IEEE Transactions on Sustainable Energy最新文献

IEEE Transactions on Sustainable Energy Information for Authors IEEE可持续能源信息汇刊

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IEEE Transactions on Sustainable Energy Pub Date : 2025-12-23 DOI: 10.1109/TSTE.2025.3640786

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IEEE Transactions on Sustainable Energy Pub Date : 2025-12-23 DOI: 10.1109/TSTE.2025.3640784

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IEEE Industry Applications Society Information IEEE工业应用学会信息

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IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3606325

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2025 Index IEEE Transactions on Sustainable Energy 2025年IEEE可持续能源学报

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IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3611459

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IEEE Transactions on Sustainable Energy Information for Authors IEEE可持续能源信息汇刊

IF 1 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-09-29 DOI: 10.1109/TSTE.2025.3606327

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Power Capacity Allocation Among Multiple Renewable Power Plants: A Perspective From System Strength 基于系统强度视角的多可再生电厂发电容量分配

IF 1 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-08-11 DOI: 10.1109/TSTE.2025.3596773

Yun Liu;Hanlu Yang;Huanhai Xin;Alberto Borghetti;Jizhong Zhu

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County-Level Distributed PV Day-Ahead Power Prediction Based on Grey Correlation Analysis and Transformer-GCAN Model 基于灰色关联分析和变压器- gcan模型的县级分布式光伏日前功率预测

IF 1 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-07-01 DOI: 10.1109/TSTE.2025.3584976

Pei Zhang;Bin Zhang;Jinliang Yin;Jie Shi

{"title":"County-Level Distributed PV Day-Ahead Power Prediction Based on Grey Correlation Analysis and Transformer-GCAN Model","authors":"Pei Zhang;Bin Zhang;Jinliang Yin;Jie Shi","doi":"10.1109/TSTE.2025.3584976","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3584976","url":null,"abstract":"The distributed photovoltaic (PV) power stations within the entire county exist spatiotemporal correlation. Merely considering temporal correlation makes it challenging to meet the day-ahead scheduling demands. This paper proposes a distributed PV county-level day-ahead power prediction method based on grey relational analysis and the Transformer-Graph Convolutional Attention Network (Transformer-GCAN) model. Firstly, the grey relational degree is used to measure the relevance between each distributed PV stations, and the connection relationship of the station graph is determined based on the analysis results. Secondly, the Transformer network is utilized to extract the temporal features of each PV sequence in the graph. Based on the Graph Convolutional Network (GCN) model, a Graph Attention Mechanism (GAT) is introduced to dynamically extract spatial features between each photovoltaic station in the graph. Finally, the integration of spatiotemporal features is achieved through a fully connected neural network, enabling day-ahead power prediction at the county level. Case analysis results demonstrate that compared with the Transformer-GCN model, the Root Mean Square Error (RMSE) of the power prediction model proposed in this paper is reduced by 11.90%, 15.72% and 19.61% respectively in sunny days, cloudy days and rainy days.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"709-717"},"PeriodicalIF":10.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Synthetic Inertia Control for a Wind Turbine Generator Based on Event Size and Rotor Speed 基于事件大小和转子转速的风力发电机组综合惯性控制

IF 1 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-06-23 DOI: 10.1109/TSTE.2025.3581977

Jongwon Kang;Yong Cheol Kang;Kyu-Ho Kim;Kicheol Kang;Youngsun Lee;Kyeon Hur;Dong-Ho Cho

{"title":"Synthetic Inertia Control for a Wind Turbine Generator Based on Event Size and Rotor Speed","authors":"Jongwon Kang;Yong Cheol Kang;Kyu-Ho Kim;Kicheol Kang;Youngsun Lee;Kyeon Hur;Dong-Ho Cho","doi":"10.1109/TSTE.2025.3581977","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3581977","url":null,"abstract":"To enhance the frequency nadir without causing the excessive deceleration of the rotor speed (<inline-formula><tex-math>${{{{omega }}}_{{r}}}$</tex-math></inline-formula>), synthetic inertia control (SIC) schemes of a wind turbine generator (WTG) need to provide incremental power in response to event magnitude and <inline-formula><tex-math>${{{{omega }}}_{{r}}}$</tex-math></inline-formula>. Conventional stepwise SIC approaches face limitations during large events due to the increase of predefined incremental power, which does not match the actual event size. This paper presents an SIC strategy for WTGs that adjusts incremental power in relation to event size and <inline-formula><tex-math>${{{{omega }}}_{{r}}}$</tex-math></inline-formula>. During the frequency-support phase, the incremental power is modulated based on the frequency deviation, along with a control gain proportional to <inline-formula><tex-math>${{{{omega }}}_{{r}}}$</tex-math></inline-formula>, rather than the rate of change of frequency. While this approach accounts for the power imbalance, it remains vulnerable to noise and delays in practical applications. After the frequency-support phase, the proposed method decreases the active power reference in accordance with <inline-formula><tex-math>${{{{omega }}}_{{r}}}$</tex-math></inline-formula>, ensuring it stabilizes within a secure operating range. Following stabilization, the WTG transitions smoothly back to maximum power point tracking operation. Simulation results indicate that the proposed approach significantly enhances the frequency nadir during large events, even under low wind conditions, while avoiding excessive deceleration of the rotor speed.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"684-696"},"PeriodicalIF":10.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576563

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IEEE Transactions on Sustainable Energy Publication Information IEEE可持续能源学报出版信息

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2025-06-20 DOI: 10.1109/TSTE.2025.3576553

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