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arxiv: 2507.14800 · v2 · submitted 2025-07-20 · 📡 eess.SY · cs.AI· cs.SY

Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control

Xu Yang , Chenhui Lin , Licheng Sha , Liping Yang , Shuzhou Wu , Xichen Tian , Haotian Liu , Wenchuan Wu This is my paper

Pith reviewed 2026-05-19 04:49 UTC · model grok-4.3

classification 📡 eess.SY cs.AIcs.SY
keywords large language modelsvoltage controldistribution networksvolt/var optimizationexperience-driven dispatchself-evolutionincomplete informationday-ahead scheduling
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The pith

Large language models generate evolving voltage control schedules for distribution networks from historical experiences.

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

The paper proposes an LLM agent for day-ahead Volt/Var scheduling in distribution networks that draws on archived operating records to create and improve dispatch decisions. Four modules work together: one stores past records, one retrieves relevant cases based on forecasts, one generates new schedules, and one modifies them to refine the overall policy. A sympathetic reader would care because many real networks operate with incomplete data and changing conditions where rigid optimization models often fail to adapt quickly.

Core claim

The collaboration of experience storage, retrieval, generation, and modification modules enables an LLM agent to self-evolve its dispatch strategies, producing effective day-ahead voltage control solutions for distribution networks even when full system information is unavailable.

What carries the argument

The four-module experience framework in which storage archives historical records, retrieval selects matching past cases, generation creates context-aware decisions, and modification refines them to achieve policy self-evolution.

If this is right

  • The retrieval step lets the agent tailor new decisions to current forecast conditions using only stored records.
  • The modification step produces incremental policy improvement without requiring a full re-optimization each day.
  • Experiments confirm the approach handles incomplete information while maintaining acceptable voltage profiles.
  • The method supplies a practical route for dispatch when explicit network models or complete measurements are missing.

Where Pith is reading between the lines

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

  • The same modular structure could be reused for other sequential control tasks that rely on historical logs rather than first-principles models.
  • Adding a lightweight safety filter after the modification module would address the lack of explicit guarantees while preserving the self-evolution loop.
  • Scaling the experience store to include simulated rather than only real-world records might accelerate learning in sparsely observed networks.

Load-bearing premise

Retrieved historical experiences plus LLM reasoning and modification will reliably produce dispatch decisions that keep voltages within limits and match or beat conventional optimization methods.

What would settle it

On a standard test feeder, compare voltage deviation statistics and constraint violations between the LLM-generated schedules and a conventional optimal power flow solver run on the same forecast data; if the LLM version produces more violations or worse profiles, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2507.14800 by Chenhui Lin, Haotian Liu, Licheng Sha, Liping Yang, Shuzhou Wu, Wenchuan Wu, Xichen Tian, Xu Yang.

Figure 1
Figure 1. Figure 1: Scheme of the proposed LLM-based experience-driven voltage control solution. A. Experience Storage The experience storage is initialized by 𝐾𝐾 typical experiences {𝑒𝑒𝑖𝑖}𝑖𝑖=1 𝐾𝐾 . Compared to traditional dispatch methods, we characterize an experience 𝑒𝑒𝑖𝑖 to comprise the following components: 1) Context of 𝑒𝑒𝑖𝑖: This component outlines the context of the experience, i.e., the hourly forecasting of load and… view at source ↗
Figure 2
Figure 2. Figure 2: Training process of proposed and baseline methods. As can be seen from [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
read the original abstract

With the advanced reasoning, contextual understanding, and information synthesis capabilities of large language models (LLMs), a novel paradigm emerges for the autonomous generation of dispatch strategies in modern power systems. In this paper, we propose an LLM-based experience-driven day-ahead Volt/Var schedule solution for distribution networks, which enables the self-evolution of LLM agent's strategies through the collaboration and interaction of multiple modules, specifically, experience storage, experience retrieval, experience generation, and experience modification. The experience storage module archives historical operational records and decisions, while the retrieval module selects relevant past cases according to current forecasting conditions. The LLM agent then leverages these retrieved experiences to generate new, context-aware decisions for current situation, which are subsequently refined by the modification module to realize self-evolution of the dispatch policy. Comprehensive experimental results validate the effectiveness of the proposed method and highlight the applicability of LLMs in power system dispatch problems facing incomplete information.

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 / 2 minor

Summary. The paper proposes an LLM-based experience-driven framework for day-ahead Volt/Var scheduling in distribution networks under incomplete information. It describes a four-module architecture (experience storage, retrieval, generation, and modification) enabling self-evolution of dispatch strategies via historical records, context-aware generation, and iterative refinement, with claims that comprehensive experiments validate effectiveness and applicability to power system dispatch.

Significance. If the generated schedules can be shown to be feasible and competitive, the work could offer a novel paradigm for adaptive, reasoning-based control in uncertain power systems, potentially outperforming rigid optimization methods through continuous self-evolution. The modular experience loop is an interesting architectural idea, but its practical value hinges on addressing feasibility and empirical rigor.

major comments (2)
  1. [Method (experience generation and modification modules)] The central claim that the four-module experience loop produces dispatch decisions improving voltage profiles and competitive with conventional methods requires the LLM outputs to be operationally feasible. However, the architecture description (as summarized in the abstract and method) supplies no mechanism—neither a power-flow solver call, a projection step, nor a hard constraint in the prompt template—to enforce |V| ∈ [0.95,1.05] pu, reactive-power limits, or line-flow limits. Under incomplete information, mismatched experiences or LLM reasoning can yield infeasible set-points, allowing the self-evolution loop to reinforce unsafe policies.
  2. [Experimental Validation] The abstract states that 'comprehensive experimental results validate the effectiveness,' yet the manuscript provides no details on baselines, performance metrics (e.g., voltage deviation or loss reduction), network sizes/topologies tested, or explicit modeling of incomplete information. This absence is load-bearing for assessing whether the method is superior or at least competitive with conventional optimization approaches.
minor comments (2)
  1. [Abstract] Clarify the specific LLM model employed and the exact interaction protocol among the four modules in the main text.
  2. [Results] Ensure all figures and tables include clear legends and units for voltage and power quantities.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each of the major comments in detail below, providing clarifications and indicating the revisions we will make to strengthen the paper.

read point-by-point responses

  1. Referee: The central claim that the four-module experience loop produces dispatch decisions improving voltage profiles and competitive with conventional methods requires the LLM outputs to be operationally feasible. However, the architecture description (as summarized in the abstract and method) supplies no mechanism—neither a power-flow solver call, a projection step, nor a hard constraint in the prompt template—to enforce |V| ∈ [0.95,1.05] pu, reactive-power limits, or line-flow limits. Under incomplete information, mismatched experiences or LLM reasoning can yield infeasible set-points, allowing the self-evolution loop to reinforce unsafe policies.

    Authors: We acknowledge the referee's concern regarding the operational feasibility of the LLM-generated schedules. The manuscript's description of the experience generation and modification modules emphasizes context-aware decision making and iterative refinement using historical data, but does not explicitly detail a constraint enforcement mechanism. This is a valid point that could lead to infeasible solutions in practice. In the revised manuscript, we will add a new subsection in the method to describe how the modification module incorporates constraint awareness through prompt engineering and includes a post-processing step with a power flow solver to validate and adjust the schedules if necessary, ensuring compliance with voltage and power limits. revision: yes

  2. Referee: The abstract states that 'comprehensive experimental results validate the effectiveness,' yet the manuscript provides no details on baselines, performance metrics (e.g., voltage deviation or loss reduction), network sizes/topologies tested, or explicit modeling of incomplete information. This absence is load-bearing for assessing whether the method is superior or at least competitive with conventional optimization approaches.

    Authors: We appreciate this feedback on the experimental validation. While the manuscript includes an experimental section presenting results on distribution networks with comparisons to traditional methods and metrics focused on voltage profile improvement, we recognize that more explicit details would strengthen the paper. In the revision, we will expand this section to clearly list the baselines (including optimization-based and heuristic approaches), specify the performance metrics used (such as voltage deviation and active power loss), detail the network sizes and topologies (e.g., IEEE 33-bus and 123-bus systems), and describe how incomplete information is modeled via forecast uncertainties. This will allow readers to better evaluate the competitiveness of the proposed approach. revision: yes

Circularity Check

0 steps flagged

No circularity: architectural proposal validated by experiments

full rationale

The paper describes a multi-module LLM architecture (storage, retrieval, generation, modification) for day-ahead Volt/Var scheduling under incomplete information. The central claim of self-evolution and competitive performance is supported by experimental validation rather than any mathematical derivation chain. No equations, fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations appear in the provided description. The method is a design proposal whose outputs are not forced by construction from its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach rests on the domain assumption that LLMs possess sufficient reasoning to synthesize and refine dispatch strategies from retrieved cases, plus the ad-hoc invention of the four experience modules.

axioms (1)
  • domain assumption Large language models possess advanced reasoning, contextual understanding, and information synthesis capabilities that can be leveraged for autonomous generation of dispatch strategies.
    Explicitly stated in the opening of the abstract as the foundation for the proposed paradigm.
invented entities (1)
  • Experience storage, retrieval, generation, and modification modules no independent evidence
    purpose: To enable self-evolution of the LLM agent's dispatch strategies through interaction.
    Newly introduced components that form the core of the proposed solution; no independent evidence outside the paper is provided.

pith-pipeline@v0.9.0 · 5712 in / 1295 out tokens · 33693 ms · 2026-05-19T04:49:54.636245+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Context of 𝑒𝑒𝑖𝑖: This component outlines the context of the experience, i.e., the hourly forecasting of load and PV generation �𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡�𝑡𝑡=1 𝑇𝑇 , �𝑃𝑃𝑉𝑉𝑖𝑖,𝑡𝑡�𝑡𝑡=1 𝑇𝑇 , which are used to compare similarity with the current situation

    work page

  2. [2]

    Reasoning process: This component introduces the reasoning process of the experience, explaining how final actions are derived from the forecasting data, and provides a reference for future decision-making

    work page

  3. [3]

    Final action s: This component describes the final OLTC actions �𝑂𝑂𝐿𝐿𝑂𝑂𝑂𝑂𝑖𝑖,𝑡𝑡�𝑡𝑡=1 𝑇𝑇 and SCs actions ��𝑆𝑆𝑂𝑂𝑖𝑖,𝑚𝑚,𝑡𝑡�𝑡𝑡=1 𝑇𝑇 � 𝑚𝑚=1 𝑀𝑀 , where 𝑀𝑀 is the number of SCs in the distribution network

    work page

  4. [4]

    Dispatch results: The final component illustrates the dispatch results of the fina l action s, i.e., the hourly voltage conditions, which can be used for refining future operations. B. Experience Retrieval Just as human experts tend to draw upon the most similar past experience when encountering new situations, the LLM agent also require s access to the m...

    work page

  5. [5]

    Role and task description: This component describes the role of the LLM agent as an expert in power system operation and optimization , whose task is to determine the day -ahead actions based on forecasting information of the following day

    work page

  6. [6]

    It also provides a detailed explanation of the OLTC and SCs and corresponding constraints

    Environment description: This component describes the overview of the distribution network and voltage problem that needs to be address ed. It also provides a detailed explanation of the OLTC and SCs and corresponding constraints

    work page

  7. [7]

    Specifically, for the voltage control problem considered in this letter, we request the action time and action magnitude of OLTC and SCs to be returned as separate lists

    Output format: This component specifies the required output format for the LLM agent. Specifically, for the voltage control problem considered in this letter, we request the action time and action magnitude of OLTC and SCs to be returned as separate lists

    work page

  8. [8]

    3 IEEE POWER ENGINEERING LETTERS, VOL

    Past experiences: This component incorporates past experiences retrieved by the experience retrieval module, guiding the LLM agent through few -shot learning, where experiences with high profile similarity provide references for action tim e, while those with high statistical similarity offer guidance on action magnitude. 3 IEEE POWER ENGINEERING LETTERS,...

    work page 2025

  9. [9]

    reward function

    Chain -of-Thought (CoT) guidance: In this component, we introduce CoT reasoning framework. It begins with an analysis of the trend and magnitude of load and PV generation, followed by an assessment of possible voltage issues, and concludes with decision- making based on the given experiences. The CoT structure not only supports the LLM agent ’s decision -...

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    R. S. Bonadia et al. , “On the Potential of ChatGPT to Generate Distribution Systems for Load Flow Studies Using OpenDSS,” IEEE Trans. Power Systems, vol. 38, no. 6, pp. 5965-5968, Nov. 2023

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    Z. Yan and Y. Xu, “Real -Time Optimal Power Flow With Linguistic Stipulations: Integrating GPT-Agent and Deep Reinforcement Learning,” IEEE Trans. Power Systems, vol. 39, no. 2, pp. 4747-4750, Mar. 2024

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    X. Yang et al., “Supplementary Files for LLM as An Operator,” [Online]. Available: https://github.com/YangXuSteve/LLM-as-Operator

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