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arxiv: 2605.03751 · v2 · submitted 2026-05-05 · 💻 cs.CE

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Carbon-Aware Compute--Power Scheduling for AI Data Centers with Microgrid Prosumer Operations

Gaoyuan Du, Jiaxuan Li, Johnny R. Zhang, Qianyi Sun, Shiqi Wang, Xian Sun

Pith reviewed 2026-05-14 21:05 UTC · model grok-4.3

classification 💻 cs.CE
keywords carbon-aware schedulingAI data centersmicrogrid prosumerMILP optimizationworkload routingenergy storageemissions reductioncompute-power co-optimization
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The pith

A joint MILP model for scheduling AI workloads and microgrid power operations raises total operational benefit while cutting emissions compared to separate baselines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that coordinating compute decisions with energy decisions in AI data centers produces measurable gains in net operational value and lower carbon output. It does so by building a single optimization model that places rigid training jobs, routes flexible inference traffic, runs on-site generators and batteries, and handles grid exchanges under latency, power-balance, and emission caps. A sympathetic reader would care because AI infrastructure is becoming a large, controllable load that can either strain or stabilize local power systems depending on how the two are managed together. The experiments indicate that routing flexibility and storage timing are the main levers that create the extra value.

Core claim

The central claim is that a mixed-integer linear program that simultaneously optimizes heterogeneous AI workload placement and microgrid prosumer actions (local generation, storage, and bidirectional grid flows) delivers higher total operational benefit and lower emissions than either a compute-only scheduler or an energy-only scheduler on the same instances.

What carries the argument

The joint MILP formulation that treats rigid training jobs, elastic inference routing, local generation dispatch, battery state-of-charge, and carbon-budget tracking as coupled decision variables under power-balance and latency constraints.

If this is right

  • Inference-routing flexibility accounts for a large share of the total value created by the joint schedule.
  • Battery storage supplies useful time-shifting that improves both economics and emission outcomes.
  • Sites with substantial local generation see larger gains from the joint approach than grid-dependent sites.
  • The framework remains tractable for realistic numbers of jobs and time periods while respecting all listed constraints.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same joint-model structure could be used to study how carbon budgets propagate across a fleet of data centers when some sites have different renewable profiles.
  • Operators could test whether relaxing latency constraints on a subset of inference traffic further amplifies the reported benefits.
  • The model offers a concrete way to quantify the grid-stabilization service that flexible AI loads could provide to utilities.

Load-bearing premise

The synthetic test cases used for experiments capture the main interactions between workload flexibility, prosumer energy operations, and the listed operational constraints well enough that the observed gains would appear on real systems.

What would settle it

Running the same joint model on actual operational traces from a multi-site AI operator and finding that the reported benefit and emission reductions disappear or reverse.

Figures

Figures reproduced from arXiv: 2605.03751 by Gaoyuan Du, Jiaxuan Li, Johnny R. Zhang, Qianyi Sun, Shiqi Wang, Xian Sun.

Figure 1
Figure 1. Figure 1: Sensitivity and scalability of the proposed MILP. view at source ↗
read the original abstract

AI data centers are increasingly becoming tightly coupled compute--energy systems, where workload placement, cooling demand, electricity procurement, storage operation, and carbon emissions interact over time. This paper studies carbon-aware compute--power scheduling for geographically distributed AI data centers with microgrid prosumer capabilities. We propose a mixed-integer linear programming (MILP) framework that jointly schedules rigid training jobs, routes elastic inference workloads, dispatches local generation and battery storage, and manages bidirectional grid interaction under latency, continuity, power-balance, and carbon-budget constraints. The model captures two key features of emerging AI infrastructure: heterogeneous workload flexibility and site-level energy prosumer operation. Experiments on synthetic yet practically motivated instances show that the proposed joint MILP substantially improves total operational benefit over compute-only and energy-only baselines while reducing emissions. The results further indicate that inference-routing flexibility is a major source of value, battery storage provides useful temporal flexibility, and local-generation-rich settings are particularly favorable. The framework provides a tractable optimization abstraction for sustainable and grid-interactive AI data centers.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript proposes a mixed-integer linear programming (MILP) framework for carbon-aware joint scheduling of compute workloads (rigid training jobs and elastic inference) and power operations (local generation, battery storage, bidirectional grid interaction) in geographically distributed AI data centers with microgrid prosumer capabilities. The model incorporates constraints on latency, continuity, power balance, and carbon budgets. Experiments on synthetic yet practically motivated instances claim that the joint optimization substantially increases total operational benefit relative to compute-only and energy-only baselines while also reducing emissions, highlighting the value of inference routing flexibility and battery storage.

Significance. If the synthetic results generalize, the framework could provide a tractable optimization abstraction for AI data center operators to co-optimize economic performance and sustainability under carbon constraints and renewable integration. It addresses timely issues in grid-interactive computing by treating heterogeneous workload flexibility and site-level prosumer operations as coupled decisions.

major comments (1)
  1. [Experiments] Experiments section: The central claim of substantial improvements in operational benefit and emissions reduction rests entirely on synthetic instances generated from assumed distributions. No calibration against real data-center workload or energy traces is reported, nor is there out-of-sample testing on held-out operational data. This leaves open whether the observed gaps versus baselines would persist when workload-energy correlations, battery degradation, or tariff structures deviate from the modeled assumptions.
minor comments (2)
  1. [Abstract] Abstract: Including at least one concrete quantitative result (e.g., average percentage improvement in benefit or emission reduction) would better convey the magnitude of the claimed gains.
  2. [Model] Model formulation: Clarify the exact definition and units of the carbon-budget constraint and how it interacts with the objective weights; the current description leaves the scaling between benefit and emissions terms ambiguous.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment regarding the experimental validation below and outline planned revisions to improve transparency.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: The central claim of substantial improvements in operational benefit and emissions reduction rests entirely on synthetic instances generated from assumed distributions. No calibration against real data-center workload or energy traces is reported, nor is there out-of-sample testing on held-out operational data. This leaves open whether the observed gaps versus baselines would persist when workload-energy correlations, battery degradation, or tariff structures deviate from the modeled assumptions.

    Authors: We agree that our experiments rely exclusively on synthetic instances and do not include calibration against real data-center workload or energy traces, nor out-of-sample testing on held-out data. The instances were constructed using parameter distributions informed by publicly available industry reports, academic studies on AI workloads, renewable generation profiles, and electricity tariffs to reflect realistic operating conditions. We acknowledge that this leaves open questions about robustness to deviations in workload-energy correlations, battery degradation, or tariff structures. In the revised manuscript we will expand the Experiments section with a detailed description of the instance generation methodology (including all distributions and parameter sources) and add a dedicated Limitations subsection. This subsection will discuss the modeling assumptions, potential sensitivity to the factors raised by the referee, and the practical difficulties of obtaining synchronized real-world traces that capture both compute and microgrid operations at the required resolution. These changes will not alter the core results but will provide readers with a clearer basis for assessing generalizability. revision: partial

Circularity Check

0 steps flagged

No significant circularity in MILP formulation or experimental evaluation

full rationale

The paper proposes a new MILP optimization model that jointly schedules workloads, generation, storage, and grid interactions under explicit constraints. The claimed improvements in operational benefit and emissions are obtained by solving this model to optimality on synthetic instances; no equation reduces to a fitted parameter by construction, no prediction is statistically forced from a subset of data, and no load-bearing premise relies on self-citation chains. The derivation is therefore self-contained: the model definition plus the instance generator directly produce the reported outcomes without circular reduction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The claim rests on standard MILP modeling assumptions and the representativeness of synthetic test cases; no new physical entities are introduced.

free parameters (1)
  • Objective weights for benefit versus emissions
    Weights balancing operational benefit and carbon cost are required in any MILP objective but not numerically specified.
axioms (1)
  • domain assumption All relevant constraints (latency, continuity, power balance, carbon budget) can be expressed as linear or mixed-integer linear inequalities
    Invoked when the abstract states the model captures these constraints within a MILP framework.

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