IET Smart Grid最新文献

{"title":"Interpretable Machine Learning-Based Intelligent High-Frequency Static Security Adjustment Method for Power System Operations","authors":"Shiqi Duan,&nbsp;Xinyu Liu,&nbsp;Yu Lei,&nbsp;Juan Yu,&nbsp;Yanan Yu,&nbsp;Zhifang Yang","doi":"10.1049/stg2.70074","DOIUrl":"10.1049/stg2.70074","url":null,"abstract":"<p>The high penetration of renewable energy exacerbates stochastic fluctuations on both the generation and demand sides. It results in spatiotemporally frequent static security violations and requires high-frequency assessment and adjustment of the operation mode. However, conventional offline verification and manual adjustment methods fail to meet this requirement. Therefore, this paper proposes an interpretable machine learning-based intelligent high-frequency static security adjustment method for power system operations. First, a multitask XGBoost collaborative framework-based static security analysis model is developed by utilising power flow characteristics under the base case (<i>N</i> state) and various <i>N</i> − 1 contingency scenarios (<i>N</i> − 1 states) as input–output features. It can efficiently and accurately evaluate the power flow violation status under both the <i>N</i> state and various <i>N</i> − 1 states. Second, when operational modes exhibit power flow violation status, an intelligent adjustment methodology based on the SHAP (SHapley Additive exPlanations) interpretability framework is developed to quantify input feature contributions to security risks and identify key factors and their causality-driven mechanisms. Then, it generates transparent decision logic and effective adjustment strategies. Finally, case studies on the IEEE 30-bus system, a practical 341-bus grid and a practical 1834-bus grid validate that the proposed method not only achieves efficient and accurate static security assessment but also provides interpretable adjustment schemes for diverse operating states, thereby offering operators clear decision-making guidance.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566780","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148081165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Coordination of All-Electric Buildings for Providing Energy Flexibility and Frequency Regulation Services in Electricity Markets","authors":"Sevda Zeinal Kheiri,&nbsp;Mohammad Amin Mirzaei,&nbsp;Masood Parvania","doi":"10.1049/stg2.70078","DOIUrl":"10.1049/stg2.70078","url":null,"abstract":"<p>Electrified buildings with intelligent systems are becoming controllable energy resources capable of providing flexibility to the grid while supporting larger carbon reduction efforts. This paper presents an optimal coordination model for a community of all-electric buildings to offer energy flexibility and regulation capacity to the electricity market. The proposed model adopts an agent-based framework in which each building independently optimises its resources whilst coordinating with an aggregator agent that interfaces with the electricity market. Each building integrates fully electric air and water heating, through a central cold climate air source heat pump (ccASHP) and a heat pump water heater (HPWH) system. The proposed model prioritises the occupants' comfort whilst optimising cost savings and contributing to grid services. Unit-specific line heat losses are incorporated into the central ccASHP model, enhancing the precision of the estimated available capacity for grid services. A comprehensive study is conducted within 28-unit buildings to assess the performance of the proposed model. The numerical results indicate significant savings in electricity bills by offering energy flexibility and regulation up and down capacity to the market via the community of all-electric buildings.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566836","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148081189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Physics-Constrained and Nature-Inspired Reinforcement Learning With Adaptive Model Predictive Control for Multi-Machine Renewable Rich Microgrids","authors":"Neelofar Shaukat,&nbsp;Md. Rabiul Islam,&nbsp;Syed Ayaz Ali Shah,&nbsp;Jamil Ahmad Khan","doi":"10.1049/stg2.70075","DOIUrl":"10.1049/stg2.70075","url":null,"abstract":"&lt;p&gt;This research study proposes a physics-constrained nature-inspired (PCNI) reinforcement learning (RL) and model predictive control (MPC) strategy that simultaneously incorporates a trigonometric Lyapunov function into both RL's training loss functions and reward shaping, that is, dual role of physics. The proposed control synergy features a unique structure, that is, a physics-informed RL supervisor acts as a meta-optimiser, dynamically adjusts the MPC's cost function weights and innovatively its prediction horizon in real-time. The RL agent is trained with a proposed custom loss function, incorporating Lyapunov constraints, system physics, and operational limits for innovative, safe, and physically plausible learning. This means that, unlike employing heuristic reward shaping, this paper introduces a concept where physics is explicitly integrated into the training process. Inspired by the natural species' adaptability, such as birds adjust their flight trajectories and reaction distance under disturbances, the proposed framework dynamically reshapes the prediction horizon and cost function weights to preserve system stability. The robustness of the proposed control architecture is validated on a multi-machine nonlinear microgrid model under severe and recurring disturbances through a rigorous simulation environment with random perturbations every 4 seconds across seven distinct initial conditions. A conventional droop, MPC, and RL controllers are included as a baseline reference for comparative analysis. Simulation results illustrate consistently bounded frequency and voltage responses, and stringent compliance with operational limits for the proposed control scheme. The proposed control methodology achieves: (a) &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;92.90&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $92.90mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; stability rate compared with &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;3.8&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $3.8mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for droop, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.7&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1.7mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for MPC, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;1.3&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt; $1.3mathit{%}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for RL, (b) average frequency RMSE of 0.041 pu compared with 0.029 pu for droop, 0.057 pu for MPC and 0.062 pu for RL, (c) voltage RMSE of 0.0062 pu compared with 0.018","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566806","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":"Meteorology-Informed Coordinated Operation of Low-Carbon Park Energy System Considering Electric Vehicle Clusters and Energy Storage","authors":"Honghui Zhang,&nbsp;Dejie Zhao,&nbsp;Limin Jia","doi":"10.1049/stg2.70065","DOIUrl":"10.1049/stg2.70065","url":null,"abstract":"<p>With the increasing integration of electric vehicles (EVs) and renewable energy in industrial parks, achieving coordinated low-carbon operation of park-level energy systems has become a critical challenge. Meteorological conditions introduce significant uncertainty in renewable generation, making its effective management pivotal to system operation. This paper proposes a meteorology-informed price-based bilevel optimisation framework. In the upper level, the operator minimises the total operating cost and carbon emissions of the park by considering fuel expenditure, grid transactions and carbon trading. The optimised price is endogenously determined to reflect the marginal supply cost together with the carbon shadow price. In the lower level, the aggregated EV cluster and energy storage system (ESS) respond to this price by scheduling their charging and discharging behaviours to achieve both economic efficiency and carbon reduction. The model jointly optimises economic dispatch, carbon mitigation and price coordination among multiple energy resources. Case studies based on an actual industrial park verify the effectiveness of the framework. The results demonstrate that it significantly reduces total carbon emissions, enhances renewable energy utilisation and improves flexibility in energy scheduling. Overall, the proposed framework provides a practical and scalable solution for intelligent and low-carbon operation of modern industrial parks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564705","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148072684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Forecasting Framework for EV Charging Demand Using Variational Mode Decomposition and Louvain Community Detection","authors":"Qiong Wang,&nbsp;Linru Jiang,&nbsp;Qinghe Sun,&nbsp;Chenjie Yan,&nbsp;Jiawei Li,&nbsp;Xi Zhang","doi":"10.1049/stg2.70062","DOIUrl":"https://doi.org/10.1049/stg2.70062","url":null,"abstract":"<p>Accurate short-term forecasting of electric vehicle (EV) charging demand is crucial for demand-side management and grid stability in modern power systems, especially within the context of virtual power plant (VPP) operations. However, EV load profiles exhibit strong stochasticity and multi-scale variability, making traditional single-model predictors prone to overfitting, mode mixing, or degraded performance under shifting operating conditions. This study proposes a hybrid decomposition–clustering–adaptive forecasting framework that integrates variational mode decomposition (VMD), Louvain community detection and a lightweight adaptive model pool. First, VMD decomposes the raw load signal into mode components with reduced frequency overlap. Second, a correlation-based similarity graph is constructed and processed by the Louvain algorithm to automatically group modes with coherent temporal characteristics. Finally, an adaptive prediction mechanism selects or refines models for each community based on a normalised MSE threshold. Experiments on three real-world charging-station datasets show that the proposed method significantly improves forecasting performance, achieving a 60.78%–75.18% reduction in MAPE compared with conventional single-model baselines, and a 40.55%–53.70% improvement compared with nonadaptive VMD–LSTM schemes, while maintaining manageable computational cost. These results demonstrate the framework's robustness and its potential applicability to real-time EV charging management and other nonstationary energy forecasting tasks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563769","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":"Screening Distribution Networks for Loss Minimum Reconfiguration: Leveraging Demonstration Insights for Statistical Deployment Prioritisation","authors":"Kei Hagiwara,&nbsp;Sakura Ami,&nbsp;Yu Fujimoto,&nbsp;Tsunayoshi Ishii,&nbsp;Yasuhiro Hayashi,&nbsp;Shingo Uchiyama,&nbsp;Kohei Oishi,&nbsp;Kenjiro Mori","doi":"10.1049/stg2.70067","DOIUrl":"10.1049/stg2.70067","url":null,"abstract":"<p>Loss minimisation reconfiguration technology, which aims to reduce power losses by operating sectionalising switches in distribution systems (DSs), plays a critical role in modern power systems. However, implementing this technology requires comprehensive modelling that reflects the physical characteristics of actual distribution networks making its widespread deployment challenging. To effectively disseminate this technology, we propose a statistical screening approach to prioritise DSs based on their expected loss reduction potential. The screening framework is implemented using multiple statistical and machine learning models, including linear regression, support vector regression, random forest, an uncertainty–aware selection strategy and the Bradley–Terry model. Our method leverages these models to estimate the potential for loss reduction from routinely available physical and operational data by system operators using results from a limited number of deployments. Specifically, we analyse the results of a technical demonstration on loss minimisation in distribution networks conducted by TEPCO between 2019 and 2020 and propose a screening approach that accounts for complex spatiotemporal variations in power flows. Numerical experiments over a 20-year deployment horizon demonstrate that the proposed screening framework substantially outperforms random system selection, achieving a cumulative loss reduction that is 369.8% higher than random selection after 20 years while effectively identifying high-priority DSs.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562816","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 Vickrey Auction-Based Non-Cooperative Game Framework for P2P Electricity Trading in a Community","authors":"Mojtaba Zamanpour,&nbsp;Ali Karimi","doi":"10.1049/stg2.70063","DOIUrl":"10.1049/stg2.70063","url":null,"abstract":"<p>In recent years, peer-to-peer (P2P) energy trading in local electricity markets has attracted significant research attention as a cost-effective alternative to traditional grid exchanges. This paper proposes a new two-stage non-cooperative game theory framework for electricity exchange between prosumers, consumers and a local energy storage (LES) agent in a community market. The first stage determines peer participation order based on priority lists and willingness coefficients shared with the community manager. The second stage implements the P2P market using Vickrey auction principles, ensuring truthful bidding aligned with actual energy valuations. This iterative process continues until maximal satisfaction of prosumers' surplus and consumers' demand is achieved, with residual exchanges facilitated through the LES agent when necessary. The framework's effectiveness is demonstrated through two case studies: a comparison with existing methods in the IEEE 14-bus system and a larger test system with more agents. The results reveal three key benefits: higher profits for prosumers, lower electricity costs for consumers and a transparent market mechanism that naturally discourages false bidding by reducing its potential gains.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569922","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}