Pub Date : 2025-12-23DOI: 10.1109/TSTE.2025.3640786
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2025.3640786","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3640786","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"C4-C4"},"PeriodicalIF":10.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1109/TSTE.2025.3640784
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TSTE.2025.3640784","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3640784","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"C3-C3"},"PeriodicalIF":10.0,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11313739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-29DOI: 10.1109/TSTE.2025.3606325
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TSTE.2025.3606325","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3606325","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"C3-C3"},"PeriodicalIF":10.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-29DOI: 10.1109/TSTE.2025.3611459
{"title":"2025 Index IEEE Transactions on Sustainable Energy","authors":"","doi":"10.1109/TSTE.2025.3611459","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3611459","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"3128-3186"},"PeriodicalIF":10.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-29DOI: 10.1109/TSTE.2025.3606327
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2025.3606327","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3606327","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 4","pages":"C4-C4"},"PeriodicalIF":10.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145183960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Large-scale renewable energy projects consisting of renewable power plants (RPPs) managed by different stakeholders may suffer weak system strength. Consequently, only a limited amount of renewable power capacity can be delivered, or the insufficient system strength could incur stability issues. The allocation of system strength constrained power capacity among different RPPs deserves investigation, which is critical to their respective benefits. To this end, the system strength demand of each RPP is quantified based on its allowable power capacity. A power capacity allocation approach is proposed, ensuring that the RPPs efficiently and fairly utilize system strength. Case studies show that RPPs with longer electrical distances deliver less renewable capacity due to their greater impact on system strength.
{"title":"Power Capacity Allocation Among Multiple Renewable Power Plants: A Perspective From System Strength","authors":"Yun Liu;Hanlu Yang;Huanhai Xin;Alberto Borghetti;Jizhong Zhu","doi":"10.1109/TSTE.2025.3596773","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3596773","url":null,"abstract":"Large-scale renewable energy projects consisting of renewable power plants (RPPs) managed by different stakeholders may suffer weak system strength. Consequently, only a limited amount of renewable power capacity can be delivered, or the insufficient system strength could incur stability issues. The allocation of system strength constrained power capacity among different RPPs deserves investigation, which is critical to their respective benefits. To this end, the system strength demand of each RPP is quantified based on its allowable power capacity. A power capacity allocation approach is proposed, ensuring that the RPPs efficiently and fairly utilize system strength. Case studies show that RPPs with longer electrical distances deliver less renewable capacity due to their greater impact on system strength.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"17 1","pages":"718-721"},"PeriodicalIF":10.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808619","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}
Pub Date : 2025-07-01DOI: 10.1109/TSTE.2025.3584976
Pei Zhang;Bin Zhang;Jinliang Yin;Jie Shi
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.
{"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}
Pub Date : 2025-06-23DOI: 10.1109/TSTE.2025.3581977
Jongwon Kang;Yong Cheol Kang;Kyu-Ho Kim;Kicheol Kang;Youngsun Lee;Kyeon Hur;Dong-Ho Cho
To enhance the frequency nadir without causing the excessive deceleration of the rotor speed (${{{{omega }}}_{{r}}}$), synthetic inertia control (SIC) schemes of a wind turbine generator (WTG) need to provide incremental power in response to event magnitude and ${{{{omega }}}_{{r}}}$. 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 ${{{{omega }}}_{{r}}}$. During the frequency-support phase, the incremental power is modulated based on the frequency deviation, along with a control gain proportional to ${{{{omega }}}_{{r}}}$, 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 ${{{{omega }}}_{{r}}}$, 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.
{"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}
Pub Date : 2025-06-20DOI: 10.1109/TSTE.2025.3576553
{"title":"IEEE Transactions on Sustainable Energy Publication Information","authors":"","doi":"10.1109/TSTE.2025.3576553","DOIUrl":"https://doi.org/10.1109/TSTE.2025.3576553","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 3","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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