IET Smart Grid最新文献

{"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":"https://doi.org/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":2.7,"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":"https://doi.org/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":2.7,"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}

{"title":"Impact of Cyberphysical Attacks on Load Tap Changers and Distribution System Voltage Stability","authors":"Anas AlMajali,&nbsp;Feras Alasali,&nbsp;Naser El-Naily,&nbsp;Mohammed I. Abuashour,&nbsp;Salah Abu Ghalyon","doi":"10.1049/stg2.70076","DOIUrl":"https://doi.org/10.1049/stg2.70076","url":null,"abstract":"<p>Load tap changers (LTCs) play a critical role in voltage regulation, and their manipulation creates serious risks for distribution system stability. In this work, a digital model of a realistic power grid was utilised to investigate coordinated cyberphysical attacks, where LTC tap ratios were set to extreme values in parallel with selective customer load changes. The analysis shows that although the main bus often remained close to nominal conditions, feeder buses deviated significantly, with voltages dropping to 0.75 p.u. in undervoltage cases and rising beyond 1.07 p.u. when the LTC was forced upward. Photovoltaic (PV) integration modified these outcomes. During undervoltage scenarios, PV injection increased bus voltages by 0.02–0.03 p.u., improving stability margins. In contrast, under overvoltage conditions, PV generation exacerbated the rise, with feeders approaching 1.10 p.u. Load shedding was also examined; it reduced undervoltage deviations but further elevated voltages during overvoltage, highlighting its asymmetric effectiveness. This study contributes to understanding by demonstrating how the coordinated manipulation of LTC operation and load can drive system-wide voltage instability, even when appearing normal at central monitoring points. It also clarifies the dual role of PV under attack conditions and emphasises that conventional countermeasures are insufficient. A layered defence approach, combining secure LTC communication, feeder-level anomaly detection and adaptive voltage support, is essential for maintaining resilience in renewable-integrated distribution networks.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147569484","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":"Resilient Energy and Flexibility Scheduling in Interconnected Local Energy Networks via AI-Enabled Cyber-Integrity Enhancement","authors":"Ali Yazhari Kermani,&nbsp;Amir Abdollahi,&nbsp;Mehdi Shafiee","doi":"10.1049/stg2.70071","DOIUrl":"https://doi.org/10.1049/stg2.70071","url":null,"abstract":"<p>Interconnected local energy networks (ILENs) increasingly rely on data-driven control and flexibility trading, making their operation highly sensitive to cyber-induced data corruption and communication failures. Existing scheduling approaches typically treat cybersecurity, flexibility optimisation, and data-driven anomaly correction as independent modules, leading to suboptimal decisions when measurements are unreliable. This paper proposes a unified cyber-integrity-aware scheduling framework that integrates a Cyber-Connectivity Index (CCI) with an Extreme Gradient Boosting Ensemble Tree (XGBET) model for vulnerability detection and data recovery. The CCI quantifies communication reliability and data integrity, whereas XGBET corrects corrupted measurements and provides high-quality inputs for a bi-level, multi-objective ILEN optimisation model. The proposed framework is evaluated under healthy, compromised, and corrected data conditions using realistic ILEN configurations. Results demonstrate that incorporating cyber-integrity into the scheduling loop improves operational cost, flexibility utilisation, and resilience compared with benchmark approaches that neglect cyber-physical interactions. The findings highlight the importance of jointly modelling cyber-condition awareness and data-driven correction in future ILEN decision-making architectures.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567906","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":"Digital Transformation of Energy Systems: Technologies, Data, Governance and Cyber Security","authors":"Zoya Pourmirza,&nbsp;Mehmet Bozdal,&nbsp;Mohamad Khalil,&nbsp;Emily Judson,&nbsp;Sara Walker","doi":"10.1049/stg2.70068","DOIUrl":"https://doi.org/10.1049/stg2.70068","url":null,"abstract":"<p>Modern energy systems face increasing operational challenges due to the growing penetration of renewables, variability in generation and network congestion, which contribute to curtailment, inefficiencies and avoidable emissions. These issues constrain system flexibility and hinder progress towards net-zero targets. Digitalisation offers a means to address these challenges by improving system observability, enabling real-time coordination and supporting data-driven decision-making through technologies such as the Internet of Things, artificial intelligence and digital twin. As a result, digitalisation has enhanced the efficiency, reliability and flexibility of energy systems, supporting progress towards net-zero emissions targets. This paper reviews key technologies that enable energy system digitalisation and examines challenges arising from increased connectivity. Unlike existing studies that consider individual technologies, market mechanisms or policy frameworks in isolation, this work adopts an integrated perspective encompassing enabling technologies, data-driven applications, data governance and cyber security within digitalised energy systems. This study is guided by a horizon scanning methodology to identify emerging technological and cyber-physical challenges shaping future energy system design. Additionally, a six-dimensional framework for energy data governance is used to structure current practices and identify gaps related to data quality, discoverability, sharing, privacy and emerging responsibilities. This paper offers actionable insights for researchers, policymakers and industry stakeholders while identifying areas that require further technical and regulatory development.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320902","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":"Strategic Renewable Investment Under Cap-and-Trade: A Prospect Theory Approach","authors":"Peng Liao,&nbsp;Qisheng Huang,&nbsp;Zeyuan Suo,&nbsp;Junping Ji,&nbsp;Dapeng Liang","doi":"10.1049/stg2.70066","DOIUrl":"https://doi.org/10.1049/stg2.70066","url":null,"abstract":"<p>This study examines renewable energy investment decisions by power generation companies under cap-and-trade regulations and renewable output uncertainty. Although existing research predominantly employs expected utility theory (EUT) to model risk-neutral cost-minimisation strategies, empirical evidence highlights discrepancies between EUT assumptions and real-world decision-making. To bridge this gap, we introduce a behavioural economics framework grounded in prospect theory (PT), which explicitly incorporates risk preferences and cognitive biases into the analysis. We develop a nonconvex optimisation model to determine optimal renewable investment levels for PT-driven firms, resolving computational challenges by exploiting the unimodal structure of the objective function. Our theoretical and numerical analyses reveal three key insights: (1) Firms with higher reference points exhibit greater risk tolerance and renewable investment due to elevated outcome expectations; (2) probability distortion under PT incentivises higher renewable investments when the likelihood of high renewable output is low; (3) PT-driven firms achieve lower expected profits than EUT-modelled counterparts but secure higher guaranteed minimum profits, reflecting a preference for loss aversion over risk-neutral optimisation. These findings underscore the critical role of behavioural factors in shaping energy transition strategies under emission constraints, offering policymakers and firms actionable insights for aligning investments with risk profiles and sustainability goals.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299890","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":"Coordination Between Distribution Network and Microgrid in Confidence-Adjustable Voltage Optimisation Considering Battery Swapping Station","authors":"Wei Jiang,&nbsp;Peng Gao,&nbsp;Qinghe Sun,&nbsp;Qiong Wang,&nbsp;Yujian Ye,&nbsp;Yifan Deng,&nbsp;Zhiqi Xu","doi":"10.1049/stg2.70064","DOIUrl":"https://doi.org/10.1049/stg2.70064","url":null,"abstract":"<p>In the context of high-penetration of photovoltaic (PV) installation, the inherent fluctuation of PV generation leads voltage violations to the distribution system. The traditional voltage adjustment, such as capacitor banks and on-load tap changer, is dominated by the distribution system operator. With the emergence of microgrid, multiple flexible resources, such as electric vehicle battery swapping stations (EV-BSSs), can participate in distribution network voltage optimisation by dynamically changing the load time and amplitude characteristics. However, the uncertainty of resources, that is, the volatility of PV power and the randomness of loads, makes it difficult to precisely regulate the voltage. If the charge/discharge pattern of EV-BSS can compensate for the fluctuations, the voltage regulation would be strongly supported. This paper proposes a collaborative operation framework between distribution networks and microgrid to coordinate voltage regulation in the distribution network, integrating conventional voltage control methods with the EV-BSS. A PV inverter control strategy is also developed, incorporating both user satisfaction and the factors influencing PV generations. To further enhance system performance, a multiobjective optimisation model complemented by a confidence-adjustable mechanism is developed to dynamically fine-tune scheduling robustness under uncertainty. The effectiveness of the model is validated through case studies.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320773","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":"Co-Simulation Microgrid With Distributed Control Based on a Multi-Agent System and Communication With Adaptive Update Rate","authors":"Daniel Leocádio Fernandes,&nbsp;Guilherme Márcio Soares,&nbsp;Janaína Gonçalves de Oliveira","doi":"10.1049/stg2.70054","DOIUrl":"https://doi.org/10.1049/stg2.70054","url":null,"abstract":"<p>This study presents a distributed control system for a multiagent co-simulation environment, designed to regulate a direct current (DC) bus voltage in a grid-connected microgrid (MG). The system adopts a client/server architecture, enabling seamless communication in a network of interconnected components integrated into a MG while implementing adaptive update-rate communication to optimise data exchange efficiency. A multiagent system (MAS) orchestrates interactions between power converters, ensuring seamless operation of a DC microgrid powered by photovoltaic (PV) arrays, a battery storage system and an inverter/rectifier converter connected to the main grid. The framework integrates Python, TCP/IP sockets and industry-standard simulators (PLECS, PSIM and RTDS) to create a co-simulation environment. Key results demonstrate effective DC bus voltage regulation and battery voltage control (used as a proxy for SoC), ensuring system stability under varying operating conditions. The proposed approach enhances system responsiveness through adaptive update rate communication, which dynamically adjusts data transmission among agents and the MAS based on real-time network conditions. This improvement is evidenced by a reduction of approximately <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>13</mn>\u0000 <mi>%</mi>\u0000 </mrow>\u0000 <annotation> $13%$</annotation>\u0000 </semantics></math> in the average settling time of the secondary control layer, from <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>267</mn>\u0000 <mspace></mspace>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation> $267hspace*{.5em}mathrm{s}$</annotation>\u0000 </semantics></math> to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>232.4</mn>\u0000 <mspace></mspace>\u0000 <mi>s</mi>\u0000 </mrow>\u0000 <annotation> $232.4hspace*{.5em}mathrm{s}$</annotation>\u0000 </semantics></math>, when the communication medium delay varies during system operation under the adaptive update rate, compared with the fixed update rate scenario. These results highlight the superior dynamic performance of the hierarchical control strategy at the supervisory level.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315488","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 Optimal Control Approach for Plug-In Electric Vehicles in Active Distribution Systems Using Deep Reinforcement Learning","authors":"Yameena Tahir,&nbsp;Muhammad Faisal Nadeem,&nbsp;Muhammad Bilal Raza,&nbsp;Muhammad Akmal","doi":"10.1049/stg2.70051","DOIUrl":"https://doi.org/10.1049/stg2.70051","url":null,"abstract":"<p>The penetration of plug-in electric vehicles (PEVs) and distributed energy resources (DERs) is increasing in distribution systems, potentially leading to significant technical and economic challenges. To tackle these challenges, this paper introduces a novel framework for effectively managing DERs and EVs within active distribution systems (ADSs), incorporating time-varying ZIP load models. A deep reinforcement learning (DRL)-based control approach is developed that simultaneously optimises both technical and economic objective functions for the efficient operation of ADSs. For this purpose, the PEVs are integrated with different nodes of the ADS through solid-state transformers (SSTs). Based on available generation, load demand and EV charging profiles, the control algorithm regulates reactive power flow using SSTs and minimises the operational cost as well as power loss of the ADS. The proposed framework is successfully applied and evaluated on standard IEEE systems, demonstrating its efficacy in solving the problem of integrating PEVs and DERs using solid-state transformers.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147280114","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":"Electric Vehicles Scheduling Method for Distribution Networks Based on Distributed Proximal Policy Optimisation Considering Weather Uncertainty","authors":"Long Wang,&nbsp;Heyang Yu,&nbsp;Yi Wang,&nbsp;Xin Jin,&nbsp;Tingzhe Pan,&nbsp;Wangzhang Cao","doi":"10.1049/stg2.70061","DOIUrl":"https://doi.org/10.1049/stg2.70061","url":null,"abstract":"<p>Large-scale uncoordinated charging and discharging behaviours of electric vehicles (EVs) exacerbate power grid operational losses and reduce the efficiency of scheduling model solutions. To address this issue, this paper proposes an EVs scheduling method for distribution networks based on DPPO. Firstly, considering the operational conditions and security constraints of the distribution network, an optimisation scheduling model is constructed to minimise the total system operational cost. The objective function includes key parameters such as EV charging/discharging power, branch power losses and curtailment rates of wind and photovoltaic (PV) generation. Secondly, to address the computational challenges posed by the high dimensionality of variables, nonlinear constraints, and discrete variables, the optimisation scheduling model is transformed, and the DPPO algorithm is employed for efficient solution. Finally, simulation results based on the IEEE 33-node distribution network system demonstrate that the proposed method effectively reduces the total system operational cost and network losses, and significantly enhances the distribution network's capacity to absorb distributed wind and PV generation.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146256462","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}