IET Smart Grid最新文献_第2页

Weather-Aware Reinforcement Learning for Power Grid Topology Control Under Extreme Events 极端事件下电网拓扑控制的天气感知强化学习

IF 3.1

IET Smart Grid Pub Date : 2026-07-06 DOI: 10.1049/stg2.70099

Fan Tang, Yudong Wang, Yanpo Zhang, Hancong Guo, Shuping Gao, Chao Shen, Zongyan Zhang

{"title":"Weather-Aware Reinforcement Learning for Power Grid Topology Control Under Extreme Events","authors":"Fan Tang, Yudong Wang, Yanpo Zhang, Hancong Guo, Shuping Gao, Chao Shen, Zongyan Zhang","doi":"10.1049/stg2.70099","DOIUrl":"10.1049/stg2.70099","url":null,"abstract":"Extreme weather and high renewable penetration increase the vulnerability of power system operations to nonstationary, heavy-tailed disturbances. Most deep reinforcement learning (DRL) controllers for topology control are trained under nominal conditions and treat weather as a latent disturbance, leading to sharp performance degradation or even worse-than-Do-Nothing behaviour in extreme regimes. This paper proposes a weather-aware soft actor–critic (WA-SAC) framework that makes weather an explicit, structured input to corrective topology control in RES-rich grids. The framework couples: (i) an LLM-driven extreme-weather data augmentation pipeline that generates scenario-oriented, physically consistent renewable and load trajectories via lifecycle modulation and two-pass load balancing; (ii) an 8-dimensional hybrid weather encoding (6D categorical type, intensity, and trend) and a weather-conditioned graph-attention actor–critic that learns adaptive topology actions; and (iii) a deployment-oriented dual-path weather inference scheme supporting the workflow of training with oracle weather codes and deploying with inferred weather codes. Experiments on Grid2Op WCCI 2022 (IEEE 118-bus) with eight extreme-weather categories show that LLM-generated data is 11.7% more challenging than scaling-based alternatives on Do-Nothing survival; weather encoding improves extreme-scenario performance by 18.6% over the no-encoding baseline with scenario-dependent behaviour in mild regimes; and LLM-inferred weather codes achieve an 88% functional match rate to oracle performance under a 10% degradation tolerance, validating the proposed workflow for deployment.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462801","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}

引用次数: 0

Impedance Modelling and Stability of Grid-Connected Inverters Based on Lyapunov Function Control 基于Lyapunov函数控制的并网逆变器阻抗建模与稳定性研究

IF 3.1

IET Smart Grid Pub Date : 2026-06-28 DOI: 10.1049/stg2.70100

Wenlong Zhang, ShaoHua Sun, Zhenwei Li, Qingkai Chen, Xiao Zhang

{"title":"Impedance Modelling and Stability of Grid-Connected Inverters Based on Lyapunov Function Control","authors":"Wenlong Zhang, ShaoHua Sun, Zhenwei Li, Qingkai Chen, Xiao Zhang","doi":"10.1049/stg2.70100","DOIUrl":"https://doi.org/10.1049/stg2.70100","url":null,"abstract":"Impedance-based approaches can be employed to address harmonic stability issues in inverter grid-connected systems. The system stability can be analysed and improved by constructing an impedance model for the inverter and then performing impedance reshaping. The Lyapunov-Function-based Control (LFC) strategy ensures global stability of the inverter grid-connected system under large-signal transient conditions. However, research on the impedance modelling and stability analysis for inverters using this method remains insufficient. To address the harmonic stability issue of the inverter based on LFC under weak grid conditions, this article first establishes a mathematical model for the inverter's output impedance. Then the influence of various parameters on the stability of the inverter grid-connected system is analysed based on the impedance model. Furthermore, a method for impedance reshaping is proposed to improve system stability by tuning the active damping coefficient. Finally, simulations and experiments are conducted to validate the theoretical output impedance model and the proposed stability enhancement approach.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386847","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}

引用次数: 0

Reinforcement Learning in Microgrid Energy Management: Review, Methods and Prospects 微电网能源管理中的强化学习：综述、方法与展望

IF 3.1

IET Smart Grid Pub Date : 2026-06-18 DOI: 10.1049/stg2.70097

Muhammad Fahad Zia, Usman Inayat, Muhammad Junaid Anjum, Haris M. Khalid, Sheroze Liaquat

{"title":"Reinforcement Learning in Microgrid Energy Management: Review, Methods and Prospects","authors":"Muhammad Fahad Zia, Usman Inayat, Muhammad Junaid Anjum, Haris M. Khalid, Sheroze Liaquat","doi":"10.1049/stg2.70097","DOIUrl":"https://doi.org/10.1049/stg2.70097","url":null,"abstract":"A rapid integration of distributed energy resources and electrification has increased the need for intelligent energy management in microgrids under grid-connected and islanded operation. The variability of renewables, energy storage system cycle limits and market interactions are primary drivers for learning-based coordination in microgrids. Therefore, this papers presents a comprehensive review of reinforcement learning and deep reinforcement learning methods for microgrid energy management. The comparison of RL and DRL methods is provided for MG EMS applications. Multiagent DRL frameworks and constrained safe RL architectures are also discussed. The states, actions, rewards, evaluation metric and grid-connectivity are also presented for reinforcement learning based microgrid energy management systems. Finally, limitations of existing methods are identified, including challenges related to reward function design, partial observability, uncertainty management, cybersecurity and real-world deployment along with future research directions.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282294","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}

引用次数: 0

Equivalence of Rights and Responsibilities in Capacity Mechanisms Under Cross-Provincial Electricity Trading: Problem Analysis and Improvement 跨省电力交易下容量机制的权责对等：问题分析与改进

IF 2.7

IET Smart Grid Pub Date : 2026-06-09 DOI: 10.1049/stg2.70095

Haolin Wang, Ce Zhou, Xujia Yin, Xianlin Wang, Fuwang Yang

{"title":"Equivalence of Rights and Responsibilities in Capacity Mechanisms Under Cross-Provincial Electricity Trading: Problem Analysis and Improvement","authors":"Haolin Wang, Ce Zhou, Xujia Yin, Xianlin Wang, Fuwang Yang","doi":"10.1049/stg2.70095","DOIUrl":"https://doi.org/10.1049/stg2.70095","url":null,"abstract":"Cross-provincial electricity trading in China expands the spatial scope of resource allocation and allows the adequacy benefits provided by provincial capacity mechanisms to extend beyond local jurisdictions. However, the costs of such mechanisms are still largely recovered within the provinces where they are implemented, giving rise to cross-provincial cost leakage and a mismatch between the beneficiaries of capacity adequacy and the entities bearing the associated costs. This mismatch distorts market incentives and weakens the effectiveness of long-term supply adequacy signals in cross-provincial trading. To address this issue, this paper reveals the formation mechanism of the rights–responsibilities mismatch in capacity mechanisms under coupled cross-provincial electricity trading and analyzes its critical impact on market incentive signals and power supply security. Based on this analysis, an improvement scheme for fairer capacity cost allocation is proposed, which is compatible with the current market operation environment. The proposed scheme corrects cross-provincial market incentive signals by allowing capacity costs to be transmitted to external beneficiaries that actually receive adequacy support through cross-provincial transactions, thereby achieving a cross-provincial match between costs and benefits. Simulation results demonstrate that the proposed method improves the fairness of capacity cost sharing and mitigates distorted trading incentives. This study provides a useful reference for the design and operation of cross-provincial electricity markets in the presence of non-localised capacity adequacy benefits.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237701","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}

引用次数: 0

Artificial Intelligence in Bidirectional Energy Management: A Critical Review of Control Strategies for V2G and G2V Systems 双向能量管理中的人工智能：V2G和G2V系统控制策略综述

IF 2.7

IET Smart Grid Pub Date : 2026-06-08 DOI: 10.1049/stg2.70093

Gajendra Singh Chawda, Shivanshu Shekhar, Guilherme Vieira Hollweg, Wencong Su, Mengqi Wang, Mandoye Ndoye, Ben Oni

{"title":"Artificial Intelligence in Bidirectional Energy Management: A Critical Review of Control Strategies for V2G and G2V Systems","authors":"Gajendra Singh Chawda, Shivanshu Shekhar, Guilherme Vieira Hollweg, Wencong Su, Mengqi Wang, Mandoye Ndoye, Ben Oni","doi":"10.1049/stg2.70093","DOIUrl":"https://doi.org/10.1049/stg2.70093","url":null,"abstract":"The growing adoption of electric vehicles (EVs) and renewable energy sources has accelerated the need for intelligent bidirectional energy management systems. Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) technologies offer promising solutions to enhance grid stability, optimise energy use and leverage EVs as mobile storage units. However, real-time control of these systems remains challenging due to the dynamic nature of power flows, user behaviour and energy demand uncertainties. This review explores how Artificial Intelligence (AI) and Machine Learning (ML) methods–such as supervised learning, reinforcement learning, deep learning and hybrid models–address these challenges. This paper highlight key applications, recent case studies, and experimental advances in AI-driven V2G and G2V control, emphasising improvements in system efficiency, battery health and grid reliability. Critical challenges including data quality, real-time computation, and cybersecurity are discussed, along with future directions such as Explainable AI, blockchain integration, and lightweight decentralised models. This review aims to present a critical overview of AI-enabled control strategies for V2G and G2V systems and to guide future innovations in sustainable and intelligent energy management.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237507","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}

引用次数: 0

Uncertainty-Driven Ensemble Framework for Efficient Ultra-Short-Term Renewable Power Prediction 高效超短期可再生能源预测的不确定性驱动集成框架

IF 2.7

IET Smart Grid Pub Date : 2026-06-02 DOI: 10.1049/stg2.70091

Jinge Song, Jun Zhao, Chaoying Yang, Xiao Chang

{"title":"Uncertainty-Driven Ensemble Framework for Efficient Ultra-Short-Term Renewable Power Prediction","authors":"Jinge Song, Jun Zhao, Chaoying Yang, Xiao Chang","doi":"10.1049/stg2.70091","DOIUrl":"https://doi.org/10.1049/stg2.70091","url":null,"abstract":"The rapid expansion of renewable energy and the growing role of electricity market trading have created an urgent demand for ultra-short-term probabilistic forecasting of wind and solar generation. Existing studies often emphasise deterministic accuracy, yet lightweight and deployment-friendly approaches for calibrated uncertainty modelling remain scarce. This paper proposes a practical framework for ultra-short-term probabilistic forecasting based on a Swin Transformer backbone combined with a learnable perturbation strategy. Unlike conventional ensemble methods that require training multiple models, the proposed approach introduces adaptive sample-level disturbances into a single deterministic predictor, enabling efficient uncertainty quantification without additional training overhead. The framework is validated on 15-min resolution wind and solar power data from a region with diverse meteorological and spatial conditions. Results show that the disturbance-based ensemble achieves reliable predictive intervals while preserving high deterministic accuracy, supporting risk-aware scheduling and energy management in renewable-rich power systems. The proposed method offers a scalable and computationally efficient alternative for uncertainty-aware forecasting in large-scale zero-carbon energy applications.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148173872","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}

引用次数: 0

Source–Grid–Load Coordinated Resilience Enhancement for Distribution Networks Considering the Flexibility of Data Centre Resources Under Fault Conditions 考虑故障条件下数据中心资源灵活性的配电网源网负荷协同弹性增强

IF 2.7

IET Smart Grid Pub Date : 2026-05-22 DOI: 10.1049/stg2.70088

Qi Wang, Ziyi Zhang, Changping Yu, Shutan Wu, Jianxiong Hu, Yi Tang, Miao Zhang

{"title":"Source–Grid–Load Coordinated Resilience Enhancement for Distribution Networks Considering the Flexibility of Data Centre Resources Under Fault Conditions","authors":"Qi Wang, Ziyi Zhang, Changping Yu, Shutan Wu, Jianxiong Hu, Yi Tang, Miao Zhang","doi":"10.1049/stg2.70088","DOIUrl":"https://doi.org/10.1049/stg2.70088","url":null,"abstract":"With increasing renewable energy penetration and more frequent extreme weather events, fault occurrences in distribution networks (DNs) have risen significantly, highlighting the urgent need to enhance distribution system resilience. However, existing fault recovery strategies are constrained by the lack of a coordinated scheduling mechanism across multiple resource types. To address this limitation, this paper proposes a source–grid–load (SGL) coordinated resilience enhancement strategy that leverages the spatiotemporal flexibility of data centre (DC) resources. A comprehensive SGL scheduling framework is established: on the source side, a mobile emergency generator (MEG) dispatch model is developed for rapid active power support; on the grid side, a coordinated optimisation model for network reconfiguration and islanding is formulated to enhance topological adaptability; on the load side, a DC workload migration model is proposed to flexibly regulate demand. A multiobjective optimisation problem is then constructed to minimise load shedding and DC operating costs, which is linearised and convexified for tractable solving. Case studies conducted on modified IEEE 33-bus and PG&E 69-bus systems under various fault scenarios validate the proposed method. Comparative results show that the SGL-coordinated strategy significantly improves DN resilience and operational efficiency.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087940","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}

引用次数: 0

IF 2.7

IET Smart Grid Pub Date : 2026-05-22

引用次数: 0

A Review on AI-Driven Energy Consumption Forecasting for Smart Buildings 基于ai的智能建筑能耗预测研究进展

IF 2.7

IET Smart Grid Pub Date : 2026-05-16 DOI: 10.1049/stg2.70082

Sundus Munir, Muhammad Tayyab Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Adnan Khan, Mamoon M. Saeed, Habib Hamam

{"title":"A Review on AI-Driven Energy Consumption Forecasting for Smart Buildings","authors":"Sundus Munir, Muhammad Tayyab Khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Adnan Khan, Mamoon M. Saeed, Habib Hamam","doi":"10.1049/stg2.70082","DOIUrl":"https://doi.org/10.1049/stg2.70082","url":null,"abstract":"Smart buildings integrate advanced technologies with user-friendly interfaces to optimise energy consumption while enhancing occupant comfort. Achieving sustainable living in smart homes relies on motion sensors, infrared sensors, and other intelligent systems that automate and regulate energy usage. While these technologies improve user experience, reducing energy consumption remains a critical challenge for stakeholders committed to sustainability. This paper presents different AI methods for energy consumption forecasting. This review also provides a comparative analysis of various AI-driven, deep learning and machine learning approaches, evaluating their effectiveness in forecasting energy demand and promoting sustainability in smart buildings. This review also gives an overview of predictions on the amount of energy smart buildings will use to achieve the goal of making them more energy-efficient The study also looks at different ways to predict the future for both residential and non-residential buildings.","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148080871","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}

引用次数: 0

IF 2.7

IET Smart Grid Pub Date : 2026-05-16

引用次数: 0