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最新刊期
卷 50 ， 期 13 ， 2026
- 摘要：Currently, the large language models (LLMs) have aroused widespread enthusiasm in various fields. They are trained based on massive data and characterized by a large number of parameters, and their capabilities of multimodal learning, multi-task integration, intelligent generation and decision-making have been significantly improved. To promote the application exploration of large model technology in the electric power field, firstly, this paper briefly introduces the development, basic principles, main characteristics and key technologies of LLMs. Secondly, the demand for the representative capabilities of LLMs in the intelligent process of new power system is deeply analyzed. Then, the large model methods that can handle relevant data and tasks are introduced from the perspective of common data types and tasks in the power system field. Finally, the possible challenges and potential countermeasures of LLMs are analyzed according to the current development in the power system field.关键词：large model;large language model (LLM);foundation model;multi-task integration;multimodal;new power system;artificial intelligence (AI);intelligentization3|10|0更新时间：2026-08-21
- 摘要：With the rapid development of new power systems, the integration of high-proportion renewable energy and power electronic devices introduces the characteristics of significant uncertainty and complex coupling into system operation. Existing decision-making methods exhibit limitations in complex scenarios, including limited multimodal fusion capability, weak dynamic reasoning capability, poor generalization, and deficient interpretability. These limitations hinder the realization of an effective closed-loop integration across perception, cognition, and decision-making processes. In recent years, large reasoning models (LRMs) equipped with natural language understanding and logical reasoning capabilities have demonstrated remarkable performance in complex decision-making tasks. This provides a novel technological pathway for intelligent decision-making in new power systems. First, this paper systematically reviews the development history, fundamental principles, core capabilities, and key technologies of LRMs. Focusing on complex decision-making scenarios in new power systems, such as intelligent dispatch, safe operation, and electricity markets, this paper provides an in-depth analysis of the application advantages and potential of LRMs. Furthermore, this paper summarizes the key challenges faced by LRMs in practical deployment, including data security, trustworthy reasoning, and system integration. Finally, this paper envisions future development directions toward autonomous evolutionary learning, human-computer interaction collaborative decision-making, and the evolution of hybrid reasoning architectures, providing systematic insights and forward-looking guidance for both theoretical research and engineering applications of LRMs in new power systems.关键词：artificial intelligence (AI);large language model (LLM);large reasoning model;new power system;intelligent decision-making;autonomous evolutionary learning;human-computer interaction3|10|0更新时间：2026-08-21
- 摘要：The dispatch automation applications such as power grid regulation and auxiliary decision-making highly rely on the completeness and accuracy of multi-dimensional information modeling, including power system mechanism knowledge, scheduling regulations and stability rules. Traditional modeling methods that use automated processes or knowledge graphs are highly dependent on human and expert experience, resulting in high modeling costs and difficulty in flexible expansion. With the development of large language models (LLMs), using model fusion text to represent and model knowledge has become a new way. Therefore, this paper proposes a knowledge representation and embedding method based on LLMs driven by chain-of-thought on regulations. First, a fully-automated synthesis of training and evaluation data from scheduling texts is achieved based on a hybrid logic segmentation scheme and a data synthesis framework of model distillation. Then, a two-stage strategy of efficient fine-tuning guidance and full-parameter fine-tuning internalization is used to train LLMs for knowledge embedding. Test results demonstrate that the trained model effectively enhances the understanding of power domain knowledge without compromising its general capabilities. The business agents constructed by the trained LLMs have been applied in the auxiliary decision-making business of multiple scheduling institutions at or above the provincial level.关键词：artificial intelligent (AI);chain-of-thought;reasoning model;knowledge representation;knowledge embedding;dispatch automation;large language model (LLM);model distillation2|17|0更新时间：2026-08-21
- 摘要：Large language models (LLMs) hold broad application prospects in the analysis and operation of distribution system due to their capabilities in human-computer interaction and reasoning. However, their insufficient numerical computation capabilities often lead to significant deviations in simulation calculation results. To address this, this paper proposes a synergy technology for power scientific computing and LLMs. Learning from the actual power grid scheduling mode of “dispatcher + simulation computing tools”, it constructs an architecture of “LLM dispatcher + power artificial intelligence (AI) calculator”. Through chain-of-thought reasoning, the natural language instructions are parsed into executable steps and a high-precision power AI scientific computing toolchain is autonomously invoked, enabling efficient and secure day-ahead operation scheduling for distribution networks. Considering the inherent hallucination issues in LLMs, typical causes of collaboration failures between LLMs and power AI models are analyzed. Furthermore, a collaboration capability enhancement method based on rejection sampling fine-tuning technology is proposed. The proposed method is validated in a regional distribution network of a certain city, and the results demonstrate that it can effectively generalize to multiple types of urban distribution network operation tasks.关键词：distribution network;day-ahead scheduling;large language model (LLM);large scientific computing model;artificial intelligence (AI);chain-of-thought (CoT)0|3|0更新时间：2026-08-21
- 摘要：During the digital transformation of integrated operation and planning of distribution networks, the complex coordination logics exist among the three parts of perception, analysis, and decision-making. It is challenging to address these issues via simple workflow methods or isolated expert knowledge. As a result, it is difficult to holistically consider the operation and planning boundaries, and achieve the intelligent scheme generation. To this end, a decision-making method for the integrated operation and planning of distribution networks based on large language model (LLM) and reasoning chain-of-thought (CoT) is proposed. By decomposing conventional complex problems of distribution networks into multi-stage task chains via a CoT mechanism, the key constraints are dynamically screened to adapt to the operation and planning strategies using expert knowledge, thereby reducing the computational complexity. This method adopts the coordinated CoT as the basis for the operation and planning of distribution networks, integrating the physical constraints of distribution networks with embedded expert knowledge to establish the closed-loop feedback mechanism of operation and planning. LLM enables the flexible coordination and invocation of multiple tools and intelligent reasoning, and the scientific rigor of planning schemes and efficiency of modeling and computation are enhanced. Case validation on an actual distribution system demonstrates that the proposed method can effectively improve the efficiency of problem diagnosis and the collaborative decision-making ability for operation and planning in complex scenarios.关键词：large language model (LLM);agent;chain-of-thought (CoT);distribution network;operation;planning;knowledge embedding2|3|0更新时间：2026-08-21
- 摘要：To address the challenges of secure distribution network operation and reliable load supply under high-penetration renewable energy integration, this paper proposes a distribution network reconfiguration method based on joint decision-making of large language model (LLM) and deep reinforcement learning (DRL) agents. First, a multi-objective dynamic distribution network reconfiguration model is formulated, and a two-stage joint decision-making framework is designed. In the first stage, a fine-tuned LLM identifies dispatching instruction intentions and preliminarily screens the range of transfer feeders. This assists the exploration and learning of the DRL agent. In the second stage, an improved soft actor-critic (SAC) algorithm is employed. The Gumbel-Softmax reparameterization technique enables the policy network to extract valid gradient signals from the discrete action space. Meanwhile, a graph convolutional network (GCN) is embedded to enhance topological feature extraction and topology variation perception. To reduce the action space size, the “switch pair” method and action space masking mechanism are introduced. This ensures the validity of all operations. Finally, case studies on a practical distribution system verify the effectiveness and superiority of the proposed method.关键词：distribution network reconfiguration;large language model (LLM);deep reinforcement learning;agent;soft actor-critic (SAC) algorithm;graph convolutional network (GCN);joint decision-making0|3|0更新时间：2026-08-21
- 摘要：With the integration of high-penetration distributed photovoltaics, distribution networks are confronted with severe challenges such as voltage violation and increased network loss. Large model technology is expected to overcome the deficiencies of traditional data-driven methods in generalization and interpretability. Nevertheless, the difficulty of knowledge modal representation and the complex coupling of multi-source data restrict the application of this technology in the power distribution domain. This paper proposes a reactive power and voltage optimization method for distribution networks based on a multimodal large model. By integrating the power flow mechanism of distribution networks, a multi-head attention mechanism is reconstructed to accurately embed heterogeneous information such as grid topology and node power. Meanwhile, the contrastive learning and chain-of-thought reasoning are introduced to construct a parameter training framework applicable to multi-modal large models, which realizes the rapid solution of reactive power control strategies for high-penetration photovoltaic distribution networks with simultaneous consideration of voltage violation and system network loss. Finally, case simulation is carried out on the IEEE 14-bus, IEEE 33-bus and IEEE 69-bus test systems. The results verify the effectiveness of the proposed method and its generalization capability for power systems at different scales.关键词：distribution network;reactive power and voltage optimization;photovoltaic;inverter;multimodal;Attention mechanism;large language model (LLM)0|3|0更新时间：2026-08-21
- 摘要：In the background of high-proportion renewable energy integration, the uncertainty and complexity of power grid operation have increased significantly, and the power system operation risk chain is lengthy with numerous correlative factors. Traditional risk assessment methods relying on rule bases and expert experience can hardly meet the real-time operation demands of cross-scenario and strong constraints. General large language models show strong understanding and generation capability in the field of question and answer, but in risk assessment for power system operation, they face problems such as high dependence on human experience, low accuracy of professional semantics and insufficient understanding of power grid topology, and are difficult to adapt to the safe operation and flexible dispatch demands of new power systems. Therefore, this paper proposes a risk assessment and reasoning method for power system operation based on knowledge transfer and structured enhancement of chain-of-thought. Firstly, this method constructs multi-scenario datasets covering topological disturbance and risk grading through auxiliary labeling and expert rule constraints, and realizes efficient transfer of general models to the power field. Secondly, a text-based topology injection mode is used to improve the model topology perception and constraint consistency. Then, a programmable reward signals are designed based on group relative policy optimization method, to jointly optimize the integrity of reasoning chain, terminology consistency and grading accuracy. Finally, ablation experiments and comparative experiments verify the effectiveness of the proposed method. The results show that the proposed method is superior to existing large language models in both the coherence of multi-step reasoning and the accuracy of risk grading, and can provide intelligent support for the safe and stable operation of new power systems.关键词：risk assessment;power system;knowledge transfer;text-based topology;group relative policy optimization (GRPO);large language model (LLM);chain-of-thought (CoT);reasoning0|3|0更新时间：2026-08-21
- 摘要：As the primary link in power grid emergency management and rapid restoration, fault diagnosis faces multiple challenges such as high-concurrency alarm signals, redundant noise, and complex topology. This paper proposes a generative fault diagnosis large model integrating topology awareness and multi-task learning, which is based on the bidirectional auto-regressive Transformer model to realize end-to-end generation from alarm signals to structured diagnosis texts. To improve the model understanding ability of the power grid structure, the topology-semantic joint modeling is proposed, propagating semantic embedding vectors corresponding to devices in graph neural networks to guide the model to perceive physical connections and protection coordination relationships between devices. A multi-task learning enhancement mechanism is introduced to jointly train the text generation of the main task and the device classification of the auxiliary task, improving the diagnosis accuracy of the model at the device level. A complex fault dataset with multi-class noise containing 4 000 samples constructed on a 220 kV system is validated. Experimental results show that the proposed method achieves full-task diagnosis accuracy of 0.930, which is improved by 15.1% compared with the baseline large model, specifically reaching backup protection action identification accuracy of 0.994. Furthermore, Bootstrap statistical tests and multi-level noise tests confirm the significance and robustness in the performance improvement of this method.关键词：power system;fault diagnosis;generative large model;large language model;topology;graph neural network;multi-task learning0|3|0更新时间：2026-08-21
- 摘要：The dynamic characteristics of new distribution systems and the abruptness of faults impose higher requirements for emergency maintenance decision-making and derived risk reasoning. Therefore, this paper proposes a novel emergency maintenance mode based on large language model (LLM), covering system dynamic decision-making mechanisms of system risk assessment and adequacy assessment, and the full process of derived risk reasoning. The overall architecture for distribution emergency maintenance decision-making and derived risk reasoning is designed, with task decomposition and an LLM-based multi-agent collaboration model. The risk assessment agent conducts system risk assessment in segmented time intervals. The adequacy assessment agent generates transfer topologies and counts supply nodes, while calling external power flow tools to compute system adequacy. The comprehensive decision-making agent determines the optimal maintenance time window and interacts with users in real time. The derived risk reasoning agent identifies key equipment with derived risks, evaluates derived risks, and uses retrieval-augmented generation to classify incident severity. Based on few-shot learning from historical samples, it generates incident risk disposal plans. Finally, the effectiveness of the proposed emergency maintenance mode is validated on an improved multi-zone, multi-level distribution system.关键词：distribution system;risk assessment;adequacy assessment;emergency maintenance decision-making;large language model (LLM);multi-agent0|3|0更新时间：2026-08-21
- 摘要：Against the backdrop of the diversified development of source-grid-load-storage in distribution systems and the continuously growing requirements for reliability, the scheduling of maintenance plans for distribution networks has become increasingly challenging. Existing problems include complicated multi-department collaboration, strong coupling among individual plans, and chain reactions triggered by plan revisions. To address these issues, this paper proposes a maintenance plan scheduling framework based on the collaboration of large and small models. The framework adopts a large language model (LLM) fine-tuned via quantized low-rank adaptation and an improved deep Q-network (DQN) algorithm serving as the small model. First, leveraging the natural language understanding capability of the LLM, the unstructured maintenance demands collected from multi-department coordination meetings are parsed. Accordingly, the optimization objectives, constraints, parameter settings and corresponding interactive environment configurations of the small model are automatically adjusted. Furthermore, the small model optimizes maintenance plans using the improved DQN algorithm, and progressive weights combining historical experiences and newly added environmental information are adopted to boost training performance. Finally, a multi-objective Markov optimization model for maintenance planning is established, which takes into account the equilibrium of resource allocation and plan execution deviation. Validation and analyses are conducted using actual maintenance data collected from a regional distribution network in Shanghai, China. Experimental results demonstrate that the proposed large-small model collaborative framework possesses satisfactory generalization ability and dynamic adaptability. It can effectively support the decision-making process for complex maintenance plan scheduling and improve both scheduling efficiency and the rationality of formulated schemes.关键词：distribution network;maintenance plan;scheduling;large language model (LLM);large model;small model;reinforcement learning;deep Q-network0|4|0更新时间：2026-08-21
- 摘要：As critical components of the demand side, residential users are characterized by a massive aggregated size but low per-unit consumption, resulting in significant transaction costs for the flexibility invocation that hinder the application of many current demand response methods. Therefore, this paper proposes an intelligent residential demand response method based on large language model (LLM) multi-agent framework, leveraging the capabilities of LLM in mass user interaction, energy consumption pattern recognition, and generalized heterogeneous modeling. Firstly, a collaborative LLM multi-agent framework is proposed for residential users participating in demand response, where specialized modules for interaction, analysis, and decision-making work in coordination to enhance LLM performance and efficiency in demand response tasks. Secondly, for the two tasks of user behavior analysis and demand response decision-making, the idea of large-small model collaboration is adopted, and the LLM-assisted analysis technology with embedded adaptive feature extraction and LLM collaborative decision-making optimization method integrating mathematical optimization model are proposed, respectively, in order to fully utilize the generalization adaptability of large models and the computational accuracy of small models. The case study results demonstrate that the proposed framework can effectively enhance the demand response interaction ability of residential users, improve the accuracy and economy of demand response, and provide innovative ideas for refined management and flexibility potential exploration of resident users.关键词：large language model;multi-agent;demand response;residential user;energy consumption pattern recognition;flexibility;new power system0|3|0更新时间：2026-08-21
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Lightweight Large Model-driven Human-Computer Interactive Regulation Method for Virtual Power Plants
摘要：Massive residential users on the load side are characterized by ambiguous information input, frequent demand fluctuations, and highly discrete behaviors. Existing methods struggle to conduct structured characterization, customized modeling and quantitative accounting of the regulation capability of such resources, which has become a key technical bottleneck restricting the practical popularization of virtual power plants among thousands of households. This paper firstly proposes an intelligent modeling method for massive distributed flexible loads based on large models, and designs a full-link human-computer interaction regulation mode for virtual power plants covering user demand input, automatic large model modeling, regulation instruction execution, and human-computer closed-loop feedback. Secondly, a two-stage collaborative large model training method combining supervised learning and reinforcement learning is proposed to realize efficient fine-tuning of low-parameter and lightweight large models. Case study results show that compared with existing methods, the proposed method has remarkable advantages in training efficiency, regulation accuracy and model deployment cost. It provides strong support for fully unleashing the regulation potential of residential users and comprehensively activating flexible regulation resources on the load side.关键词：virtual power plant (VPP);flexibility resource;large model;large language model (LLM);human-computer interaction;supervised learning;reinforcement learning0|5|0更新时间：2026-08-21 - 摘要：With the increasing demand for intelligent simulation in new power systems, traditional tools struggle to meet rapid response requirements, while general large language models face challenges of knowledge limitations and hallucination generation in vertical domains. To address the lack of operational knowledge about specific simulation platform in existing models, this paper proposes an adaptively evolving simulation agent framework based on large language models. The framework establishes a “decision-execution-feedback-learning” closed-loop mechanism, designs a dynamic skill extraction and vectorized storage method, transforming successful exploration trajectories into a structured skill library to address the domain-knowledge deficiency of general models. Meanwhile, a skill evaluation model trained on historical trajectories is constructed to quantitatively predict skill execution success rates under specific states, providing value function guidance for agent decision-making and reducing trial-and-error costs. Finally, case studies based on the CloudPSS platform show that the proposed method can improve the task completion rate of models with various parameter scales and reduce the number of dialogue rounds, verifying the potential of this method in reducing the threshold for using simulation software and achieving real-time simulation.关键词：new power system;large language model;power simulation;agent;skill library;retrieval-augmented generation3|6|0更新时间：2026-08-21
- 摘要：Accurate characterization of renewable energy output scenarios is critical for quantifying uncertainty in large-scale integration of renewable energy. Deep learning models are effective in capturing complex distributions. However, their performance heavily relies on hyperparameter configurations. Conversely, numerical optimization methods, such as Bayesian optimization, suffer from cold-start issues and slow convergence, failing to meet the stringent timeliness requirements of day-ahead scheduling. To address these challenges, this paper proposes a large language model (LLM)-driven scenario generation method for renewable energy output based on the conditional diffusion model. Firstly, a conditional diffusion model tailored for scenario generation for grid-side renewable energy output is constructed. This model embeds conditional information to implicitly learn the conditional probability distribution of actual renewable energy outputs and generates scenario sets via Markov chains. Secondly, a LLM-driven chain-of-thought optimization framework is proposed. By leveraging the semantic reasoning capabilities of the LLM to analyze training states, this framework rapidly pinpoints optimal parameter intervals under an extremely limited computational budget. Case studies on a provincial power system in Southwest China demonstrate that the proposed method significantly outperforms traditional approaches in both scenario generation accuracy and optimization efficiency. Specifically, the average Euclidean distances of the generated scenario sets are approximately 30.7% higher than those of the traditional methods. Even under a highly restricted budget of only 10 iterations, the method further enhances accuracy by approximately 1%. Furthermore, the proposed method effectively bolsters the reliability of dispatch decisions and the stability of system operation, providing robust support for efficient multi-scenario power system dispatching.关键词：power system dispatching;renewable energy;uncertainty quantification;scenario generation;large language model (LLM);chain-of-thought (CoT);conditional diffusion model0|3|0更新时间：2026-08-21
Key Technologies and Applications of Large Models for New Power Systems
- 摘要：To achieve rapid regulation of voltage stability in new power systems, a coordinated preventive‑emergency control strategy for voltage stability that balances regulation cost and instability risk is proposed. First, a complementary coordinated control architecture integrating preventive control and emergency control is established. In the preventive control phase, a physics-guided neural network architecture is adopted, embedding operation and stability constraints into the model training process and optimizing network parameters through back propagation of loss terms. Meanwhile, to accurately assess the transient voltage stability of systems under various preventive control strategies, a long short-term memory network is introduced to construct a stability assessment model, thereby effectively avoiding transient risks. In the emergency control phase, an adaptive emergency control strategy based on the current voltage sag severity and future restoration trend is proposed. Based on the system steady-state operating point after preventive control, the transient instability risk in the emergency control phase is quantitatively evaluated by comprehensively considering the fault probability and regulation cost. Finally, by integrating the two-stage optimization objectives, an improved genetic algorithm is employed to realize the effective enhancement of both the solution quality and efficiency for the coordinated control strategy. Simulation results based on the New England 10-machine 39-bus system and the Nordic system demonstrate that the proposed strategy can effectively address transient voltage stability issues induced by faults with various severe levels.关键词：voltage stability;instability;voltage sag;preventive control;emergency control;coordinated control;genetic algorithm;long short-term memory (LSTM) network1|0|0更新时间：2026-08-21
- 摘要：In DC microgrids, energy storage systems controlled by analogous virtual synchronous generator (AVSG) has limitations in simultaneously suppressing voltage sags and accelerating voltage recovery. Issues such as excessive voltage change ratio and voltage deviation, or prolonged voltage restoration time, may arise, affecting the DC bus voltage stability. To address this, this paper proposes a control method combining virtual inertia adaptation with model predictive control (MPC). This method enhances voltage fluctuation suppression by dynamically adjusting the inertia coefficient and optimizing the energy storage power compensation, while ensuring rapid voltage recovery. First, an adaptive control strategy for the virtual inertia coefficients of AVSG is designed based on the Sigmoid function to enhance voltage inertia response capability and suppress voltage fluctuations. Then, based on the MPC, a cost function is designed to minimize the weighted sum of squares of voltage deviation and energy storage output current. Through rolling optimization, the optimal control sequence is solved to achieve real-time compensation for the reference current value of AVSG, thereby increasing the support power for voltage recovery, correcting voltage overshoot and oscillations caused by adaptive control, and shortening the time required for voltage recovery and stabilization. Finally, a real-time simulation model of the DC microgrid is established using the StarSim/NI-PXIe simulation platform, and controller- (hardware-) in-the-loop experiments are conducted. The simulation and experiment results show that, compared with the conventional AVSG control, the proposed method reduces the voltage sag peak value by 23.5% and shortens the voltage recovery time by 58.6% when a large step load increase of approximately 40% of the total microgrid capacity occurs.关键词：DC microgrid;energy storage;voltage stability;analogous virtual synchronous generator (AVSG);inertia;voltage recovery;model predictive control (MPC)0|1|0更新时间：2026-08-21
- 摘要：With the integration of a significant number of distributed renewable energy sources and power electronic devices, the number of disturbance factors in DC power distribution systems has gradually increased. Among these, periodic disturbances may induce forced oscillations in such systems. Traditional research has focused primarily on underdamped oscillations caused by inappropriate system parameter settings, with relatively little attention paid to forced oscillations in DC distribution systems induced by periodic disturbances. This paper takes a DC distribution system based on a master-slave control scheme as an example to construct a fourth-order system model. Using the second-order time-domain differential equations obtained through order reduction, an analytical expression for the amplitudes of forced oscillations is derived, revealing the mechanism by which forced oscillations occur in DC power distribution systems. Furthermore, by combining frequency-domain metrics of the closed-loop transfer function, an intuitive analysis is conducted on the influence of system parameters and disturbance source characteristics on the amplitudes of forced oscillations, revealing the patterns of influence exerted by system and control parameters on the amplitudes of forced oscillations. Finally, the validity of the constructed model and theoretical analysis is verified through simulation and experiments.关键词：DC distribution system;disturbance;forced oscillation;master-slave control;order reduction0|0|0更新时间：2026-08-21
- 摘要：The large-scale integration of EVs poses substantial challenges to the stable operation of the power grid. Therefore, accurate load forecasting for EV charging stations is critical for ensuring power grid stability. Nevertheless, charging station loads are influenced by various factors, making accurate forcasting highly challenging. To address the insufficient utilization of spatial factors in existing load forecasting methods for EV charging stations, this paper proposes a forecasting method based on the improved spatio-temporal graph convolutional network (ISTGCN) model. Firstly, a spatio-temporal graph convolutional network (STGCN) is constructed by integrating graph convolutional networks (GCNs) and temporal convolutional networks (TCNs). This architecture extracts spatial features between charging stations from both distance and virtual matrices while effectively overcoming the gradient explosion problem commonly encountered in recurrent models. Secondly, to enhance forecasting accuracy and efficiency, a convolutional block attention module (CBAM) is introduced to further facilitate information flow within the network. Furthermore, to address the difficulty of hyperparameter tuning, the crested porcupine optimizer (CPO) algorithm, known for its superior global search capability, is incorporated into the model to improve forecasting performance. Finally, short-term load forecasting is conducted based on real-world data from charging stations in a specific city. The experimental results demonstrate that the proposed method has significant advantages in forecasting performance.关键词：electric vehicle (EV);load forecasting;graph convolutional network (GCN);temporal convolutional network (TCN);attention;hyperparameter;crested porcupine optimizer (CPO) algorithm0|0|0更新时间：2026-08-21
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