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arxiv: 2602.03070 · v5 · pith:7Y4BRX3Onew · submitted 2026-02-03 · 📡 eess.SY · cs.SE· cs.SY

ProOPF: Benchmarking and Improving LLMs for Professional-Grade Power Systems Optimization Modeling

Chao Shen , Zihan Guo , Xu Wan , Zhenghao Yang , Yifan Zhang , Wengi Huang , Jie Song , Zongyan Zhang
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Mingyang Sun
This is my paper

Pith reviewed 2026-05-25 07:32 UTC · model grok-4.3

classification 📡 eess.SY cs.SEcs.SY
keywords optimal power flowLLM evaluationpower systems optimizationnatural language to optimization modelbenchmark datasetOPF modeling
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The pith

A dataset of 12,000 instances and a benchmark of 121 expert test cases enable evaluation of LLMs on professional optimal power flow modeling.

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

The paper introduces ProOPF-D and ProOPF-B to address the lack of rigorous benchmarks for using large language models to turn natural-language operational requirements into executable optimal power flow models. ProOPF-D supplies 12,000 paired examples of requests, parameter changes, and structural extensions to a base OPF problem along with working code. ProOPF-B supplies 121 expert-annotated cases with ground-truth implementations for end-to-end testing in both concrete and abstract modeling settings. If the benchmark works, it would let researchers measure how well current LLMs can automate dispatch adjustments that power-system operators now perform manually under renewable-driven uncertainty.

Core claim

We introduce ProOPF-D and ProOPF-B, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expert-annotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes.

What carries the argument

ProOPF-D (12K NL-to-OPF instances) and ProOPF-B (121 expert-annotated test cases with ground-truth code), which together support systematic measurement of LLM performance on professional-grade power-system optimization tasks.

If this is right

  • LLMs can be measured for their ability to translate natural-language dispatch requests into both concrete numerical adjustments and abstract structural changes to an OPF formulation.
  • The benchmark separates concrete and abstract modeling regimes, allowing targeted assessment of where current models succeed or fail.
  • Executable ground-truth code for every test case makes automatic verification of generated models possible without manual inspection.
  • The 12K training instances in ProOPF-D supply paired data that can be used to fine-tune or prompt-tune models for this specific domain.

Where Pith is reading between the lines

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

  • If models that perform well on ProOPF-B also reduce the time operators spend rewriting OPF formulations during sudden renewable shifts, the benchmark would indirectly support faster grid adaptation.
  • The approach could be extended to other power-system problems such as unit commitment or contingency analysis by creating analogous NL-to-code pairs.
  • A model that passes ProOPF-B might still require human oversight for safety-critical edge cases not captured in the 121 tests.

Load-bearing premise

The 121 expert-annotated test cases in ProOPF-B are representative of the full range of professional-grade OPF modeling tasks that arise in operational power-system workflows.

What would settle it

Run the 121 ProOPF-B cases on multiple LLMs and then test the same models on a fresh collection of real operator requests drawn from actual control-room logs; if the models that score highest on ProOPF-B produce systematically incorrect or infeasible models on the new requests, the benchmark does not capture professional-grade performance.

Figures

Figures reproduced from arXiv: 2602.03070 by Chao Shen, Jie Song, Mingyang Sun, Wengi Huang, Xu Wan, Yifan Zhang, Zhenghao Yang, Zihan Guo, Zongyan Zhang.

Figure 1
Figure 1. Figure 1: From cross-domain to within-domain generalization in LLM-based optimization. Existing works emphasize coarse-grained generalization across heterogeneous optimization tasks, whereas ProOPF-B/D targets fine-grained, within-domain generalization in OPF through parametric and structural formulation modifications. Yang et al., 2025b). However, these pipelines exhibit three limitations: 1) they focus only on mat… view at source ↗
Figure 2
Figure 2. Figure 2: The ProOPF-D dataset construction pipeline. L1 generates samples by directly instantiating OPF models from explicitly specified parameter patches. L2 synthesizes scenario-driven samples by mapping qualitative operational descriptions to parameter modification directions using expert-curated scenario trees. L3 extends the base OPF formulation with expert-designed structural variants combined with explicit p… view at source ↗
Figure 3
Figure 3. Figure 3: Expert-curated scenario trees for L2 synthesis. Top: Hi￾erarchical structure from event-level (E-level) through mechanism￾level (M-level) nodes to leaf nodes encoding parameter types and modification trends. Bottom: Retrieval process where Retrieve(T | par(δk), dir(δk)) matches parameter patch δk to leaf nodes, and the root-to-leaf path forms scenario fragment ck. This substitution prevents the leakage of … view at source ↗
Figure 4
Figure 4. Figure 4: Six-Dimensional Capability Radar Chart. Each axis represents a fundamental competency in OPF modeling (see Appendix G for detailed defini￾tions). All values are expressed as percentages. plummeting by 50% to over 85% across leading baselines, indicating that current models depend heavily on in-context scaffolding to navigate topological constraints. Diagnostic Failure Analysis. Here we decompose capabil￾it… view at source ↗
Figure 5
Figure 5. Figure 5: Schematic comparison of evaluation workflows for concrete (Levels 1/3) and abstract (Levels 2/4) OPF modeling in ProOPF-B, illustrating the key difference in validation procedures. 47 [PITH_FULL_IMAGE:figures/full_fig_p047_5.png] view at source ↗
read the original abstract

Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural-language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse-grained cross-domain generalization, offering limited, rigorous evaluation in power-system settings, particularly for Optimal Power Flow (OPF). We therefore introduce \textbf{ProOPF-D} and \textbf{ProOPF-B}, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expert-annotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes.

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

2 major / 0 minor

Summary. The paper claims to address a gap in LLM evaluation for power-systems optimization by introducing ProOPF-D (12K NL-to-OPF instances with parameter adjustments, structural extensions to a canonical OPF, and executable code) and ProOPF-B (121 expert-annotated test cases with ground-truth code) to enable end-to-end assessment of LLMs under concrete and abstract OPF modeling regimes, motivated by renewable-induced uncertainty in near-real-time dispatch workflows.

Significance. If the benchmark construction is shown to be representative and rigorously validated, the artifacts would provide a domain-specific resource for measuring LLM capabilities in translating operational requirements into executable OPF models, a setting where existing cross-domain benchmarks offer limited coverage; the inclusion of executable implementations is a positive feature for reproducibility.

major comments (2)
  1. [Abstract] Abstract: The central claim that ProOPF-B's 121 expert-annotated cases enable rigorous end-to-end evaluation of LLMs for professional-grade OPF modeling is load-bearing on the assumption that these cases are representative of operational workflows (network scale, uncertainty handling, real-time constraint changes, multi-period extensions), yet no quantitative coverage analysis, mapping to utility workflows, or inter-annotator agreement statistics are reported.
  2. [Abstract] Abstract: The description of ProOPF-B supplies no information on validation procedures for the expert annotations or baseline LLM performance on the test cases, which is required to judge whether the artifacts support the intended claims about professional-grade modeling.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the benchmark's validation and representativeness. We address each major comment below and will revise the manuscript to provide the requested details.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that ProOPF-B's 121 expert-annotated cases enable rigorous end-to-end evaluation of LLMs for professional-grade OPF modeling is load-bearing on the assumption that these cases are representative of operational workflows (network scale, uncertainty handling, real-time constraint changes, multi-period extensions), yet no quantitative coverage analysis, mapping to utility workflows, or inter-annotator agreement statistics are reported.

    Authors: We agree that explicit quantitative coverage analysis and inter-annotator agreement would strengthen the paper. The 121 cases were selected by domain experts to span key dimensions of operational workflows, but the manuscript does not report coverage statistics or agreement metrics. We will add a dedicated subsection with a coverage table mapping cases to network scales, uncertainty types, real-time changes, and multi-period extensions, plus inter-annotator agreement statistics from the annotation process. revision: yes

  2. Referee: [Abstract] Abstract: The description of ProOPF-B supplies no information on validation procedures for the expert annotations or baseline LLM performance on the test cases, which is required to judge whether the artifacts support the intended claims about professional-grade modeling.

    Authors: The manuscript states that annotations were performed by power-systems experts and that ground-truth code is executable, but it does not detail validation procedures beyond expert review or report baseline LLM results. We will expand the ProOPF-B description with explicit validation steps (expert review protocol and executability checks) and add baseline performance results from multiple LLMs to demonstrate the benchmark's utility. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction is self-contained artifact creation

full rationale

The paper introduces ProOPF-D (12K instances) and ProOPF-B (121 expert-annotated cases) as new datasets and benchmarks for LLM evaluation on OPF modeling. No derivation chain, equations, fitted parameters, or predictions are claimed that could reduce to the inputs by construction. The central contribution is the creation of these artifacts with ground-truth code; representativeness of the 121 cases is an unverified assumption but does not constitute circularity in any derivation. No self-citations, ansatzes, or renamings of known results are load-bearing in a mathematical sense. This is a standard non-circular benchmark paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work is the construction of empirical evaluation artifacts rather than a theoretical derivation.

pith-pipeline@v0.9.0 · 5729 in / 1211 out tokens · 28565 ms · 2026-05-25T07:32:20.150597+00:00 · methodology

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Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · 1 internal anchor

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    Qwen3 Technical Report

    URL https://dx.doi.org/10.21227/ vma9-wk20. Yang, A., Li, A., Yang, B., Zhang, B., Hui, B., Zheng, B., Yu, B., Gao, C., Huang, C., Lv, C., Zheng, C., Liu, D., Zhou, F., Huang, F., Hu, F., Ge, H., Wei, H., Lin, H., Tang, J., Yang, J., Tu, J., Zhang, J., Yang, J., Yang, J., Zhou, J., Zhou, J., Lin, J., Dang, K., Bao, K., Yang, K., Yu, L., Deng, L., Li, M., ...

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  2. [2]

    Use imperative language (e.g., ”Scale...”, ”Set...”) and include the base system, all modifications, and solver requirements

    Natural Language Description:Write a clear, professional instruction that explicitly states all parameter modifications with their numerical values. Use imperative language (e.g., ”Scale...”, ”Set...”) and include the base system, all modifications, and solver requirements

    work page

  3. [3]

    base system

    MATPOWER Code:Generate executable MATLAB code that loads the base system using loadcase(), applies all parameter modifications using appropriate MATPOWER indexing, configures solver options viampoption(), executes OPF viarunopf(). Example: Input Model Specification: { "base system": "case39", "parameter modifications": [ {"component": "bus", "bus id": 1, ...

    work page

  4. [4]

    base system

    Natural Language Description:Compose the scenario fragments into a coherent, professional narrative that describes operational conditions and their implications on the power system. The description should: • Integrate all scenario fragments into a unified operational scenario • Use natural, scenario-based language (e.g., ”During an extreme summer heatwave...

    work page arXiv

  5. [9]

    Scale the active power demand at bus 1 by a factor of 1.5

    Includeprintpf()to display results Example Input:Perform AC optimal power flow (ACOPF) on the IEEE 39-bus system (case39). Scale the active power demand at bus 1 by a factor of 1.5. Set the maximum active power output of all generators at bus 32 to 500 MW. Relax the minimum voltage magnitude constraint at bus 10 by setting VMIN to 0. Set the branch reacta...

    work page

  6. [12]

    Configure solver options viampoption()

    work page

  7. [13]

    Execute OPF viarunopf()

    work page

  8. [14]

    bus_PD_3

    Includeprintpf()to display results Example 1: Input:Perform AC optimal power flow (ACOPF) on the IEEE 14-bus system (case14). Scale the active power demand at bus 2 by a factor of 1.2. Set the maximum active power output of all generators at bus 1 to 300 MW. Set the OPF violation tolerance (opf.violation) to 1e-6, and write the corresponding MATPOWER code...

    work page arXiv

  9. [15]

    Generate a MATLAB function that accepts placeholder variables as function parameters (naming convention: ob- ject parameter id, e.g., bus PD 3, bus VMAX 8)

    work page

  10. [17]

    For each parameter modification implied by the scenario: • Retrieve the original value from the base system • Add an assertion to validate the modification direction: –For ”decrease” scenarios:assert(new value <= original value) –For ”increase” scenarios:assert(new value >= original value) –For ”set zero” scenarios:assert(new value == 0) • Apply the place...

    work page

  11. [19]

    During the late-night hours, industrial loads at bus 3 are significantly reduced, causing both active and reactive power demand to decrease substantially

    Execute OPF viarunopf() Example Input:A regional grid is modeled using the IEEE 14-bus system (case14). During the late-night hours, industrial loads at bus 3 are significantly reduced, causing both active and reactive power demand to decrease substantially. The long-distance transmission lines exhibit pronounced capacitive charging effects, leading to el...

    work page

  12. [20]

    Generate a MATLAB function that accepts placeholder variables as function parameters (naming convention: ob- ject parameter id)

    work page

  13. [22]

    For each parameter modification, retrieve the original value, add direction validation assertions, and apply the placeholder parameter value

    work page

  14. [23]

    Configure solver options viampoption()including solver specification if provided

    work page

  15. [24]

    converged

    Execute OPF viarunopf() 40 Title Suppressed Due to Excessive Size Example 1: Input:A regional grid is modeled using the IEEE 14-bus system (case14). During peak demand hours, the electrical load at bus 2 increases significantly due to commercial activity. Meanwhile, generator maintenance at bus 1 reduces the available generation capacity. Set opf.violatio...

    work page arXiv

  16. [25]

    Load the base system usingloadcase()

    work page

  17. [26]

    Apply all parameter modifications using appropriate MATPOWER indexing

    work page

  18. [27]

    Configure solver options viampoption()including model type if structural modification specifies a problem type change

    work page

  19. [29]

    Implement structural modifications: • For objective extensions: Construct the quadratic cost matrix Q based on the specified form, then use om.add quad cost()to add the term • For constraint extensions: Use appropriate constraint addition methods

    work page

  20. [31]

    Construct a results structure with solution values Example Input:Build a DC optimal power flow (DCOPF) optimization problem for the IEEE 39-bus system (case39). In addition to the default generation cost in the base case, add a quadratic penalty on phase-angle differences across all in-service transmission lines to discourage excessive angle separation (p...

    work page

  21. [32]

    Load the base system and apply explicit parameter modifications

    work page

  22. [33]

    Configure solver options including model type if structural modification specifies a problem type change

    work page

  23. [34]

    Useopf setup()to create an optimization model object

    work page

  24. [35]

    Implement structural modifications using appropriate methods (e.g.,om.add quad cost()for objective extensions)

    work page

  25. [36]

    Solve usingom.solve()and extract solution components

    work page

  26. [37]

    Add a quadratic penalty on phase-angle differences across all in-service transmission lines with penalty weight beta = 5

    Construct a results structure with solution values Example 1: Input:Formulate a DC optimal power flow (DCOPF) problem for the IEEE 14-bus system (case14). Add a quadratic penalty on phase-angle differences across all in-service transmission lines with penalty weight beta = 5. Scale the active power demand at bus 2 by 1.2. Set opf.violation to 1e-6, and wr...

    work page