IET Smart Grid最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076161","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":"Research on Distributed PV Power Identification Method Based on CNN and Multi-Scale Fluctuation Feature Extraction","authors":"Zhiren Liu,&nbsp;Xin Chen,&nbsp;Jiarong Yu,&nbsp;Wenzhou Lu","doi":"10.1049/stg2.70089","DOIUrl":"https://doi.org/10.1049/stg2.70089","url":null,"abstract":"<p>Accurate identification of distributed photovoltaic (PV) generation at the distribution transformer area level is essential for improving grid operation and renewable energy management. However, in practical scenarios, individual behind-the-metre PV systems are typically not equipped with dedicated monitoring devices, and only aggregated transformer-side total power data are available. To address this challenge, this paper develops a distributed PV power identification method based on a multiscale convolutional neural network (CNN). Solar irradiance estimation is incorporated to maintain physical consistency with radiation variation, whereas the multiscale CNN structure captures temporal variation characteristics of PV generation. By establishing a nonlinear mapping relationship between reference PV station generation and aggregated transformer-side total power data, the proposed method enables separation of PV generation from load demand without requiring individual PV measurements. Case studies demonstrate that the proposed method achieves superior identification accuracy compared with benchmark models. Ablation and sensitivity analysis further validate the robustness and practical applicability of the framework under limited sensing conditions.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148078290","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":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076221","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":"Dual-Objective Adversarial Attack on Wind Power Forecasting: Balancing Destructiveness and Stealthiness","authors":"Chenghao Liang,&nbsp;Gaoqi Liang,&nbsp;Xingyu Zhu,&nbsp;Ting Wu,&nbsp;Jiaqi Ruan,&nbsp;Guolong Liu","doi":"10.1049/stg2.70087","DOIUrl":"https://doi.org/10.1049/stg2.70087","url":null,"abstract":"<p>As inputs to wind power forecasting models, meteorological factors are vulnerable to adversarial attacks, leading to deviations in forecasted wind power. Existing research on adversarial attacks against renewable energy forecasting models mostly focuses on a single destructive objective, neglecting attack stealthiness. This makes it challenging to achieve actual destructive effects in scenarios where example detection mechanisms are deployed. Therefore, this paper proposes a Dual-Objective Adversarial Attack (DOAA) algorithm that balances attack destructiveness and stealthiness to achieve the joint optimisation of both. First, a wind power forecasting model based on deep neural network is constructed, which accurately captures the complex coupling relationships between meteorological factors and wind power. Second, a graph autoencoder (GAE) model suitable for time-series example detection is designed, which fuses the temporal correlations of data from adjacent time periods using graph structures and realises the detection of adversarial examples through reconstruction loss. Finally, the above models are integrated to construct the DOAA algorithm based on Projected Gradient Descent. By adjusting the coefficient of the reconstruction loss term, the preference for attack destructiveness or stealthiness can be achieved. Experimental results verify that the proposed DOAA algorithm achieves a favourable balance between attack destructiveness and stealthiness.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148077088","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":"Possibilistic–Probabilistic Consumer Participation Modelling and Cybersecure Demand Response Enabled by Convolutional–Bidirectional Long Short-Term Memory Forecasting","authors":"Faezeh Gholipour Zarandi,&nbsp;Fahimeh Ghasemian,&nbsp;Amir Abdollahi","doi":"10.1049/stg2.70084","DOIUrl":"https://doi.org/10.1049/stg2.70084","url":null,"abstract":"<p>Demand response programmes (DRPs) increasingly rely on accurate load forecasting, realistic modelling of consumer participation, and strong resilience against data manipulation. This paper presents an integrated cyber-secure DRP design framework that jointly addresses these requirements. A hybrid convolutional–bidirectional long short-term memory (CB-LSTM) model is first employed to predict consumption behaviour with improved accuracy, supporting consumer categorisation into low-, medium-, and high-usage groups. Building on these predictions, a comprehensive DRP portfolio incorporating TOU and DLC-based schemes is dynamically assigned to each category. To capture behavioural uncertainty, consumer participation is modelled using a Z-number possibilistic–probabilistic formulation, enabling more reliable estimation of engagement levels and enhancing the fairness of DRP allocation. A key contribution of this study is the introduction of a CB-LSTM-assisted deviation bound-based detection/correction (CBLADBDC) mechanism to mitigate false data injection (FDI) attacks. The proposed method exhibits strong detection capability, achieving a 98.0% recall and maintaining a low false positive rate of 1.1%, thereby preserving the integrity of load profiles and preventing unnecessary incentive payments. Simulation results demonstrate that the integrated framework reduces peak demand by 24%–27% while maintaining cost-efficient DRP performance.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281982","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":"Development of Model Predictive Control of Hybrid Solar PV-Wind for Economic Operation of Electrolyser System","authors":"Samson W. Mlay,&nbsp;Jackson J. Justo,&nbsp;Ibrahim Mwammenywa,&nbsp;Sarah P. Ayeng'o,&nbsp;Bekkering J. Jan","doi":"10.1049/stg2.70086","DOIUrl":"https://doi.org/10.1049/stg2.70086","url":null,"abstract":"<p>The increasing penetration of renewable energy (RE) and hydrogen technologies is reshaping the operational requirements of modern decarbonised energy systems, necessitating advanced control strategies to coordinate renewable generation, hydrogen storage, and heterogeneous end-use demands. This paper presents a mixed-integer Model Predictive Control (MPC) framework for a grid-connected hybrid microgrid integrating solar photovoltaics, wind turbines, and a proton exchange membrane electrolyser. The system simultaneously satisfies two distinct hydrogen demand profiles: continuous residential heating and discrete mobility demand via tube-trailer refuelling, capturing temporal dynamics rarely addressed in prior studies. To manage discrete mobility demand beyond the prediction horizon, a critical-time mechanism was introduced to ensures timely tube-trailer dispatch while preventing premature grid electricity imports. In contrast to existing works that primarily adopt fixed horizons, this study systematically compares fixed and shrinking prediction horizons with 12-h and 24-h lengths, evaluating their impact on operational performance and mixed-integer linear programming computational complexity. Simulation results show RE utilisation above 96% for heating and above 90% for mobility. Hydrogen production and grid electricity consumption vary by less than 0.1% across horizon configurations. Shrinking horizons cut computation time by up to 70%, demonstrating the MPC approach as a scalable, computationally efficient solution for integrating dual hydrogen demands.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282087","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":"Deep Reinforcement Learning-Based Load-Side Resonance Forcing via Hyper-Scale Data Center Load Modulation","authors":"Mohamed Shamseldein","doi":"10.1049/stg2.70083","DOIUrl":"https://doi.org/10.1049/stg2.70083","url":null,"abstract":"<p>Hyper-scale data centers, with rapid load modulation capabilities, introduce a novel cyber-physical attack vector for power systems. This paper investigates coordinated resonance attacks wherein adversaries exploit data center load flexibility to excite inter-area oscillation modes. We formulate the attack as a Markov Decision Process and develop a Deep Reinforcement Learning framework using Proximal Policy Optimization that enables model-free adversaries to discover mode-matched forcing patterns through local frequency observations alone. We extend the analysis to multi-agent scenarios using Centralised-Training-Decentralised-Execution, demonstrating that geographically distributed data centers can learn anti-phase coordination without real-time communication. Simulations on a reduced-order dynamic equivalent (tuned to the IEEE 39-bus 0.64 Hz mode) show that: (1) a single agent learns mode-targeted forcing using 200 MW of controllable load; and (2) two coordinated agents achieve comparable amplification with only 100 MW each. Sensitivity analysis reveals attack severity scales linearly with load magnitude and inversely with system inertia, and that the learned forcing pattern degrades gracefully under mode frequency mismatch (&gt; 50% effectiveness at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation> $pm $</annotation>\u0000 </semantics></math>6% mismatch, still ∼3 × random at <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>±</mo>\u0000 </mrow>\u0000 <annotation> $pm $</annotation>\u0000 </semantics></math>25%). High-fidelity validation using ANDES on IEEE 39-bus and WECC 179-bus benchmarks confirms that narrowband forcing produces consistently larger frequency deviations than random modulation—specifically, 1.20 × and 2.20 × the random baseline on the respective benchmarks—while remaining bounded (tens of mHz) under standard control loops. The active-power focus of the attack is justified by the predominantly real-power nature of data center loads and the electromechanical <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mi>P</mi>\u0000 <mo>−</mo>\u0000 <mi>δ</mi>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation> $(P-delta )$</annotation>\u0000 </semantics></math> coupling governing inter-area modes; secondary reactive power effects are analysed and shown to be an order of magnitude smaller. These findings highlight an emerging security concern as data center penetration increases and grid inertia declines.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099007","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":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148098983","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063219","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":"Quantitative Analysis Method of Stability Ability for Renewable Electric Energy Delivery System","authors":"Jiawang Xiong,&nbsp;Wei Pei,&nbsp;Guanqi Wang,&nbsp;Wei Deng,&nbsp;Hao Xiao","doi":"10.1049/stg2.70077","DOIUrl":"10.1049/stg2.70077","url":null,"abstract":"<p>One mainstream approach to alleviate insufficient voltage support in renewable-energy power delivery systems is to introduce a portion of grid-forming (GFM) inverters. However, the distinct characteristics of grid-following (GFL) and grid-forming (GFM) controls complicate the quantitative assessment of system stability capability. To address this issue, this paper proposes an amplitude mapping-based method for quantitatively evaluating stability capability, yielding a simple and practical analytical expression. Specifically, we introduce the idea of assessing system stability through waveform-amplitude stability. Based on this concept, the mathematical model of the renewable-energy delivery system is established, and the expression of the stability capability index (SA) is derived. Next, the impacts of key parameters on SA are analysed, and the critical GFL–GFM power ratio is quantified. Finally, the theoretical findings are validated through case studies.</p>","PeriodicalId":36490,"journal":{"name":"IET Smart Grid","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/stg2.70077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696286","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}