{"id":"fe2bb05e-b739-4bd3-89f9-ef9f9d785dd4","arxiv_id":"2605.16190","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"A day-ahead co-optimization model for data-center workloads and degradation-aware BESS dispatch under peak-load and ramp limits produces feasible schedules whose storage value rises by a factor of two or more when interconnection constraints bind.","lead":"The paper develops a co-optimization framework that jointly schedules deadline-constrained computing workloads using DVFS and manages co-located battery storage to respect grid interconnection limits on peak power and ramps while participating in ancillary services. Smart readers in energy and computing should note it because the results indicate storage value can double when those limits bind, potentially expanding data-center headroom under grid stress.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Real-time DVFS and workload scheduling costs unmodeled, most critical when interconnection limits bind","rationale":"The reader's weakest assumption directly identifies the load-bearing point for the quantitative claims under stressed conditions. The abstract's emphasis on real-world traces and the revised text on DVFS and workload composition make this assumption the least secure link; the full model formulation is not shown here but the claim's validity turns on whether the real-time execution premise survives scrutiny.","tokens_in":1850,"tokens_out":350,"duration_ms":32015,"concrete_test":"Take the day-ahead schedule from the stressed-case study (peak-load and ramping limits active), replay it in a discrete-event simulator that applies measured DVFS transition latencies, job queueing delays, and thermal throttling; compare realized incompletion fraction and BESS degradation against the optimization output. If incompletion rises by more than 10 % or effective BESS value drops below 1.5× baseline, the headline benefit quantification does not hold.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The central claim rests on case-study results showing feasible day-ahead schedules and BESS value rising by a factor of two or more under stressed peak-load and ramping limits, driven by reduced schedulable-workload incompletion. This quantification assumes that the jointly optimized workload scheduling and DVFS decisions can be executed in real time with negligible unmodeled performance, delay, or reliability penalties. Under binding interconnection constraints the model uses these levers to create headroom; any real-time cost that increases incompletion or forces additional BESS cycling would directly erode the reported factor-of-two benefit and the claim that processor-level control is a material flexibility lever.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript develops a robust co-optimization framework for day-ahead operation of data centers with co-located BESS under utility-imposed interconnection limits on peak load and ramping. It jointly optimizes deadline-constrained workloads through scheduling and DVFS together with degradation-aware BESS dispatch to minimize operating costs while enabling ancillary-service participation. Case studies using real-world market and workload data report feasible schedules, with BESS value rising by a factor of two or more under stressed peak-load and ramping conditions (driven by reduced schedulable-workload incompletion) and position DVFS as a material flexibility lever under tight limits.","tokens_in":1971,"tokens_out":636,"duration_ms":46487,"significance":"If the central modeling assumptions hold, the work demonstrates that compute-storage co-optimization can materially expand data-center operational headroom and grid value precisely when interconnection capacity is scarcest. The use of real-world traces strengthens practical relevance and the finding that benefits amplify under binding constraints offers a concrete, falsifiable prediction for operators. These elements would be notable contributions to the literature on flexible demand and storage co-location if the quantitative claims survive additional validation.","major_comments":[{"comment":"Abstract and Case Studies section: The claim that daily BESS value increases by a factor of two or more under stressed peak-load and ramping limits rests on the assumption that jointly optimized workload scheduling and DVFS decisions can be executed in real time with negligible unmodeled performance, delay, or reliability penalties. When interconnection constraints bind, the model relies on these levers to create headroom; any real-time cost that increases incompletion or forces extra BESS cycling would directly erode the reported benefit and the assertion that processor-level control is a material flexibility lever.","section":"Abstract and Case Studies"},{"comment":"Case Studies section: The quantitative results (feasible schedules, factor-of-two value increase, >25% cost impact from workload composition) are presented without error bars, sensitivity tables on key parameters such as DVFS cost coefficients or workload flexibility ratios, or explicit out-of-sample validation. This makes it difficult to assess whether the reported benefits are robust to the modeling choices that underpin the central claim.","section":"Case Studies"}],"minor_comments":[{"comment":"Abstract: The text contains visible LaTeX revision commands (e.g., “revise{by BESS actions…}” and “revise{Additionally, DVFS studies…}”) that should be removed before final submission.","section":"Abstract"},{"comment":"Model formulation: Notation distinguishing schedulable versus non-schedulable workloads and the precise definition of interconnection ramping limits could be introduced earlier and used consistently to improve readability.","section":"Model formulation"}],"recommendation":"major_revision","confidential_remarks":"The manuscript fits the scope of eess.SY and addresses a timely topic, but the authors should more explicitly differentiate the contribution from existing data-center demand-response and BESS co-location studies to strengthen the novelty claim."},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive and detailed feedback. The comments highlight important considerations regarding model assumptions and result robustness. We address each major comment below and outline the revisions we will make to strengthen the manuscript.","responses":[{"response":"We agree that the day-ahead co-optimization framework implicitly assumes that the computed workload schedules and DVFS decisions can be implemented in real time without significant unmodeled penalties or reliability impacts. The model focuses on generating feasible day-ahead schedules under interconnection constraints, with BESS actions explicitly reducing potential workload incompletion. Real-time execution dynamics and associated costs lie outside the current scope. In the revised manuscript we will add an explicit discussion of this modeling assumption and its implications in the Case Studies section, and we have updated the abstract to qualify the reported BESS value increase under the modeled conditions.","revision_made":"partial","referee_comment":"[Abstract and Case Studies] Abstract and Case Studies section: The claim that daily BESS value increases by a factor of two or more under stressed peak-load and ramping limits rests on the assumption that jointly optimized workload scheduling and DVFS decisions can be executed in real time with negligible unmodeled performance, delay, or reliability penalties. When interconnection constraints bind, the model relies on these levers to create headroom; any real-time cost that increases incompletion or forces extra BESS cycling would directly erode the reported benefit and the assertion that processor-level control is a material flexibility lever."},{"response":"We concur that additional sensitivity analysis would improve assessment of robustness. In the revised Case Studies section we will include sensitivity tables for the DVFS cost coefficients and workload flexibility ratios. The presented results derive from deterministic optimization using real-world traces; we will add a short discussion noting the deterministic formulation and identifying stochastic extensions or out-of-sample testing as valuable future directions. These changes will better support the quantitative claims.","revision_made":"yes","referee_comment":"[Case Studies] Case Studies section: The quantitative results (feasible schedules, factor-of-two value increase, >25% cost impact from workload composition) are presented without error bars, sensitivity tables on key parameters such as DVFS cost coefficients or workload flexibility ratios, or explicit out-of-sample validation. This makes it difficult to assess whether the reported benefits are robust to the modeling choices that underpin the central claim."}],"tokens_in":1559,"tokens_out":503,"duration_ms":36917,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this paper builds a day-ahead optimization model that jointly schedules deadline-constrained compute jobs, applies DVFS, and dispatches co-located batteries while respecting utility peak and ramp interconnection limits. Their real-data case studies show feasible schedules and indicate that BESS daily value more than doubles when those limits bind, largely by cutting potential workload incompletion.","headline":"Co-optimization of workloads, DVFS, and BESS under grid limits yields doubled BESS value in stressed case studies, but real-time execution costs remain unmodeled.","tokens_in":2472,"tokens_out":154,"would_cite":false,"duration_ms":43876,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":{"model":"grok-4.3","evidence":[{"relation":"unclear","rs_module":"IndisputableMonolith/Cost/FunctionalEquation.lean","rs_theorem":"washburn_uniqueness_aczel","paper_passage":"The model jointly considers deadline-constrained computing workloads, managed through workload scheduling and dynamic voltage and frequency scaling (DVFS), together with degradation-aware BESS dispatch"},{"relation":"unclear","rs_module":"IndisputableMonolith/Foundation/ArithmeticFromLogic.lean","rs_theorem":"LogicNat ≃ Nat recovery","paper_passage":"reformulations of our model for both continuous and discrete DVFS settings, yielding tractable linear and mixed-integer linear optimization models"}],"headline":"Practical data-center/BESS co-optimization MILP with DVFS and workload scheduling has no structural overlap with RS forcing chain","alignment":"orthogonal","rationale":"The paper's core is a two-stage robust MILP (Eq. 5) jointly optimizing discrete DVFS levels (z_l,t binaries, P-states), schedulable-job execution rates w_j,t, BESS dispatch with cycle limits (11f), and ancillary-service offers under interconnection constraints (2). All machinery is standard power-systems scheduling with degradation penalties and scenario-based robustness. No J-cost functional, reciprocal symmetry, golden-ratio identities, 8-tick periodicity, or parameter-free constant derivation appears. The domain (grid-constrained data-center flexibility) lies outside the RS forcing theorems.","tokens_in":60147,"confidence":"high","tokens_out":340,"duration_ms":13449,"cache_read_input_tokens":38528,"cache_creation_input_tokens":0},"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Co-optimizing data center workloads with battery storage meets grid limits and doubles BESS value under stress.","keywords":["data centers","battery energy storage","co-optimization","grid interconnection limits","workload scheduling","DVFS","ancillary services","peak load management"],"falsifier":"Field implementation of the optimized day-ahead schedules in an operating data center that measures actual workload completion rates, energy costs, grid compliance, and any performance or reliability deviations from model predictions.","tokens_in":2736,"feed_emoji":"","tokens_out":734,"duration_ms":50472,"temperature":0.7,"pith_summary":"The paper develops a co-optimization framework for data centers with co-located battery energy storage systems under utility interconnection limits on peak load and ramping. It jointly manages deadline-constrained computing workloads through scheduling and dynamic voltage and frequency scaling together with degradation-aware BESS dispatch to reduce costs and enable ancillary service participation. Case studies using real-world market and workload data confirm that the framework produces feasible day-ahead schedules across operating conditions. Benefits increase substantially when interconnection constraints bind, with daily BESS value rising by a factor of two or more as storage actions reduce the risk of schedulable workload incompletion while respecting limits.","feed_headline":"Data centers double battery value by co-optimizing compute and storage","feed_subtitle":"Under tight grid peak and ramp limits, BESS value more than doubles by helping complete more workloads while staying compliant, per real-trx","key_machinery":"The day-ahead co-optimization model that jointly optimizes deadline-constrained workload scheduling and DVFS with degradation-aware BESS dispatch subject to peak-load and ramping interconnection limits.","core_discovery":"The authors establish that a robust day-ahead co-optimization model integrating workload scheduling, DVFS, and BESS dispatch yields feasible schedules that optimize operations and grid services, with BESS value increasing by a factor of two or more under stressed peak-load and ramping limits primarily by mitigating potential incompletion in schedulable workloads while complying with constraints; under baseline conditions value stems from ancillary participation and improved management, while tight peak caps make workload composition matter such that non-schedulable jobs raise costs by more than 25 percent and DVFS emerges as an additional flexibility lever.","pith_inferences":["Data centers could more readily locate in regions with constrained grid capacity if the approach is adopted.","The same co-optimization logic could extend to other flexible loads paired with storage such as EV charging hubs.","Real-time adjustments beyond the day-ahead horizon might yield further gains provided performance costs stay low.","Grid operators could treat data centers as more reliable providers of flexibility services."],"forward_implications":["Feasible day-ahead schedules are produced across a range of operating conditions.","BESS daily value increases by a factor of two or more when peak-load and ramping constraints bind.","Under tight peak-load caps a higher share of non-schedulable jobs raises operating cost by more than 25 percent relative to flexible mixes.","DVFS serves as a material flexibility lever when load limits are tight.","Coordinated compute-storage flexibility expands operational headroom and grid value of data centers."],"fun_headline_variants":["Tight grid limits more than double BESS value for data centers","Co-optimization more than doubles BESS value when peak limits bind","Under stressed limits co-optimization doubles data center BESS value","Workload scheduling doubles BESS value under interconnection constraints"],"cache_read_input_tokens":64,"weakest_assumption_plain":"Workload scheduling and DVFS can be executed in real time with negligible unmodeled performance or reliability costs, and the chosen real-world market and workload traces represent conditions where interconnection limits actually bind.","fun_headline_variants_meta":{"raw":{"variants":["Tight grid limits more than double BESS value for data centers","Co-optimization more than doubles BESS value when peak limits bind","Under stressed limits co-optimization doubles data center BESS value","Workload scheduling doubles BESS value under interconnection constraints"]},"model":"grok-4.3","cost_usd":0.011752,"raw_usage":{"total_tokens":5126,"prompt_tokens":797,"num_sources_used":0,"completion_tokens":68,"cost_in_usd_ticks":117515500,"prompt_tokens_details":{"text_tokens":797,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":4261,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":797,"tokens_out":68,"duration_ms":55577,"temperature":1.0,"reasoning_tokens":4261,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-20T15:52:24.022099+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Field implementation of the optimized day-ahead schedules in an operating data center that measures actual workload completion rates, energy costs, grid compliance, and any performance or reliability deviations from model predictions.","supporting_citations":[],"review_version":1}