Pub Date : 2024-09-13DOI: 10.1109/tpwrs.2024.3460427
Reza Bayani, Saeed Manshadi
{"title":"Physics-Informed Graph-Based Learning to Enable Solving Optimal Distribution Switching Problem","authors":"Reza Bayani, Saeed Manshadi","doi":"10.1109/tpwrs.2024.3460427","DOIUrl":"https://doi.org/10.1109/tpwrs.2024.3460427","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"53 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142231639","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 : 2024-09-12DOI: 10.1109/TPWRS.2024.3460424
Wenwen Zhang;Gao Qiu;Hongjun Gao;Yaping Li;Shengchun Yang;Jiahao Yan;Wenbo Mao;Junyong Liu
The low-efficiency and power imbalance risk have challenged the aging fixed time resolution scheduling, especially when facing largely penetrated renewable energies. Time-adaptive unit commitment (T-UC) is recently advanced to solve the issues. However, existing T-UC methods are subjective open-looped, thus may be still far from optimality. To further improve the T-UC, a performance-driven time-adaptive stochastic UC (T-SUC) based on neural network (NN) is proposed. It firstly leverages k-means++ on multivariate forecasts to settle dispatch resolution for SUC. Then, the SUC performances, involving computing efforts and power imbalance risks (PIRs) at the finest horizon, are encoded by neural network. The analyzing for the NN further allows us to feedback the performances to control dispatch resolution. Numerical studies justify that, compared to recent T-UC rivals, our method reduces over 40% of the PIR on the finest intraday time resolution, with the fastest elapsed time.
{"title":"Performance-Driven Time-Adaptive Stochastic Unit Commitment Based on Neural Network","authors":"Wenwen Zhang;Gao Qiu;Hongjun Gao;Yaping Li;Shengchun Yang;Jiahao Yan;Wenbo Mao;Junyong Liu","doi":"10.1109/TPWRS.2024.3460424","DOIUrl":"10.1109/TPWRS.2024.3460424","url":null,"abstract":"The low-efficiency and power imbalance risk have challenged the aging fixed time resolution scheduling, especially when facing largely penetrated renewable energies. Time-adaptive unit commitment (T-UC) is recently advanced to solve the issues. However, existing T-UC methods are subjective open-looped, thus may be still far from optimality. To further improve the T-UC, a performance-driven time-adaptive stochastic UC (T-SUC) based on neural network (NN) is proposed. It firstly leverages \u0000<italic>k-means++</i>\u0000 on multivariate forecasts to settle dispatch resolution for SUC. Then, the SUC performances, involving computing efforts and power imbalance risks (PIRs) at the finest horizon, are encoded by neural network. The analyzing for the NN further allows us to feedback the performances to control dispatch resolution. Numerical studies justify that, compared to recent T-UC rivals, our method reduces over 40% of the PIR on the finest intraday time resolution, with the fastest elapsed time.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"39 6","pages":"7453-7456"},"PeriodicalIF":6.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174750","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 : 2024-09-06DOI: 10.1109/TPWRS.2024.3455764
Jiacheng Liu;Jun Liu;Rudai Yan;Tao Ding
The machine learning-based transient stability assessment (TSA) has shown satisfactory accuracy while been limited by the lack of interpretability. This letter thereby presents a novel deep learning paradigm that naturally embeds the Lyapunov stability theory of dynamic systems, in which approximating Lyapunov functions (LFs) is transformed into traditional regression or classification tasks. The Lyapunov stability theory is firstly extended and then integrated into a specific neural network structure, which consists of a flexible LF approximator and its corresponding gradient adjoint network. It is originally revealed that transient stability binary classification by deep Lyapunov learning (DLL) is equivalent to constructing a semi-analytical LF in the state space. Case studies validate the effectiveness of the proposed DLL scheme.
{"title":"Deep Lyapunov Learning: Embedding the Lyapunov Stability Theory in Interpretable Neural Networks for Transient Stability Assessment","authors":"Jiacheng Liu;Jun Liu;Rudai Yan;Tao Ding","doi":"10.1109/TPWRS.2024.3455764","DOIUrl":"10.1109/TPWRS.2024.3455764","url":null,"abstract":"The machine learning-based transient stability assessment (TSA) has shown satisfactory accuracy while been limited by the lack of interpretability. This letter thereby presents a novel deep learning paradigm that naturally embeds the Lyapunov stability theory of dynamic systems, in which approximating Lyapunov functions (LFs) is transformed into traditional regression or classification tasks. The Lyapunov stability theory is firstly extended and then integrated into a specific neural network structure, which consists of a flexible LF approximator and its corresponding gradient adjoint network. It is originally revealed that transient stability binary classification by deep Lyapunov learning (DLL) is equivalent to constructing a semi-analytical LF in the state space. Case studies validate the effectiveness of the proposed DLL scheme.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"39 6","pages":"7437-7440"},"PeriodicalIF":6.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144283","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 : 2024-09-06DOI: 10.1109/TPWRS.2024.3455785
Guoan Yan;Zhengshuo Li
For many actual distribution systems, an accurate system model might not be available, so the operator has to fit an approximate power flow model over a set of field measurements. To completely shield the adverse impact of outliers that are unavoidable in a training dataset, this letter proposes a novel outlier-immune method to construct a data-driven linear power flow (DD-LPF) model that exhibits a much better out-of-sample accuracy than those constructed by common approaches. Moreover, a continuous relaxation-rounding algorithm is proposed to further accelerate the training process. The computational time of constructing this proposed DD-LPF model is satisfactory enough, which underlines its potential applicability for field applications.
{"title":"Construction of an Outlier-Immune Data-Driven Power Flow Model for Model-Absent Distribution Systems","authors":"Guoan Yan;Zhengshuo Li","doi":"10.1109/TPWRS.2024.3455785","DOIUrl":"10.1109/TPWRS.2024.3455785","url":null,"abstract":"For many actual distribution systems, an accurate system model might not be available, so the operator has to fit an approximate power flow model over a set of field measurements. To completely shield the adverse impact of outliers that are unavoidable in a training dataset, this letter proposes a novel outlier-immune method to construct a data-driven linear power flow (DD-LPF) model that exhibits a much better out-of-sample accuracy than those constructed by common approaches. Moreover, a continuous relaxation-rounding algorithm is proposed to further accelerate the training process. The computational time of constructing this proposed DD-LPF model is satisfactory enough, which underlines its potential applicability for field applications.","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"39 6","pages":"7449-7452"},"PeriodicalIF":6.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142144369","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 : 2024-09-03DOI: 10.1109/tpwrs.2024.3453945
Bohui Wang, Zhanbo Xu, Xiaohong Guan
{"title":"Robust Load Frequency Control for Uncertainties Multi-area Power Systems with Couplings of Systems Dynamics and Reconfigurable Communication Networks-Part I: Concepts, Design, and System Developments","authors":"Bohui Wang, Zhanbo Xu, Xiaohong Guan","doi":"10.1109/tpwrs.2024.3453945","DOIUrl":"https://doi.org/10.1109/tpwrs.2024.3453945","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"1 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130539","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 : 2024-09-03DOI: 10.1109/tpwrs.2024.3453300
Hongxing Ye, Chuyue Tian, Yinyin Ge, Lei Wu
{"title":"Revisit Optimal PMU Placement With Full Zero-Injection Cluster and Redundancy Sharing","authors":"Hongxing Ye, Chuyue Tian, Yinyin Ge, Lei Wu","doi":"10.1109/tpwrs.2024.3453300","DOIUrl":"https://doi.org/10.1109/tpwrs.2024.3453300","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"381 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130543","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 : 2024-09-02DOI: 10.1109/tpwrs.2024.3453043
Sicheng Gong, J.K. Kok, J.F.G. Cobben
{"title":"Rapid Feasibility Assessment of Energy Unit Integration in Distribution Networks","authors":"Sicheng Gong, J.K. Kok, J.F.G. Cobben","doi":"10.1109/tpwrs.2024.3453043","DOIUrl":"https://doi.org/10.1109/tpwrs.2024.3453043","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"381 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123863","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 : 2024-08-30DOI: 10.1109/tpwrs.2024.3452154
Rudai Yan, Yan Xu
{"title":"Multi-Objective and Multi-Agent Deep Reinforcement Learning for Real-Time Decentralized Volt/VAR Control of Distribution Networks Considering PV Inverter Lifetime","authors":"Rudai Yan, Yan Xu","doi":"10.1109/tpwrs.2024.3452154","DOIUrl":"https://doi.org/10.1109/tpwrs.2024.3452154","url":null,"abstract":"","PeriodicalId":13373,"journal":{"name":"IEEE Transactions on Power Systems","volume":"6 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101250","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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