Iet Generation Transmission & Distribution最新文献

{"title":"Power System Short-Term Voltage Stability Assessment Method Based on Spatial-Temporal Graph Attention Network","authors":"Hailiang Li,&nbsp;Zhuanglin Liang,&nbsp;Dexin Ma,&nbsp;Shiyuan Zhang,&nbsp;Weike Mo","doi":"10.1049/gtd2.70067","DOIUrl":"https://doi.org/10.1049/gtd2.70067","url":null,"abstract":"<p>Short-term voltage stability (STVS) assessment is critical for ensuring the operational security of modern industrial internet-based power systems. For data-driven STVS evaluation approaches, effectively leveraging both time-series data and topological structure of complex industrial power networks to extract critical spatial-temporal features remains a challenge. This paper introduces a novel spatial-temporal feature learning framework, termed spatial-temporal graph attention network (STGAT), which integrates graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU). In the framework, channel attention mechanism (CAM) is incorporated into the GAT to enhance spatial representation, while temporal attention mechanism is applied to the BiGRU to capture essential temporal features. By considering highly representative spatial-temporal correlations of power system dynamics, the recommended STGAT model delivers a fast, accurate, and robust classification framework for STVS assessment. Extensive testing on the IEEE 39-bus system validates the feasibility and preeminence of the recommended STGAT method compared to existing models, ensuring its suitability for online STVS assessment in industrial environments.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimised Scheduling for Distribution Networks, Microgrids and Demand-Side Using Multi-Level Game Theory","authors":"Xiangwu Yan,&nbsp;Heyang Cao,&nbsp;Chen Shao,&nbsp;Zehua Wang,&nbsp;Jiaoxin Jia","doi":"10.1049/gtd2.70058","DOIUrl":"https://doi.org/10.1049/gtd2.70058","url":null,"abstract":"<p>This paper introduces a multi-level leader-follower game scheduling method, which accounts for the coordinated interests of multiple stakeholders in the integrated operation of distribution networks (DNs), microgrids (MGs), and flexible loads (FLs) systems. Firstly, to address the challenges posed by significant non-linearity and computational burden in the DN game model, optimisation efficiency is enhanced from two perspectives. An improved Bayesian optimisation algorithm is implemented to reduce the number of optimisation iterations for electricity prices, while a graph theory-based forward-backward sweep method is employed to enhance power flow calculation efficiency. Secondly, distinct optimisation strategies are applied to MGs with varying operational profiles: the internet data centre MGs implement the differential evolution algorithm, while the time-shift load MG employs the covariance matrix adaptation evolution strategy, striking a balance between computational speed and solution accuracy for the MG cluster. Moreover, the demand response load layer is modelled and solved as a linear optimisation problem. Finally, based on the IEEE-33 bus test case analysis, the proposed model fully integrates photovoltaic energy, resulting in a 24.5% improvement in the system's overall economic performance and increasing the benefits for each stakeholder.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive Identification of Distribution Network Topology Based on User-Side Internet of Things Device Measurement Data","authors":"Lei Zhu,&nbsp;Shutan Wu,&nbsp;Qi Wang,&nbsp;Yang Li","doi":"10.1049/gtd2.70064","DOIUrl":"https://doi.org/10.1049/gtd2.70064","url":null,"abstract":"<p>The flexibility requirements of network topologies in advanced distribution networks (ADNs) have led to increasingly complex distribution network topologies, posing a significant challenge for topology identification. The integration of a vast array of internet of things (IoT) devices at existing distribution network terminals facilitates the real-time collection and transmission of physical measurement data, thus providing new ideas for identifying distribution network topologies. This paper introduces a distribution network topology progressive identification method grounded in measurement data captured by IoT devices. First, the study employs similarity mining of IoT measurement data to identify the topological connection relationships within the low-voltage distribution network. Second, by integrating real-time measurement data with historical data, the medium-voltage-side data are consolidated, and the topology of the medium-voltage distribution network is identified by an inverse power flow model based on a fixed parameter ratio. Furthermore, to address the limitations of low recognition accuracy and non-unique recognition outcomes associated with the aforementioned methods, this study examines the regional similarity among distribution networks and proposes a hyperparameter to increase the recognition accuracy. Finally, different case studies show that the proposed method maintains a topological identification accuracy of more than 80%, and shows a wide range of applicability in different types and sizes of distribution networks.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric Thermal Balance Control Method of Plant Level Integrated Energy Systems Based on Robust Linear Optimization With Probability Density Bias Prediction Characteristics","authors":"Jiakui Shi,&nbsp;Menghui Li,&nbsp;Shuangshuang Fan,&nbsp;Kun Yao,&nbsp;Jie Wan,&nbsp;Peng E","doi":"10.1049/gtd2.70051","DOIUrl":"https://doi.org/10.1049/gtd2.70051","url":null,"abstract":"<p>The rapid development of renewable energy has promoted the research of the integrated energy system. In particular, the joint optimal scheduling of renewable energy and traditional thermal power units is the key technology to solve the current renewable energy integration and the source network load balance. At present, the total installed capacity of cogeneration units is huge, which hinders the integration of renewable energy and the flexibility of the power grid regulation. Therefore, this paper proposes a robust optimal scheduling strategy for the plant-level integrated energy system considering electricity-heat balance. The wind power and solar energy are connected in parallel with the heating network of cogeneration units to decouple heat and power in a more economical and flexible way, while improving the integration of renewable energy. Based on the principle of Kang's robust optimization, robust optimization, a linear robust optimization probability density bias prediction optimization method for the plant-level integrated energy system considering the electric-heat balance is proposed in this paper. The dynamic nonlinear constraints in the output process of thermal and electrical loads are transformed into linear, which takes into account the conservatism of robust optimization and the economy of the objective function. The proposed optimization algorithm facilitates the integration of a high proportion of renewable energy into the power grid and is also applicable to other integrated energy systems with nonlinear constraint characteristics.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration","authors":"Kaleem Ullah,&nbsp;Daniyal Shakir,&nbsp;Usama Abid,&nbsp;Saad Alahmari,&nbsp;Sheraz Aslam,&nbsp;Zahid Ullah","doi":"10.1049/gtd2.70060","DOIUrl":"https://doi.org/10.1049/gtd2.70060","url":null,"abstract":"<p>Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast and Scalable Iterative Solution of a Socio-Economic Security-Constrained Optimal Power Flow With Two-Stage Post-Contingency Control","authors":"Matias Vistnes,&nbsp;Vijay Venu Vadlamudi,&nbsp;Oddbjørn Gjerde","doi":"10.1049/gtd2.70055","DOIUrl":"https://doi.org/10.1049/gtd2.70055","url":null,"abstract":"<p>Power systems must accommodate faster-growing demand and energy production at a rate that exceeds the pace of new grid infrastructure development. Moving from the deterministic ‘N-1’ security criterion to a probabilistic security criterion in security-constrained optimal power flow (SCOPF) can safely increase the power transfer capability of power systems. However, this has been computationally intractable for large power systems when including corrective actions. In this paper, a fast and scalable iterative methodology for solving the SCOPF problem is proposed using problem decomposition and the inverse matrix modification lemma (IMML). The proposed probabilistic corrective-SCOPF formulation tackles system operational security planning by combining previous research with considerations of short-term and long-term post-contingency limits, probability of branch outages, and preventive and corrective actions. Using two post-contingency states and contingency probabilities, the SCOPF could provide improved system security at a lower cost when compared to the SCOPF with only preventive actions, for example, the typical ‘N-1’ formulation. Additional security is ensured using a post-contingency load-shedding limit constraint based on system operator policy. The bearing idea in the proposed solution methodology is to relax the problem and then iteratively add constraints as and when they are violated, resulting in a solution that satisfies all constraints in the original problem. Solving the post-contingency power flow using the IMML with bus voltage angles was found to be up to four orders of magnitude faster than doing the same using a high-performance sparse matrix solver (KLU) with power transfer distribution factors. The proposed methodology is applied to a range of test systems containing up to 10,000 buses with a computational time of up to 3375 s for 12,706 branch contingencies. Calculating the contingency power flows takes 1.3% of the total solution time using the proposed methodology, by exploiting the IMML.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal relationship discovery and causal-oriented approaches for enhanced performance and interpretability in prediction of prosumer behavior and demand flexibility","authors":"Mingyue He,&nbsp;Mojdeh Khorsand","doi":"10.1049/gtd2.13319","DOIUrl":"https://doi.org/10.1049/gtd2.13319","url":null,"abstract":"<p>Causal analysis paves the way for more interpretable assessment of complex systems and phenomena, such as human-in-the-loop components of energy systems. This article will pursue novel approaches for causal analysis of prosumers' behavior. The knowledge of this causality is core for multiple smart grid applications including but not limited to the design of demand side management programs, retail electricity market design, development of effective distributed energy resources aggregation strategies, and net load forecasting. The complex nature of human interactions with energy relies on many factors and understanding behavior causality is a core, unsolved challenge. This article presents a probabilistic algorithm for discovering causal relationships between the end users' consumption flexibility and its influencing factors. The obtained causal knowledge is then utilized to boost the precision of demand flexibility prediction. Two causal-oriented approaches are proposed to enhance the performance and interpretability of predictive models, incorporating causal information through causal regularization and data preprocessing. Simulation results demonstrate that the algorithm can effectively identify causal probabilities among different factors and unveil key characteristics of the prosumers' behavior. Additionally, these proposed causal-oriented approaches outperform the non-causal-oriented predictive models in terms of both performance and interpretability, highlighting the advantages of incorporating causal information.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Hybrid Event-Based System Separation and Response-Driven Under-Frequency Load Shedding Scheme in a Standalone Power System With Renewables","authors":"Ying-Yi Hong,&nbsp;Hsaio-Chu Huang","doi":"10.1049/gtd2.70054","DOIUrl":"https://doi.org/10.1049/gtd2.70054","url":null,"abstract":"<p>The increasing integration of renewable energy sources into power systems, driven by their clean and sustainable attributes, has attracted significant attention. However, during severe contingency events, renewable resources such as photovoltaics (PV) may trip prematurely due to their own protection strategies, potentially occurring before load shedding and leading to cascading outages. This paper presents a novel hybrid special protection scheme (SPS) for autonomous (standalone) power systems with renewables. The proposed approach combines an event-based system separation strategy with a response-driven under-frequency load shedding (UFLS) strategy to mitigate premature renewables tripping, thereby preventing catastrophic cascading load shedding. Hybrid particle swarm optimization is employed to determine the optimal splitting points in the power system. Additionally, the total shed loads at all steps of 81L relays in the separated areas are minimized, while the average frequency nadir across all separated areas is maximized. The uncertainty in PV power generation is modeled using probabilistic methods to account for various scenarios. The effectiveness of the proposed method is demonstrated using a 31-bus standalone power grid with a total demand of 66.62 MW and PV power generation of 7.61 MW. The results show that the proposed method sheds only 11.30 MW of load and trips 2.18 MW of PV power generation, compared to 25.28 MW of load shedding and 6.47 MW of PV tripping observed in existing methods under high irradiance conditions.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodology to Analyse the Feasible Use of Battery Energy Storage Systems as Part of Electricity Distribution Network Asset Management","authors":"Juha Haakana,&nbsp;Jouni Haapaniemi,&nbsp;Otto Räisänen,&nbsp;Jukka Lassila","doi":"10.1049/gtd2.70056","DOIUrl":"https://doi.org/10.1049/gtd2.70056","url":null,"abstract":"<p>Battery energy storage systems (BESS) have become a part of electrical power systems, especially in providing system services. In addition, storage systems offer many benefits also for the electricity distribution network operation, even though they are not yet widely utilised. In this paper, a methodology to assess the feasible use of BESS in the continuous distribution network development and asset management process is developed. The methodology determines alternative paths for the network development, considering the present network structure, requirements for the network operation, and the life cycle cost analysis, including capital, operational, and interruption costs of the network. Network development cases where BESS can be beneficial include a rapid improvement in network capacity and securing electricity supply against network fault interruptions. The analysis focuses on the renovation of a rural overhead line network, but the methodology is generic and can also be applied to urban areas.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MDS-YOLO Model-Based Defect Detection Method for Porcelain Insulators Using Infrared Images","authors":"Shaotong Pei,&nbsp;Weiqi Wang,&nbsp;Chenlong Hu,&nbsp;Haichao Sun,&nbsp;Hongyu Di,&nbsp;Bo Lan,&nbsp;Bing Xiao","doi":"10.1049/gtd2.70032","DOIUrl":"https://doi.org/10.1049/gtd2.70032","url":null,"abstract":"<p>With the rapid development of image processing technology in recent years, the detection of insulator defects through infrared images has become an important online inspection technology. In practice, the insulator infrared image shooting needs to deal with shooting angle, background complexity, and other issues that decrease the detection accuracy. Also, small targets are difficult to identify and the detection of defects remains a problem. In order to solve these issues, this paper proposes a small target multiple defects YOLO algorithm. Based on YOLOv8, a hybrid model of self-attention and convolution is used to aggregate convolution and self-attention. Then efficient convolutional network (EfficientNetV2), is applied to improve the training speed of the model and the parameter efficiency, to ensure that the model is lightweight as a whole. And adopting a bi-directional feature pyramid network to improve accuracy through multi-level feature pyramids and bi-directional information transfer. The multilevel feature pyramid and bidirectional information transfer are adopted to improve the precision. Finally, the inner-SIoU loss function is used to improve the recall and precision of the small targets and enhance the robustness of the model to small targets. In order to obtain test data, this paper conducts defective insulator infrared image experiments to obtain infrared images under different conditions. After experimental verification, the MDS-YOLO algorithm proposed in this paper achieves an average of 87.85% mAP and 6.0 GFLOPs, which meets the requirements of recognising defective insulators with small targets and the effectiveness and superiority of the algorithm proposed in this paper are proved by ablation and comparison tests.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}