1. Bibliographic Information

1.1. Title

Large Language Models for Power System Applications: A Comprehensive Literature Survey

The title clearly states the paper's central topic: a survey of the existing literature on the use of Large Language Models (LLMs) within the domain of power system engineering.

1.2. Authors

The authors of this paper are Muhammad Sarwar, Muhammad Rizwan, Mubushra Aziz, and Abdul Rehman Sudais.

• Muhammad Sarwar: Affiliated with the Department of Electrical & Computer Engineering at Iowa State University, USA. His research interests appear to be in power systems, smart grids, fault analysis, and the integration of AI techniques, as indicated by his referenced publications [2, 6, 10, 14, 18, 24].

• Muhammad Rizwan & Mubushra Aziz: Both are affiliated with the Department of Computer and Information Sciences at PIEAS, Islamabad, Pakistan. This affiliation suggests expertise in computer science, likely including artificial intelligence and data science.

• Abdul Rehman Sudais: Affiliated with the Department of Computer Sciences at FAST-NUCES, Faisalabad, Pakistan. This also points to a background in computer science.

  The author team represents a blend of expertise in both electrical/power engineering and computer science, which is ideal for a paper surveying the intersection of these two fields.

1.3. Journal/Conference

The paper is available as a preprint on arXiv, with an indicated publication date of April 2025. An arXiv preprint is a manuscript that has not yet undergone formal peer review for publication in a journal or conference. Publishing on arXiv allows researchers to disseminate their findings quickly to the scientific community. While not peer-reviewed, the work provides a timely overview of a rapidly evolving field.

1.4. Publication Year

The paper is dated April 2025, and the original source link indicates it was submitted to arXiv on December 15, 2025 (UTC). The publication dates for some references extend into 2025, suggesting the paper is positioned as a forward-looking survey of very recent and emerging work.

1.5. Abstract

The abstract summarizes the paper's goal to provide a comprehensive literature review on the use of Large Language Models (LLMs) in power system engineering. It systematically analyzes research published between 2020 and 2025 across several key application areas: fault diagnosis, load forecasting, cybersecurity, control and optimization, planning, simulation, and knowledge management. The abstract highlights the promising potential of LLMs but also points out significant challenges, including data scarcity, reliability concerns in critical infrastructure, and the need for better explainability. It also notes emerging trends like domain-specific LLMs (e.g., PowerPM) and hybrid models. The paper aims to serve as a guide for researchers and practitioners by mapping the current landscape and future research paths.

1.6. Original Source Link

• Original Source Link: https://arxiv.org/abs/2512.13004v1
• PDF Link: https://arxiv.org/pdf/2512.13004v1.pdf
• Publication Status: The paper is a preprint on arXiv. It has not undergone formal peer review.

2. Executive Summary

2.1. Background & Motivation

• Core Problem: Modern power systems are becoming increasingly complex due to factors like the integration of renewable energy sources (e.g., solar, wind), the adoption of smart grid technologies, and rising demands for efficiency and reliability. Managing this complexity requires advanced tools for decision-making, control, and analysis.
• Importance and Gaps: Traditional engineering methods and early AI models may struggle to keep pace with the scale and variety of data (both structured and unstructured) generated in modern grids. There is a need for more intelligent, adaptive, and versatile systems. While LLMs have shown revolutionary capabilities in many fields, their application in the high-stakes, safety-critical domain of power systems is nascent and not well-understood. A comprehensive overview is needed to consolidate existing research, identify common themes, and pinpoint critical challenges and opportunities.
• Innovative Entry Point: The paper's entry point is to conduct a systematic and comprehensive survey of this emerging intersection. Instead of proposing a new model, it organizes, synthesizes, and critically analyzes the body of work from 2020-2025, providing a structured map of a new research frontier. This helps to move the field from scattered, individual studies to a more cohesive understanding of the state of the art.

2.2. Main Contributions / Findings

• Primary Contributions:

  1. Structured Taxonomy of Applications: The paper systematically categorizes the applications of LLMs in power systems into distinct domains: fault diagnosis, load forecasting, cybersecurity, control & optimization, planning & scheduling, simulation & modeling, and knowledge management. This structure (visualized in Figure 1) provides a clear framework for understanding the breadth of research.
  2. Synthesis of Recent Research: It consolidates and summarizes key findings from a wide array of recent papers (2020-2025), presenting specific examples of how LLMs like GPT-4 and Llama are being used.
  3. Identification of Challenges and Future Directions: The survey critically identifies major obstacles, such as data scarcity, reliability/safety concerns ("hallucinations"), security vulnerabilities, and lack of explainability. Based on these challenges, it proposes concrete future research directions, including hybrid models, domain-specific LLMs, and robust security frameworks.

• Key Conclusions/Findings:

  1. LLMs show significant promise in enhancing power system operations, particularly in tasks involving natural language, unstructured data, and complex reasoning (e.g., interpreting fault reports, incorporating news sentiment into load forecasts, creating knowledge management systems).
  2. The direct application of general-purpose LLMs is risky and limited for core operational tasks due to reliability, safety, and security concerns. Their stochastic nature is ill-suited for the deterministic requirements of critical infrastructure.
  3. A clear trend is emerging towards domain specialization, including developing power-system-specific foundation models (e.g., PowerPM) and using techniques like Retrieval-Augmented Generation (RAG) and fine-tuning to imbue general models with specialized knowledge.
  4. Hybrid approaches, which combine LLMs with traditional physics-based models or other AI techniques (e.g., reinforcement learning), are a highly promising path forward to leverage the strengths of LLMs while mitigating their weaknesses.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, a novice reader needs to be familiar with the following concepts:

• Power System: A network of electrical components used to generate, transmit, and distribute electricity. Key functions include generation (creating power), transmission (moving power over long distances at high voltage), and distribution (delivering power to end-users at lower voltage). The stability and reliability of this grid are paramount.
• Smart Grid: An evolution of the traditional power grid that uses information and communication technology to gather and act on information about the behavior of suppliers and consumers. This enables more efficient, reliable, and sustainable electricity services.
• Large Language Model (LLM): A type of artificial intelligence model designed to understand, generate, and process human language. LLMs are built on deep learning architectures, most notably the Transformer, and are trained on massive datasets of text and code. Examples include OpenAI's GPT series and Meta's Llama series.
• Transformer Architecture: A neural network architecture that has become the foundation for most modern LLMs. Its key innovation is the self-attention mechanism, which allows the model to weigh the importance of different words in an input sequence when processing and generating language. Unlike older models like Recurrent Neural Networks (RNNs), Transformers can process entire sequences in parallel, making them highly efficient.
• Tokenization: The process of breaking down a piece of text into smaller units called tokens. These tokens can be words, sub-words, or characters. For example, the sentence "Power systems are complex" might be tokenized into ["Power", "systems", "are", "complex"].
• Embeddings: Numerical representations of tokens (or words) in a high-dimensional vector space. These vectors are learned during training and capture semantic relationships, meaning that words with similar meanings will have similar vector representations.
• Fine-tuning: The process of taking a pre-trained LLM (which has learned general language patterns from a vast dataset) and further training it on a smaller, domain-specific dataset. This adapts the model to perform well on a particular task, such as answering questions about power system faults.
• Prompt Engineering: The art of carefully crafting the input text (the prompt) given to an LLM to elicit a desired, accurate, and relevant response. As shown in the paper [13], a well-designed prompt can significantly improve an LLM's performance on a specialized task.
• Retrieval-Augmented Generation (RAG): A technique that enhances an LLM's ability to generate accurate and up-to-date information. When a query is received, a RAG system first retrieves relevant documents or data from an external knowledge base (e.g., a database of technical manuals). This retrieved information is then provided to the LLM as part of the context in its prompt, helping to ground its response in factual, domain-specific data and reduce "hallucinations."

3.2. Previous Works

The paper is a survey, so nearly all cited works are "previous works." Here are some key ones that establish the technological context:

• The Transformer Model (Vaswani et al., 2017): Although not explicitly cited with its original paper, the survey's discussion of the Transformer architecture [4] is a direct reference to this foundational work. The key mechanism is scaled dot-product attention, which is crucial for understanding how LLMs work. The formula for attention is: $ \mathrm{Attention}(Q, K, V) = \mathrm{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V $

  • Explanation:
    • $Q$ (Query): A matrix representing a set of queries. In self-attention, this is a representation of the current word being processed.
    • $K$ (Key): A matrix representing a set of keys. This is a representation of all words in the sequence that the current word can "attend" to.
    • $V$ (Value): A matrix representing a set of values. This is another representation of all words in the sequence.
    • The dot product $QK^T$ calculates a score for how much each word (represented by $Q$) should focus on every other word (represented by $K$).
    • $\sqrt{d_k}$: The scaling factor, where $d_k$ is the dimension of the key vectors. This is used to stabilize the gradients during training.
    • $\mathrm{softmax}$: A function that converts the attention scores into probabilities, ensuring they sum to 1. This produces the attention weights.
    • The final output is the weighted sum of the value vectors $V$, where the weights are the probabilities calculated by the softmax. In essence, the model learns which other words in the sentence are most important for understanding the current word.

• GPT and Llama Models: The survey frequently mentions models like GPT-4 [3] and Llama [6]. These are large, decoder-only Transformer models that are pre-trained on vast internet-scale text corpora. They are known for their strong performance in zero-shot (performing a task without any examples) and few-shot (performing a task with only a few examples) learning, which makes them attractive for data-scarce domains like power systems [7].

• Traditional AI in Power Systems: The paper contrasts LLM approaches with established methods. For instance, it mentions the use of Support Vector Machines (SVMs) for high impedance fault detection [6]. An SVM is a classical machine learning model that finds an optimal hyperplane to separate data points into different classes. The survey also references the use of Long Short-Term Memory (LSTM) networks for load forecasting [20]. LSTMs are a type of recurrent neural network (RNN) specifically designed to handle long-term dependencies in sequential data, making them well-suited for time-series forecasting. The novelty of recent work lies in augmenting these models with features extracted by LLMs/NLP techniques from unstructured data like news.

3.3. Technological Evolution

1. Physics-Based Models: The earliest approaches to power system analysis were based on mathematical models derived from the physical laws of electricity (e.g., Ohm's law, Kirchhoff's laws). These models are highly accurate but can be computationally intensive and may not easily adapt to changing grid conditions.
2. Classical AI/Machine Learning: The next wave involved using traditional machine learning models like SVMs [6], decision trees, and neural networks for specific tasks like fault classification and load forecasting. These models learn patterns from historical data. Time-series models like ARIMA and LSTMs [20] became standard for forecasting.
3. Deep Learning: More advanced deep learning models, including convolutional neural networks (CNNs) and LSTMs, were applied to handle more complex data patterns, such as in time-series forecasting and stability assessment.
4. Emergence of LLMs: The current evolution, as detailed in this survey, is the application of Transformer-based LLMs. Initially designed for natural language tasks, researchers are now exploring their capabilities for:

   • Integrating Unstructured Data: Processing text from maintenance logs, news reports [20], and operator notes to enrich traditional data-driven models.

   • High-Level Reasoning: Using LLMs as a reasoning engine to interpret system states, suggest control actions [15], or generate simulation code [16].

   • Human-in-the-Loop Interaction: Creating natural language interfaces for complex power system tools, acting as a "co-pilot" for engineers [1, 29].

     This paper situates itself at the forefront of this latest technological stage, capturing the initial exploration and experimentation with LLMs in a field traditionally dominated by numerical and physics-based methods.

3.4. Differentiation Analysis

Compared to previous methods, the LLM-based approaches surveyed in this paper have several core innovations:

• Versatility and Generalization: While traditional AI models are typically trained for one specific task (e.g., an LSTM for load forecasting), pre-trained LLMs possess broad, general-purpose knowledge and can be adapted to many different tasks (fault diagnosis, optimization, code generation) with minimal retraining, often just through prompt engineering or light fine-tuning.
• Handling of Unstructured Data: LLMs excel at understanding natural language. This is a significant departure from traditional methods that rely almost exclusively on structured, numerical data (e.g., sensor readings, voltage levels). LLMs can extract valuable signals from text-based sources like news articles [20], technical manuals, and operator logs, providing richer context.
• Reasoning and Explainability: Although explainability is a challenge, some studies show LLMs can provide natural language explanations for their diagnoses or decisions [13]. This is a potential advantage over "black box" models like deep neural networks, which provide outputs without justification.
• Agent-Based Interaction: The concept of an LLM as an autonomous agent [15, 27] that can interact with software tools (like Pandapower) and make decisions is a novel paradigm. This moves beyond simple prediction to active control and management.
• Focus on the Human Interface: Many applications aim to improve the human-computer interface, using LLMs to translate complex data into intuitive language or to translate human requests into machine commands [27, 30], making sophisticated tools more accessible.

4. Methodology

As this is a literature survey, its methodology is not about proposing a single new technical solution. Instead, the methodology is centered on the systematic review process itself. The paper outlines this process in the Introduction.

4.1. Principles

The core principle of this paper is to conduct a comprehensive and systematic literature review. This involves a structured approach to find, evaluate, and synthesize all relevant research within a defined scope to provide an unbiased and complete overview of the topic. The goal is to map the current state of LLM applications in power systems, identify common themes, pinpoint challenges, and suggest future directions.

4.2. Core Methodology In-depth (Layer by Layer)

The methodology for this survey paper can be broken down into the following steps, as described in Section 1:

1. Defining the Scope: The review focuses on a specific, emerging topic: the application of Large Language Models in power system engineering. The temporal scope is explicitly defined as research published "within the last three to five years," which the authors operationalize as the period between 2020 and 2025.

2. Systematic Literature Search: The authors performed a structured search across major academic databases.

   • Databases Searched: Google Scholar, arXiv, and IEEE Xplore. These are well-chosen venues: IEEE Xplore is the premier repository for electrical engineering and power systems research, arXiv is the go-to for cutting-edge (often preprint) AI research, and Google Scholar provides broad coverage across disciplines.
   • Keywords Used: The search was guided by a set of specific keywords to ensure comprehensive coverage. These included:
     • "Large Language Models power systems"
     • "LLMs for power grid"
     • "Transformer models in power system applications"
     • "Natural Language Processing for power system analysis" This keyword-based strategy is a standard practice in systematic reviews to identify a relevant body of literature.

3. Screening and Selection of Studies: Although not explicitly detailed, a standard review process would involve screening the search results based on title, abstract, and full-text to include only those papers that directly address the research question. The selection would focus on novelty, relevance, and methodological rigor.

4. Data Extraction and Synthesis: For each selected paper, the authors extracted key information, including:

   • The specific power system application (e.g., fault diagnosis, load forecasting).
   • The methodology employed (e.g., which LLM was used, fine-tuning techniques, dataset).
   • The main findings and performance results.
   • Identified challenges or limitations.

5. Thematic Analysis and Structuring: The extracted information was then organized into a coherent structure. The authors developed a taxonomy of applications, which forms the core of the survey (Section 3). This taxonomy, also visualized in Figure 1, groups the literature into logical categories:

   • Fault Diagnosis and Anomaly Detection

   • Load Forecasting and Demand Response

   • Cybersecurity

   • Control and Optimization

   • Planning and Scheduling

   • Simulation and Modeling

   • Knowledge Management

     The following figure (Figure 1 from the original paper) shows the system architecture:

     该图像是一个示意图，展示了大语言模型（LLMs）在电力系统中的应用分类。图中分为三个主要类别：运营、规划和支持，其中运营包括故障诊断和负荷预测，规划包括调度与仿真，支持则涵盖知识管理和网络安全等领域。

   This thematic organization allows for a structured discussion of trends and findings within each sub-domain.

6. Critical Analysis and Discussion: Beyond simple summarization, the authors critically analyze the synthesized findings. This is evident in sections discussing challenges [6], methodological trends [4], and future research directions [7]. They compare different approaches, highlight overarching problems like data scarcity and reliability, and contextualize the research within the broader needs of the power industry.

7. Summarization of Findings: The paper synthesizes the findings from individual studies into a high-level summary table (Table 1), which provides a quick, comparative overview of the different application domains, the techniques used, and their perceived maturity level.

   This structured review methodology ensures that the paper is not just a collection of summaries but a valuable analytical tool that maps out a new field of research.

5. Experimental Setup

As a survey paper, this work does not conduct its own experiments. Instead, it reports on the experimental setups used in the papers it reviews. This section summarizes the datasets, metrics, and baselines mentioned across the surveyed literature.

5.1. Datasets

The datasets used in the reviewed studies are highly diverse, reflecting the wide range of applications.

• Real-World Operational Data:

  • Sensor Readings: For fault diagnosis, studies use real-time sensor data from the power grid [13], such as leakage current measurements from insulators [17].
  • Electricity Demand Data: For load forecasting, researchers use historical demand data. An example is data from the ENTSOE (European Network of Transmission System Operators for Electricity) platform [21], which provides publicly accessible electricity load and generation data for European countries.
  • Historical Fault Records: These are used to train models for fault diagnosis [13].

• Unstructured Text Data:

  • News Articles: To improve load forecasting, studies have used news articles from sources like the BBC [21]. The idea is that news about geopolitical events or transportation disruptions can influence electricity demand.
  • Technical Documentation: For knowledge management, LLMs are applied to internal documentation, operational procedures, and design manuals [29].

• Simulated Data:

  • Due to the scarcity of real-world fault data (faults are rare events), some studies generate synthetic data. For instance, reference [33] uses conditional Wasserstein generative adversarial networks (cWGANs) to generate data to address class imbalance in voltage stability assessment.
  • Simulations from power system modeling tools like MATPOWER [34], OpenDSS [16], and Pandapower [15] are used to create datasets for training and testing control and optimization algorithms [9].

• Data Example: Reference [20] notes that "public sentiment and word vector representations related to transport and geopolitics had a sustained influence on electricity demand." A hypothetical data sample might pair a time series of electricity demand with a news headline from the same day, such as: "Major airport strike grounds all flights," to train the model to associate such events with changes in power consumption.

  The choice of these datasets is effective because it directly corresponds to the problem being solved. The use of both real-world and simulated data is a common and necessary practice in power systems research, where real-world experiments on critical infrastructure are often infeasible.

5.2. Evaluation Metrics

The paper reports various evaluation metrics depending on the task.

• For Fault Diagnosis / Classification:

  • Accuracy:
    1. Conceptual Definition: Measures the proportion of correct predictions (e.g., correctly identified fault types) out of the total number of predictions made. It is a general measure of a model's correctness.
    2. Mathematical Formula: $ \text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} = \frac{TP + TN}{TP + TN + FP + FN} $
    3. Symbol Explanation:
       • TP (True Positives): Correctly predicted positive cases.
       • TN (True Negatives): Correctly predicted negative cases.
       • FP (False Positives): Incorrectly predicted positive cases.
       • FN (False Negatives): Incorrectly predicted negative cases.
  • Explainability Quality: A qualitative metric mentioned in [13], assessing how well the LLM's generated explanation for a diagnosis is understood by a human expert. This is often evaluated through user studies or expert scoring.

• For Load Forecasting (Time-Series Regression):

  • Root Mean Squared Error (RMSE):
    1. Conceptual Definition: Measures the square root of the average of the squared differences between the predicted values and the actual values. It is sensitive to large errors because the errors are squared before being averaged.
    2. Mathematical Formula: $ \text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2} $
    3. Symbol Explanation:
       • $n$: The number of data points.
       • $y_i$: The actual value for the i-th data point.
       • $\hat{y}_i$: The predicted value for the i-th data point.
  • Mean Absolute Error (MAE):
    1. Conceptual Definition: Measures the average of the absolute differences between the predicted values and the actual values. It gives an idea of the magnitude of the error on average, without penalizing large errors as much as RMSE.
    2. Mathematical Formula: $ \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i| $
    3. Symbol Explanation:
       • $n$: The number of data points.
       • $y_i$: The actual value for the i-th data point.
       • $\hat{y}_i$: The predicted value for the i-th data point.

• For Simulation Code Generation:

  • Coding Accuracy: Mentioned in [16], this metric likely measures the percentage of generated simulation scripts that are syntactically correct and produce the correct simulation output.

5.3. Baselines

The paper mentions that the novel LLM-based approaches are compared against various baselines in the original studies.

• Traditional Time-Series Models: For load forecasting, LLM-enhanced models are compared against traditional LSTM models that do not use NLP features [20].

• State-of-the-Art Deep Learning Models: In fault prediction, an optimized LLM was compared against other state-of-the-art deep learning models for time-series forecasting [17].

• Baseline Prompting Methods: For fault diagnosis, the performance of advanced prompt engineering was compared to simpler, baseline prompts to demonstrate the value of carefully crafted context [13].

• Sequence-to-Sequence Models: A Transformer-based architecture for load forecasting was compared against state-of-the-art sequence-to-sequence models [23], which are a common architecture for time-series prediction.

  These baselines are representative because they reflect the established best practices or standard approaches in each respective sub-field before the introduction of LLMs. Comparing against them is crucial to demonstrate the added value of the new, more complex LLM-based methods.

6. Results & Analysis

Since this is a survey, the results are a synthesis of the findings from the reviewed papers. A key summary of these results is presented in Table 1.

6.1. Core Results Analysis

The survey's analysis of results across different domains reveals several key trends:

• Fault Diagnosis: LLMs can significantly improve diagnostic accuracy and provide human-readable explanations. Jing and Rahman [13] showed that using ChatGPT and GPT-4 with advanced prompt engineering led to "significant improvements in diagnostic accuracy, the quality of explanations, response coherence, and contextual understanding." This indicates that LLMs can act as powerful reasoning engines when guided correctly. Furthermore, an optimized LLM outperformed other deep learning models in time-series forecasting for predicting insulator faults [17], highlighting their potential in predictive maintenance.
• Load Forecasting: Incorporating unstructured data via NLP techniques improves forecast accuracy. Bai et al. [20, 21] found that adding news-derived features (e.g., sentiment related to transport) to an LSTM model improved day-ahead electricity demand forecasts. L'Heureux et al. [23] also showed that a Transformer-based architecture outperformed standard sequence-to-sequence models, confirming the power of attention mechanisms for time-series data with contextual features.
• Control and Optimization: LLMs are capable of tackling core power system optimization problems. Bernier et al. [9] successfully used an LLM to solve Optimal Power Flow (OPF) problems by representing the grid as a graph and using fine-tuning. The development of GAIA, a specialized LLM for power dispatch [11], and the demonstration of an LLM agent controlling a Pandapower simulation [15] show feasibility for real-time control and decision-making.
• Simulation and Modeling: LLMs can function as "research assistants." Jia et al. [16] developed a framework that "significantly improved the simulation coding accuracy of LLMs," enabling them to use specialized power system tools they were not explicitly trained on. This suggests LLMs can automate tedious simulation setup tasks.
• Knowledge Management: LLMs are effective at building specialized knowledge bases. Xu et al. [29] used an LLM to construct a high-precision terminology dictionary for urban power grid design, improving semantic parsing in intelligent design systems.

6.2. Data Presentation (Tables)

The following are the results from Table 1 of the original paper:

Analysis of Table 1: This table provides an excellent, high-level synthesis of the paper's findings.

• The "Maturity" column is particularly insightful. It suggests that applications closer to traditional NLP and data analysis tasks (like Load Forecasting and Knowledge Management) are more mature. In contrast, applications requiring high reliability and direct control of physical systems (like Planning & Scheduling and Cybersecurity) are less mature, reflecting the significant safety and security hurdles that remain.
• The "LLM Techniques" column shows a trend away from using off-the-shelf LLMs and towards more sophisticated methods like RAG, fine-tuning (LoRA), hybrid models, and agent-based systems, which are necessary to adapt general-purpose models to this specialized domain.
• The "Emerging Applications" row points to the future, with the development of domain-specific foundation models like PowerPM and RE-LLaMA indicating that the field is moving towards building specialized AI from the ground up for the energy sector.

6.3. Ablation Studies / Parameter Analysis

This paper, being a survey, does not conduct its own ablation studies. However, it reports on the methodologies of other papers that implicitly involve such analysis. For example:

• The work by Jing and Rahman [13] on prompt engineering is an implicit parameter analysis. They compare "advanced prompt engineering" to "baseline prompting methods," effectively ablating the sophisticated prompt components to show their impact on performance.
• The study by Bai et al. [20] on using news features for load forecasting inherently performs an ablation study by comparing an LSTM model with NLP-derived news features to a baseline LSTM model without them. The reported improvement in forecasting performance validates the contribution of the news feature component.
• The use of Low-Rank Adaptation (LoRA) for fine-tuning by Bernier et al. [9] is a parameter-efficient fine-tuning technique. The choice of such a method implies a consideration of the trade-off between performance and computational cost, which is a key aspect of parameter analysis.

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper concludes that Large Language Models are an emerging and highly promising technology for power system engineering. The survey systematically documents their application across a wide range of domains, from operational tasks like fault diagnosis and control to support functions like knowledge management and simulation. While the potential to enhance efficiency, reliability, and decision-making is clear, the authors stress that significant challenges remain. The successful integration of LLMs into this safety-critical sector is contingent on overcoming issues related to data scarcity, model reliability (e.g., hallucinations), security vulnerabilities, and the need for transparent, explainable AI. The conclusion reiterates that future work must focus on hybrid systems, domain specialization, and robust security frameworks to realize the transformative potential of LLMs for building next-generation intelligent power grids.

7.2. Limitations & Future Work

The paper explicitly identifies several limitations of current LLM applications and proposes corresponding future research directions.

Limitations Identified:

• Data Scarcity: Limited availability of domain-specific training data due to privacy and regulatory concerns [7].
• Reliability and Safety: General-purpose LLMs can be stochastic, inconsistent, and prone to "hallucinations," which is unacceptable for critical infrastructure [4].
• Limited Domain Expertise: LLMs lack deep, inherent knowledge of power system physics and complex mathematical reasoning [4].
• Security Threats: Integrating LLMs introduces new attack surfaces, including data poisoning, privacy invasion, and denial-of-service attacks [1].
• Computational Cost: Training and deploying large models is energy-intensive and expensive [38].
• Lack of Explainability: The "black box" nature of many LLMs makes it difficult for operators to trust their outputs [13].

Future Research Directions Suggested:

• Hybrid AI Systems: Combine LLMs with traditional physics-based models or other AI methods like reinforcement learning [7, 3].
• Retrieval-Augmented Generation (RAG): Enhance LLM accuracy by grounding responses in real-time, domain-specific data and documents [7].
• Multi-Agent Systems: Use multiple, collaborating LLM agents to manage complex, distributed power system tasks [7].
• Robust Security Frameworks: Develop countermeasures to mitigate the security threats introduced by LLMs [1].
• Improved Explainability: Create methods to make LLM decision-making processes more transparent and trustworthy [7].
• Domain-Specific LLMs: Develop smaller, specialized LLMs pre-trained on power system data to improve expertise and reduce computational costs [29].
• Multi-modal LLMs: Explore models that can process diverse data types, including time-series, images, and schematics [1].

7.3. Personal Insights & Critique

This survey provides a valuable and timely service to the research community. It is well-structured, comprehensive, and effectively maps out the nascent field of LLMs in power systems.

Inspirations and Strengths:

• Excellent Structuring: The taxonomy of applications is clear and logical, providing a fantastic framework for anyone entering this research area. The summary table (Table 1) is particularly effective at conveying the state of the art at a glance.
• Balanced Perspective: The paper does an excellent job of balancing the hype around LLMs with a sober assessment of the profound challenges. It avoids techno-optimism and instead offers a pragmatic view, emphasizing that direct deployment in critical roles is not yet feasible.
• Actionable Future Directions: The proposed research directions are concrete and directly address the identified limitations. This provides a clear roadmap for future work in the field.

Potential Issues and Areas for Improvement:

• Depth of Technical Analysis: As a broad survey, the paper sometimes sacrifices depth for breadth. For instance, when discussing a study like SafePowerGraph-LLM [9], it mentions "graph and tabular representations" but doesn't delve into the specifics of how these representations are constructed or how the LLM processes them. A deeper dive into the most promising 2-3 methodologies would have been even more insightful.
• Lack of Negative Results: The survey primarily focuses on studies that report successful applications. In emerging fields, understanding why certain approaches failed is just as important as knowing which ones succeeded. A discussion of studies with negative or inconclusive results (if any were found) would add significant value.
• Unverified Assumption of "Maturity": The "Maturity" ratings in Table 1 appear to be a subjective assessment by the authors. While reasonable, the paper does not provide an explicit rubric or methodology for how these ratings were determined (e.g., based on the number of publications, deployment readiness, or Technology Readiness Level). This makes the assessment less rigorous than it could be.
• Transferability: The methods discussed have high transferability to other critical infrastructure domains, such as water distribution, transportation networks, and industrial control systems. All these fields share characteristics with power systems: they are complex, generate large amounts of data, have a high cost of failure, and rely on a mix of legacy systems and modern technology. The hybrid, agent-based, and knowledge-management approaches discussed here could be directly adapted to these other sectors. This is a point that could have been highlighted more explicitly.