Pub Date : 2024-12-17DOI: 10.1109/TSTE.2024.3508513
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TSTE.2024.3508513","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3508513","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"730-730"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805486","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844327","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3506259
{"title":"IEEE Transactions on Sustainable Energy Information for Authors","authors":"","doi":"10.1109/TSTE.2024.3506259","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3506259","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"C4-C4"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844494","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3508517
{"title":"Get Published in the New IEEE Open Access Journal of Power and Energy","authors":"","doi":"10.1109/TSTE.2024.3508517","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3508517","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"732-732"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805554","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844326","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3506255
{"title":"IEEE Transactions on Sustainable Energy Publication Information","authors":"","doi":"10.1109/TSTE.2024.3506255","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3506255","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"C2-C2"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805484","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859180","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 : 2024-12-17DOI: 10.1109/TSTE.2024.3506257
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TSTE.2024.3506257","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3506257","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"C3-C3"},"PeriodicalIF":8.6,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805485","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844505","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 : 2024-10-16DOI: 10.1109/TSTE.2024.3481417
Zhi Wang;Li Guo;Xialin Li;Xu Zhou;Jiebei Zhu;Chengshan Wang
During grid faults, the grid-connected paralleled converter systems is susceptible to a phase-locked loop (PLL) synchronization transient instability. Most existing studies focus on first-swing transient stability analysis using the equal-area criterion. However, achieving first-swing transient stability does not guarantee overall stability, as the system may still experience multi-swing transient instability. This paper analyzes the type of multi-swing transient instability issue from two aspects: transient instability mechanism and transient stability assessment. Firstly, the mechanism of multi-swing transient instability is revealed from the transient energy conversion point of view. Then, considering transient interactions between converters, the largest estimated domain of attraction (LEDA) is constructed utilizing the Takagi-Sugeno method. Using the LEDA, the multi-swing transient instability problem of the grid-connected paralleled converter systems is quantitatively analyzed. Finally, the theoretical results are verified based on the RT-LAB hardware-in-the-loop experimental platform.
{"title":"Multi-Swing PLL Synchronization Transient Stability of Grid-Connected Paralleled Converters","authors":"Zhi Wang;Li Guo;Xialin Li;Xu Zhou;Jiebei Zhu;Chengshan Wang","doi":"10.1109/TSTE.2024.3481417","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3481417","url":null,"abstract":"During grid faults, the grid-connected paralleled converter systems is susceptible to a phase-locked loop (PLL) synchronization transient instability. Most existing studies focus on first-swing transient stability analysis using the equal-area criterion. However, achieving first-swing transient stability does not guarantee overall stability, as the system may still experience multi-swing transient instability. This paper analyzes the type of multi-swing transient instability issue from two aspects: transient instability mechanism and transient stability assessment. Firstly, the mechanism of multi-swing transient instability is revealed from the transient energy conversion point of view. Then, considering transient interactions between converters, the largest estimated domain of attraction (LEDA) is constructed utilizing the Takagi-Sugeno method. Using the LEDA, the multi-swing transient instability problem of the grid-connected paralleled converter systems is quantitatively analyzed. Finally, the theoretical results are verified based on the RT-LAB hardware-in-the-loop experimental platform.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"716-729"},"PeriodicalIF":8.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844302","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 : 2024-10-14DOI: 10.1109/TSTE.2024.3480456
Zhongda Chu;Fei Teng
Security and stability challenges in future power systems with high penetration Inverter-Based Resources (IBR) have been anticipated as one of the main barriers to decarbonization. Grid-following IBRs may become unstable under small disturbances in weak grids, while during transient processes, system stability and protection may be jeopardized due to the lack of sufficient Short-Circuit Current (SCC). To solve these challenges and achieve decarbonization, the future system has to be carefully planned. However, it remains unclear how both small-signal and transient stabilities can be considered during the system planning stage. In this context, this paper proposes a coordinated planning model of different resources in the transmission system, namely the synchronous condensers and GFM IBRs to enhance system stability. The system strength and SCC constraints are analytically derived by considering the different characteristics of synchronous units and IBRs, which are further effectively linearized through a novel data-driven approach, where an active sampling method is proposed to generate a representative data set. The significant economic value of the proposed coordinated planning framework in both system asset investment and system operation is demonstrated through detailed case studies.
{"title":"Coordinated Planning for Stability Enhancement in High IBR-Penetrated Systems","authors":"Zhongda Chu;Fei Teng","doi":"10.1109/TSTE.2024.3480456","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3480456","url":null,"abstract":"Security and stability challenges in future power systems with high penetration Inverter-Based Resources (IBR) have been anticipated as one of the main barriers to decarbonization. Grid-following IBRs may become unstable under small disturbances in weak grids, while during transient processes, system stability and protection may be jeopardized due to the lack of sufficient Short-Circuit Current (SCC). To solve these challenges and achieve decarbonization, the future system has to be carefully planned. However, it remains unclear how both small-signal and transient stabilities can be considered during the system planning stage. In this context, this paper proposes a coordinated planning model of different resources in the transmission system, namely the synchronous condensers and GFM IBRs to enhance system stability. The system strength and SCC constraints are analytically derived by considering the different characteristics of synchronous units and IBRs, which are further effectively linearized through a novel data-driven approach, where an active sampling method is proposed to generate a representative data set. The significant economic value of the proposed coordinated planning framework in both system asset investment and system operation is demonstrated through detailed case studies.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"700-715"},"PeriodicalIF":8.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844562","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}
The proliferation of flexible loads has empowered residential users in contributing both upward and downward frequency regulation reserves to the power grid. Due to barriers like minimum bid size, residential users with relatively small power consumption must be aggregated before they can enter the reserve market. However, the large number of residential users renders the aggregation mechanism design problem a challenging task. In particular, the cost for reserve provision of each user is not only highly heterogeneous, but also coupled through both the temporal dimension and the up/down regulation direction. In order to allow the users to fully express their costs, in this paper, we propose a combinatorial reverse auction (CRA) framework as the market mechanism for user aggregation. In this auction, the aggregator is the auctioneer and procures reserves from residential users. The users submit package bids consisting of combinations of both upward and downward reserves over multiple time-slots, capturing the coupling in the user cost. Furthermore, to address the infamous computational challenges of large-scale combinatorial auctions, we develop a novel fast combinatorial auction (FCA) mechanism that can be solved in polynomial time. It includes an approximate winner determination algorithm and a critical payment scheme. Notably, our proposed mechanism is rigorously proved to possess desirable economic properties such as truthfulness and individual rationality. Extensive simulations have validated the theoretic properties of the proposed CRA mechanism and its advantages over existing methods. In particular, compared with the widely employed truthful Vickery-Clarke-Groves (VCG) mechanism, CRA can be $10^{4}$ times faster than VCG when the user number is above 1000. Meanwhile, it is able to achieve near-optimal social cost, where the average optimality loss is 2.92%.
{"title":"Aggregating Large-Scale Residential Users for Regulation Reserve Provision: Truthful Combinatorial Auction Based Approach","authors":"Shibo Chen;Wenjie Liu;Zhenwei Guo;Suhan Zhang;Zaiyue Yang;Chi Yung Chung","doi":"10.1109/TSTE.2024.3479451","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3479451","url":null,"abstract":"The proliferation of flexible loads has empowered residential users in contributing both upward and downward frequency regulation reserves to the power grid. Due to barriers like minimum bid size, residential users with relatively small power consumption must be aggregated before they can enter the reserve market. However, the large number of residential users renders the aggregation mechanism design problem a challenging task. In particular, the cost for reserve provision of each user is not only highly heterogeneous, but also coupled through both the temporal dimension and the up/down regulation direction. In order to allow the users to fully express their costs, in this paper, we propose a combinatorial reverse auction (CRA) framework as the market mechanism for user aggregation. In this auction, the aggregator is the auctioneer and procures reserves from residential users. The users submit package bids consisting of combinations of both upward and downward reserves over multiple time-slots, capturing the coupling in the user cost. Furthermore, to address the infamous computational challenges of large-scale combinatorial auctions, we develop a novel fast combinatorial auction (FCA) mechanism that can be solved in polynomial time. It includes an approximate winner determination algorithm and a critical payment scheme. Notably, our proposed mechanism is rigorously proved to possess desirable economic properties such as truthfulness and individual rationality. Extensive simulations have validated the theoretic properties of the proposed CRA mechanism and its advantages over existing methods. In particular, compared with the widely employed truthful Vickery-Clarke-Groves (VCG) mechanism, CRA can be \u0000<inline-formula><tex-math>$10^{4}$</tex-math></inline-formula>\u0000 times faster than VCG when the user number is above 1000. Meanwhile, it is able to achieve near-optimal social cost, where the average optimality loss is 2.92%.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"686-699"},"PeriodicalIF":8.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844504","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 : 2024-10-11DOI: 10.1109/TSTE.2024.3478760
Kang Wang;Jianzhou Wang;Zhiwu Li;Yilin Zhou
Large-scale new energy grid-connected poses significant challenges to the safe and efficient operation of smart grids. Renewable energy probabilistic forecasting (REPF) technology can analyze uncertainties in power generation, quantitatively balance risks, and prevent the breakdown of the grid. However, current REPF methods reliant on spatio-temporal maps fail to accurately estimate the probability density function (PDF) of renewable energy, resulting lacking comprehensive uncertainty analysis for distributed power generation systems (DPGS). To fill this gap, in this study, an integrated sparse gated graph density network (ISGGDN) that incorporates transfer learning to tackle the REPF challenge. A sparse gated graph dynamic convolutional network based on cross attention and residual connection is developed, which can effectively extract spatial features and spatio-temporal interactions between sites and improve the accuracy of probabilistic prediction. Furthermore, to effectively identify the types of features lost during the transfer process and to enhance the transfer learning (TL) capability, we developed an integrated approach involving multiple fine-tuning strategies based on TL. We evaluated the proposed model using wind and photovoltaic (PV) power generation data from two neighboring multi-sites, and the experimental results demonstrate that ISGGDN outperforms other existing solutions in terms of accuracy and effectiveness in REPF.
{"title":"An Integrated Sparse Gated Graph Density Network Based on Transfer Learning for Multi-Site Probabilistic Forecasting of Renewable Energy","authors":"Kang Wang;Jianzhou Wang;Zhiwu Li;Yilin Zhou","doi":"10.1109/TSTE.2024.3478760","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3478760","url":null,"abstract":"Large-scale new energy grid-connected poses significant challenges to the safe and efficient operation of smart grids. Renewable energy probabilistic forecasting (REPF) technology can analyze uncertainties in power generation, quantitatively balance risks, and prevent the breakdown of the grid. However, current REPF methods reliant on spatio-temporal maps fail to accurately estimate the probability density function (PDF) of renewable energy, resulting lacking comprehensive uncertainty analysis for distributed power generation systems (DPGS). To fill this gap, in this study, an integrated sparse gated graph density network (ISGGDN) that incorporates transfer learning to tackle the REPF challenge. A sparse gated graph dynamic convolutional network based on cross attention and residual connection is developed, which can effectively extract spatial features and spatio-temporal interactions between sites and improve the accuracy of probabilistic prediction. Furthermore, to effectively identify the types of features lost during the transfer process and to enhance the transfer learning (TL) capability, we developed an integrated approach involving multiple fine-tuning strategies based on TL. We evaluated the proposed model using wind and photovoltaic (PV) power generation data from two neighboring multi-sites, and the experimental results demonstrate that ISGGDN outperforms other existing solutions in terms of accuracy and effectiveness in REPF.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 1","pages":"673-685"},"PeriodicalIF":8.6,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844303","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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