IET Smart Grid最新文献

{"title":"Correction to Weather-Aware Reinforcement Learning for Power Grid Topology Control Under Extreme Events","authors":"","doi":"10.1049/stg2.70136","DOIUrl":"https://doi.org/10.1049/stg2.70136","url":null,"abstract":"<p>F. Tang, Y. Wang, Y. Zhang, et al., “Weather-Aware Reinforcement Learning for Power Grid Topology Control Under Extreme Events,” <i>IET Smart Grid</i> (2026): e70099, https://doi.org/10.1049/stg2.70099.</p><p>The funding information stated in the article was incorrect. The correct funding information is as follows:</p><p>‘This work was supported by the Science and Technology Project of State Grid Corporation of China (Grant No. kj2025-080)’.</p><p>The authors apologise for the error.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bi-GRU–Based Protection Scheme for Reliable Fault Identification in Series-Compensated Transmission Lines","authors":"Majid Khezerloo,&nbsp;Mojtaba Beiraghi,&nbsp;Reza Ghanizadeh","doi":"10.1049/stg2.70130","DOIUrl":"https://doi.org/10.1049/stg2.70130","url":null,"abstract":"<p>Conventional relays often struggle to operate reliably on transmission lines equipped with series capacitors, especially for faults with resistance. To tackle this problem, this paper introduces a protection strategy based on a deep Bidirectional Gated Recurrent Unit (Bi-GRU) architecture. The method uses only the locally measured voltage and current signals at the relay bus, ensuring that the decision-making process remains independent of communication links or remote data exchange. Before being processed by the Bi-GRU network, the three-phase measurements undergo a discrete Fourier transform so that only the fundamental-frequency phasor information is fed into the learning model. A diverse training dataset is created by simulating numerous operating conditions that reflect real-world complexity, including different compensation percentages, a wide range of fault resistances, multiple fault positions, and both steady-state and power-swing scenarios. Additional electromagnetic transient simulations are carried out to verify the practical effectiveness of the proposed approach. The findings show that the developed protection scheme can reliably distinguish internal faults from external disturbances, identify the involved phases and fault category, and maintain consistent performance even during power swings.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70130","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149145942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive Event-Driven Transmission–Distribution System Coordination via Graph Neural Networks and Proactive Dispatch","authors":"Aamir Nawaz,&nbsp;Atiq Ur Rehman,&nbsp;Yasir Muhammad,&nbsp;Ehtasham Mustafa,&nbsp;Zahid Javid,&nbsp;William Holderbaum","doi":"10.1049/stg2.70125","DOIUrl":"https://doi.org/10.1049/stg2.70125","url":null,"abstract":"<p>This paper proposes a predictive event-driven framework for transmission–distribution system (TS-DS) coordination that leverages graph neural networks (GNNs) with multi-head temporal attention to forecast imminent congestion, voltage violations and contingency risks. A proactive dispatch layer solves a chance-constrained optimal power flow (CC-OPF) problem with moment-based ambiguity sets, pre-positioning generation and activating flexible demand before predicted violations materialise. The central novelty is a closed prediction–optimization loop in which calibrated, spatially resolved GNN event probabilities adaptively set the per-element chance-constraint risk levels in proportion to predicted severity—a coupling absent from prior GNN-OPF and distributionally robust dispatch work. Supporting contributions include: (i) a spatiotemporal GNN combining graph attention convolution with hierarchical TS-DS pooling and causal temporal attention and (ii) a risk-aware event-driven triggering protocol balancing event probability against expected violation severity. Cantelli's inequality provides a distribution-free SOC reformulation of individual chance constraints. Experiments on IEEE 118-bus/6 × 33-bus (316 buses) and Polish 2383-bus/20-DS (3743 buses) systems over five random seeds demonstrate <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>73.2</mn>\u0000 <mi>%</mi>\u0000 <mo>±</mo>\u0000 <mn>1.8</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $73.2%pm 1.8%$</annotation>\u0000 </semantics></math> voltage violation reduction, <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>68.0</mn>\u0000 <mi>%</mi>\u0000 <mo>±</mo>\u0000 <mn>1.6</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $68.0%pm 1.6%$</annotation>\u0000 </semantics></math> congestion reduction and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>11.5</mn>\u0000 <mi>%</mi>\u0000 <mo>±</mo>\u0000 <mn>0.4</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $11.5%pm 0.4%$</annotation>\u0000 </semantics></math> operational cost reduction versus state-of-the-art reactive methods, with sub-5-second computation on both test systems.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Agent Strategic Interactions and Game-Theoretic Modelling in EV-Integrated Smart Grids: A Review","authors":"Lixiao Wang,&nbsp;Lidan Chen,&nbsp;Jiaqi Li,&nbsp;Lei Wang,&nbsp;Ge Chen,&nbsp;Zhe Zhang","doi":"10.1049/stg2.70126","DOIUrl":"https://doi.org/10.1049/stg2.70126","url":null,"abstract":"<p>As electric vehicles (EVs) become increasingly integrated into smart grids, charging and discharging scheduling, aggregator coordination, charging-service competition and energy trading have become multi-agent strategic interactions among EV users, aggregators, charging stations and grid operators. Existing reviews often examine charging control, vehicle-to-grid services, charging infrastructure or local energy trading separately, but rarely map game-theoretic models to specific interaction structures in EV-integrated smart grids (EVISGs). This review addresses this gap by developing an interaction-structure-oriented framework organised around non-cooperative and cooperative paradigms. It compares seven model families: N-player and generalised Nash games; aggregative and mean-field games; Stackelberg games and strategic market-interaction models; Bayesian and evolutionary games; coalition-formation and coalitional games; cooperative scheduling and allocation models and Nash bargaining and Shapley-value-based trading models. Cross-model synthesis shows that these models characterise competitive response, hierarchical pricing, population coordination and cooperative benefit allocation, but remain constrained by simplified behavioural assumptions, privacy-limited information exchange, incomplete network–market coupling, real-time scalability and limited deployment-level validation. Future research should prioritise behaviourally calibrated modelling, network-aware trading, privacy-preserving coordination, learning-assisted equilibrium computation and customised mechanisms evaluated for equilibrium consistency and operational feasibility.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148994020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Power Flow Analysis Based on Distribution-to-Distribution Deep Neural Network","authors":"Pin-Xian Ye,&nbsp;Kin Cheong Sou","doi":"10.1049/stg2.70124","DOIUrl":"https://doi.org/10.1049/stg2.70124","url":null,"abstract":"<p>Probabilistic power flow (PPF) analysis characterises the probability distributions of power system states, such as bus voltages and branch power flows, under stochastic inputs arising from load variability and renewable generation, playing a key role in the planning and operation of modern power systems. However, the practical adoption of PPF is limited by the high computational cost of existing methods at inference time. This paper proposes an accelerated PPF solution method based on distribution-to-distribution (D2D) deep neural network (DNN) regression, which directly maps input probability distributions to the corresponding output distributions. Unlike Monte Carlo simulation (MCS)–based PPF methods, the proposed approach requires no sampling at inference time. As a proof-of-concept case study, the proposed method is evaluated on the benchmark 33-bus radial distribution feeder with solar photovoltaic generation uncertainty modelled using beta distributions. The results demonstrate that the D2D DNN closely approximates the MCS ground truth distributions, with average relative errors below 0.5% across all output quantities, while reducing average computation time from 36 to 0.03 s per PPF scenario.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional Converter With Transient Inductor Reconfiguration and Improved Control for Shipboard Pulsed Power Loads","authors":"Zi Tian,&nbsp;Luona Xu,&nbsp;Yongjie Luo,&nbsp;Jose Matas","doi":"10.1049/stg2.70128","DOIUrl":"https://doi.org/10.1049/stg2.70128","url":null,"abstract":"<p>Reliable power supply to pulsed power loads (PPLs) is of critical importance in DC shipboard microgrids. PPLs have high instantaneous power demands and vary rapidly; therefore, pulsed power supply (PPS) systems must be able to respond quickly to sudden changes in pulsed power in order to minimise output voltage fluctuations and ensure stable load operation. To address above issues, this paper proposes an improved active capacitor converter (ACC) topology with transient inductor reconfiguration based on a three-phase interleaved configuration, along with an improved fast inductor current control strategy. This method improves dynamic response capabilities, ensuring the stability of the output voltage. This paper provides a detailed analysis of the architecture, operating principles, control strategy, and parameter design of the proposed PPS. Finally, simulation results verified the effectiveness of the proposed method.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Energy Load Forecasting Framework for Integrated Energy Systems With Climate Resilience","authors":"NanTian Huang,&nbsp;YuJie Lv,&nbsp;Liang Zhang,&nbsp;Lin Lin","doi":"10.1049/stg2.70108","DOIUrl":"https://doi.org/10.1049/stg2.70108","url":null,"abstract":"<p>This paper proposes an MTL-TCN-Transformer model based on multi-task learning in response to the challenges including climate change faced by multi-load forecasting in integrated energy systems. To tackle the issues in multi-load forecasting, such as the increased volatility due to climate change and the complex coupling relationships among loads, the proposed model is designed with an integrated architecture that effectively extracts long-term temporal dependencies, captures correlations between loads and establishes global dependencies. By using the maximum information coefficient method for feature screening, nine features representing the correlation between loads, such as the ratio and difference between loads, were constructed to capture the complex correlations among multiple loads. The application of an adaptive weight update strategy suppresses fluctuations during the training process, enhancing the robustness and convergence speed of the model. The experimental results show that the model proposed in this paper exhibits better performance than the traditional methods in the four-season multi-load forecasting, with an average reduction of 67.952% in the MAPE of the electrical load. This paper provides a solution for high-precision multi-element load forecasting and points out future expansion directions such as enhancing cross-regional generalisation capabilities.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Power State Estimation in Internet of Things Grids via Unsupervised Clustering and Data-Driven Weighting","authors":"Mahmoud Zeidan,&nbsp;Sami A. Aldalahmeh,&nbsp;Ali M. Hayajneh,&nbsp;Yazan Al-Rawashdeh","doi":"10.1049/stg2.70101","DOIUrl":"https://doi.org/10.1049/stg2.70101","url":null,"abstract":"<p>This paper investigates power system state estimation utilising remote terminal units equipped with wireless IoT modules in lieu of legacy wired systems. However, this wireless sensing paradigm renders measurements highly susceptible to bad data caused by industrial impulsive noise. Traditionally, using weighted least squares state estimator (WLS) in such situations leads to degradation in performance. While several robust weighted least squares variants have been suggested in the literature—most notably the M-estimator-based iterative reweighted least squares (IRWLS) algorithm—they require considerable computational load. To address this, we propose an efficient two-stage framework. In the first stage, a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and with Gaussian mixture models (GMMs) are trained and deployed locally at the edge-sensor node to enable maximum a posteriori (MAP) detection and filtering of bad data. In the second stage, the central fusion centre collects the filtered measurements to construct the weight matrix required for the final WLS estimation. The proposed distributed algorithm, denoted as DGM-WLS, has been extensively simulated in the IEEE 30-bus test system demonstrating superior performance and resilience under high impulsive noise setting. On the local level, the DGM-WLS detector outperforms the standard Wald test while requiring the same linear computational complexity. On the global level, its estimation accuracy is 11% and 39% better than the IRWLS and WLS algorithms respectively. Moreover, it maintains a constant average convergence time of 5 iterations across all tested noise rates and is computationally 15 times faster than the IRWLS algorithm.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An End-To-End Large Language Model and Graph Neural Network Framework for Unified Load-Shedding-Based Power System Security and Stability Assessment","authors":"Yating Zhai,&nbsp;Pei Cao,&nbsp;Yichen Ying,&nbsp;Jianbin Zhang,&nbsp;Qingyan Zhu","doi":"10.1049/stg2.70111","DOIUrl":"https://doi.org/10.1049/stg2.70111","url":null,"abstract":"<p>With the increasing penetration of renewable energy and power electronic devices, power system security and stability assessment is challenged by heterogeneous data sources and fragmented evaluation criteria. To address this issue, this paper proposes a unified security–stability assessment framework by integrating a large language model (LLM) and a graph neural network (GNN). A unified security–stability score is defined based on the expected weighted load-shedding ratio, which considers <i>N</i> – 1 contingencies, equipment failure probabilities, renewable-induced weak-grid effects quantified by short-circuit ratio (SCR) constraints, and load priority. The designed LLM is used to standardize non-standard and heterogeneous operational data, achieving a data standardization accuracy of 97.49% while significantly reducing workflow design time. On this basis, a dual-head GraphSAGE model is developed to predict node-level and edge-level security scores. Case studies show that the proposed SCR-based and weighted scoring method provides a more conservative and realistic assessment of renewable-related risks. Moreover, the dual head GNN achieves high prediction accuracy for node and edge scores, respectively, outperforming single head architectures. The results demonstrate that the proposed framework enables fast, accurate, and interpretable security–stability assessment for renewable-rich power systems.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Deep Learning for Short-Term Appliance Load Forecasting: A Comparative Study of LSTM–SHAP and TFT Attention Models","authors":"Yankai Yang,&nbsp;Ahmed S. Musleh,&nbsp;Boyu Liu,&nbsp;Guanyu Wang,&nbsp;Junhao Ren","doi":"10.1049/stg2.70110","DOIUrl":"https://doi.org/10.1049/stg2.70110","url":null,"abstract":"<p>Accurate short-term residential appliance load forecasting is critical for modern power system operation. Household electricity demand shows strong variability, nonstationarity and sudden fluctuations, which make accurate and interpretable forecasting challenging. This paper investigates interpretable deep learning approaches for short-term residential appliance load forecasting using a long short-term memory (LSTM) network explained by SHapley Additive exPlanations (SHAP) and a Temporal Fusion Transformer (TFT) explained by built-in variable-selection and attention mechanisms. To ensure an objective-consistent point-forecasting comparison, an MSE-trained LSTM and an MSE-trained TFT are evaluated using the same data partition, feature-engineering procedure, feature-selection setting and evaluation protocol. Their point-forecasting performance is assessed using RMSE, MAPE and <i>R</i>². In parallel, a quantile-trained TFT is evaluated separately for probabilistic forecasting using the mean pinball loss, approximate CRPS, prediction interval coverage probability and mean prediction interval width. Results show that the MSE-trained TFT achieves lower point-forecasting errors than the LSTM under the loss-aligned setting. The quantile-trained TFT further provides reasonably calibrated uncertainty estimates, with an 80% prediction interval coverage probability of 0.815 and an absolute coverage error of 0.015. In addition, the TFT provides feature-level and temporal explanations through its variable-selection and attention mechanisms, whereas SHAP offers complementary post-hoc explanations for the LSTM. The study provides a task-consistent comparison of recurrent and Transformer-based forecasting models and demonstrates how point accuracy, uncertainty quantification and interpretability can be jointly considered in residential load forecasting.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}