increasingly cost-aware. Subjects: Systems and Control (eess.SY) Cite as: arXiv:2602.19310 [eess.SY] (or arXiv:2602.19310v3 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2602.19310 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Andrew L. Liu [ view email ] [v1] Sun, 22 Feb 2026 19:17:18 UTC (209 KB) [v2] Fri, 14 Aug 2026 18:19:16 UTC (19,850 KB) [v3] Mon, 24 Aug 2026 22:12:11 UTC (167 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.SY < prev | next > new | recent | 2026-02 Change to browse by: cs cs.SY eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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