Pub Date : 2025-11-26DOI: 10.1109/tpwrs.2025.3637236
Lingyun Gao, Lei Chen, Xiaorong Xie, Vladimir Terzija, Zhicong Chen
{"title":"A Data-Driven Method for Fast and Accurate Identification of the Wideband Oscillations in Renewable Power Systems","authors":"Lingyun Gao, Lei Chen, Xiaorong Xie, Vladimir Terzija, Zhicong Chen","doi":"10.1109/tpwrs.2025.3637236","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3637236","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"255 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609311","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 : 2025-11-26DOI: 10.1109/tpwrs.2025.3637849
Gang Zhang, Jianqiang Yu, Feng Zhang, Hongda Liu, Lei Ding, Xin Zhang, He Yin
{"title":"Distributed Load Restoration for Integrated Transmission and Distribution Systems with a Robust Model Projection Method","authors":"Gang Zhang, Jianqiang Yu, Feng Zhang, Hongda Liu, Lei Ding, Xin Zhang, He Yin","doi":"10.1109/tpwrs.2025.3637849","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3637849","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"22 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609310","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 : 2025-11-21DOI: 10.1109/tpwrs.2025.3635527
Ke Zhang, Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhaohao Ding
{"title":"Adaptive Polyhedral Approximation for Enhancing the Distributionally Robust Optimization of Uncertain Power Systems","authors":"Ke Zhang, Xu Wang, Mohammad Shahidehpour, Chuanwen Jiang, Zhaohao Ding","doi":"10.1109/tpwrs.2025.3635527","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3635527","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"13 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567885","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 : 2025-11-17DOI: 10.1109/tpwrs.2025.3630499
Sophie Hall, Francesco Micheli, Giuseppe Belgioioso, Ana Radovanović, Florian Dörfler
{"title":"Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees","authors":"Sophie Hall, Francesco Micheli, Giuseppe Belgioioso, Ana Radovanović, Florian Dörfler","doi":"10.1109/tpwrs.2025.3630499","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3630499","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"178 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535874","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}
This letter proposes a novel power flow (PF) linearization method for accurately enforcing network constraints in optimal dispatch problems. Unlike conventional linearization methods that focus on reducing PF solution errors, the proposed method aims to enhance the decision feasibility of network-constrained dispatch problems modeled with linear PF equations. A data-driven framework based on hybrid regression and classification is developed to determine coefficients of the linear PF equation. This problem is equivalent to minimizing a weighted sum of the root-mean-square error and hinge loss, which compels the linear PF model to enforce network constraints accurately. Simulations with various system scales verify that the proposed PF linearization method outperforms existing ones in terms of decision feasibility and optimality.
{"title":"Data-Driven Power Flow Linearization via Hybrid Regression and Classification for Accurately Enforcing Network Constraints","authors":"Zhenfei Tan;Xiaoyuan Xu;Han Wang;Zheng Yan;Mohammad Shahidehpour","doi":"10.1109/TPWRS.2025.3633105","DOIUrl":"10.1109/TPWRS.2025.3633105","url":null,"abstract":"This letter proposes a novel power flow (PF) linearization method for accurately enforcing network constraints in optimal dispatch problems. Unlike conventional linearization methods that focus on reducing PF solution errors, the proposed method aims to enhance the decision feasibility of network-constrained dispatch problems modeled with linear PF equations. A data-driven framework based on hybrid regression and classification is developed to determine coefficients of the linear PF equation. This problem is equivalent to minimizing a weighted sum of the root-mean-square error and hinge loss, which compels the linear PF model to enforce network constraints accurately. Simulations with various system scales verify that the proposed PF linearization method outperforms existing ones in terms of decision feasibility and optimality.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"41 1","pages":"793-796"},"PeriodicalIF":7.2,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535415","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 : 2025-11-14DOI: 10.1109/TPWRS.2025.3633106
Jiacheng Liu;Jun Liu;Tao Ding;Chao Ren;Rudai Yan
Machine learning (ML)-based transient stability assessment (TSA) provides extraordinary accuracy performance while limited by potential misjudgment risks. To address this issue, this letter originally develops a generic scene-dependent credibility evaluation (SCE) framework. The variance upper bound of ML model prediction error is inferred using an improved localized generalization error estimation (ILGEE) method, and the probability density of system stability is furtherly described as a Gaussian distribution incorporating Neumann boundary condition. Then the scene-dependent credibility index (SCI) is ultimately derived and defined as the information entropy implying the uncertainty of TSA results. Case studies verify the validity of the SCE framework and demonstrate the promising 100% accurate TSA performance with critical proposed SCI as 0.93.
{"title":"A Generic Scene-Dependent Credibility Evaluation Framework for Machine Learning-Based Transient Stability Assessment of Power Systems","authors":"Jiacheng Liu;Jun Liu;Tao Ding;Chao Ren;Rudai Yan","doi":"10.1109/TPWRS.2025.3633106","DOIUrl":"10.1109/TPWRS.2025.3633106","url":null,"abstract":"Machine learning (ML)-based transient stability assessment (TSA) provides extraordinary accuracy performance while limited by potential misjudgment risks. To address this issue, this letter originally develops a generic scene-dependent credibility evaluation (SCE) framework. The variance upper bound of ML model prediction error is inferred using an improved localized generalization error estimation (ILGEE) method, and the probability density of system stability is furtherly described as a Gaussian distribution incorporating Neumann boundary condition. Then the scene-dependent credibility index (SCI) is ultimately derived and defined as the information entropy implying the uncertainty of TSA results. Case studies verify the validity of the SCE framework and demonstrate the promising 100% accurate TSA performance with critical proposed SCI as 0.93.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"41 1","pages":"773-776"},"PeriodicalIF":7.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515708","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 : 2025-11-14DOI: 10.1109/TPWRS.2025.3632611
Ruoyan Fan;Chaofan Lin;Peng Zhang
This letter introduces a novel quantum Newtonian power flow (QNPF) algorithm. The QNPF features a more general quantum circuit that can process non-Hermitian matrices with fewer required qubits and eliminates the need for iterative optimization of gate parameters. Our contributions include: 1) Developing a quantum state-based Newton's power flow framework to enhance accuracy, convergence, and versatility; 2) Integrating quantum singular value transformation to efficiently solve each iteration of QNPF with scalable quantum circuits; and 3) Devising a block-rescaling technique to ensure computational accuracy in ill-conditioned cases. Test results validate the accuracy, scalability and numerical stability of QNPF, underscoring its potential to advance quantum power flow computation.
{"title":"Quantum Newtonian Power Flow","authors":"Ruoyan Fan;Chaofan Lin;Peng Zhang","doi":"10.1109/TPWRS.2025.3632611","DOIUrl":"10.1109/TPWRS.2025.3632611","url":null,"abstract":"This letter introduces a novel quantum Newtonian power flow (QNPF) algorithm. The QNPF features a more general quantum circuit that can process non-Hermitian matrices with fewer required qubits and eliminates the need for iterative optimization of gate parameters. Our contributions include: 1) Developing a quantum state-based Newton's power flow framework to enhance accuracy, convergence, and versatility; 2) Integrating quantum singular value transformation to efficiently solve each iteration of QNPF with scalable quantum circuits; and 3) Devising a block-rescaling technique to ensure computational accuracy in ill-conditioned cases. Test results validate the accuracy, scalability and numerical stability of QNPF, underscoring its potential to advance quantum power flow computation.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"41 1","pages":"777-780"},"PeriodicalIF":7.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515711","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 : 2025-11-11DOI: 10.1109/tpwrs.2025.3631766
Salman S. Shiuab, Vaithianathan Mani Venkatasubramanian, Venkata K. Jandhyala, Gilles Torresan
{"title":"SCADA-Based Detection and Analysis of Oscillations with Inferential Statistics","authors":"Salman S. Shiuab, Vaithianathan Mani Venkatasubramanian, Venkata K. Jandhyala, Gilles Torresan","doi":"10.1109/tpwrs.2025.3631766","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3631766","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"1 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145491861","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 : 2025-11-11DOI: 10.1109/tpwrs.2025.3631373
Jingshi Cui, Yi Guo, Chenye Wu
{"title":"Multi-Period Adaptive Robust Scheduling of Flexibility Providers With Diverse Response Delays","authors":"Jingshi Cui, Yi Guo, Chenye Wu","doi":"10.1109/tpwrs.2025.3631373","DOIUrl":"https://doi.org/10.1109/tpwrs.2025.3631373","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"171 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145491860","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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