Pub Date : 2024-09-03DOI: 10.1109/tste.2024.3454060
Ande Bala Naga Lingaiah, Narsa Reddy Tummuru
{"title":"A Photovoltaic-Grid Integrated System for the Residential Power Management","authors":"Ande Bala Naga Lingaiah, Narsa Reddy Tummuru","doi":"10.1109/tste.2024.3454060","DOIUrl":"https://doi.org/10.1109/tste.2024.3454060","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"7 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219450","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/tste.2024.3452489
Zhuorui Wu, Meng Zhang, Song Gao, Zheng-Guang Wu, Xiaohong Guan
{"title":"Physics-Informed Reinforcement Learning for Real-Time Optimal Power Flow with Renewable Energy Resources","authors":"Zhuorui Wu, Meng Zhang, Song Gao, Zheng-Guang Wu, Xiaohong Guan","doi":"10.1109/tste.2024.3452489","DOIUrl":"https://doi.org/10.1109/tste.2024.3452489","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"48 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219454","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}
{"title":"Exploiting the Flexibility of District Heating System for Distribution System Operation: Set-Based Characterization and Temporal Decomposition","authors":"Weitao Chen, Xiaojun Wang, Wei Wei, Yin Xu, Jianzhong Wu","doi":"10.1109/tste.2024.3452560","DOIUrl":"https://doi.org/10.1109/tste.2024.3452560","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"306 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219455","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 : 2024-08-28DOI: 10.1109/tste.2024.3450503
Yangjun Zeng, Yiwei Qiu, Jie Zhu, Shi Chen, Buxiang Zhou, Jiarong Li, Bosen Yang, Jin Lin
{"title":"Scheduling Multiple Industrial Electrolyzers in Renewable P2H Systems: A Coordinated Active-Reactive Power Management Method","authors":"Yangjun Zeng, Yiwei Qiu, Jie Zhu, Shi Chen, Buxiang Zhou, Jiarong Li, Bosen Yang, Jin Lin","doi":"10.1109/tste.2024.3450503","DOIUrl":"https://doi.org/10.1109/tste.2024.3450503","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"72 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219457","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 : 2024-08-28DOI: 10.1109/tste.2024.3448366
Haohui Ding, Qinran Hu, Tao Qian, Zaijun Wu
{"title":"Modeling and Optimization Operation of Improved Power-to-Hydrogen-and-Heat method at Low Temperature for Reducing Carbon Emissions","authors":"Haohui Ding, Qinran Hu, Tao Qian, Zaijun Wu","doi":"10.1109/tste.2024.3448366","DOIUrl":"https://doi.org/10.1109/tste.2024.3448366","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"70 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219456","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 : 2024-08-26DOI: 10.1109/tste.2024.3449909
Yonghui Nie, Zhi Li, Jie Zhang, Lei Gao, Yang Li, Hengyu Zhou
{"title":"Optimal Dispatch Strategy for a Multi-microgrid Cooperative Alliance Using a Two-Stage Pricing Mechanism","authors":"Yonghui Nie, Zhi Li, Jie Zhang, Lei Gao, Yang Li, Hengyu Zhou","doi":"10.1109/tste.2024.3449909","DOIUrl":"https://doi.org/10.1109/tste.2024.3449909","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"5 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219458","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 : 2024-08-21DOI: 10.1109/tste.2024.3447023
Keunju Song, Minsoo Kim, Hongseok Kim
{"title":"Graph-based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data","authors":"Keunju Song, Minsoo Kim, Hongseok Kim","doi":"10.1109/tste.2024.3447023","DOIUrl":"https://doi.org/10.1109/tste.2024.3447023","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"13 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219459","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 : 2024-08-16DOI: 10.1109/TSTE.2024.3443117
Weihan Lin;Xiaofan Li;Lei Zuo
The shape of the floating buoy of a point absorber wave energy converter (WEC) plays a crucial role in both wave energy harvesting and current drag reduction. In this study, an approach to optimizing the buoy hull geometry with a neural network that replaces the hydrodynamic analysis software is presented, aimed at reducing the ocean current drag force while improving wave energy captured. A new parametric model is introduced to describe the complex shape of the buoy by utilizing the control points of non-uniform rational b-splines. A neural network is developed to significantly reduce the computational time compared to traditional hydrodynamic simulation methods. The optimal hull shape of the buoy is determined by solving an optimization problem using a genetic algorithm, a global optimization technique. The results of the case studies show that the optimal buoy hull shape reduces 68.7% and 71.1% of the current drag, and 50% of mooring line forces compared to the cylinder-shaped buoy and the optimal-power-shaped hull from literature. The optimal buoy hull shape increases the wave energy extraction by 46.1% compared to the thin-ship-shaped buoy but performs 21.1% worse than the optimal-power-shaped hull from the literature.
{"title":"Shape Optimization of a Point Absorber Wave Energy Converter for Reduced Current Drag and Improved Wave Energy Capture Using Neural Networks and Genetic Algorithms","authors":"Weihan Lin;Xiaofan Li;Lei Zuo","doi":"10.1109/TSTE.2024.3443117","DOIUrl":"10.1109/TSTE.2024.3443117","url":null,"abstract":"The shape of the floating buoy of a point absorber wave energy converter (WEC) plays a crucial role in both wave energy harvesting and current drag reduction. In this study, an approach to optimizing the buoy hull geometry with a neural network that replaces the hydrodynamic analysis software is presented, aimed at reducing the ocean current drag force while improving wave energy captured. A new parametric model is introduced to describe the complex shape of the buoy by utilizing the control points of non-uniform rational b-splines. A neural network is developed to significantly reduce the computational time compared to traditional hydrodynamic simulation methods. The optimal hull shape of the buoy is determined by solving an optimization problem using a genetic algorithm, a global optimization technique. The results of the case studies show that the optimal buoy hull shape reduces 68.7% and 71.1% of the current drag, and 50% of mooring line forces compared to the cylinder-shaped buoy and the optimal-power-shaped hull from literature. The optimal buoy hull shape increases the wave energy extraction by 46.1% compared to the thin-ship-shaped buoy but performs 21.1% worse than the optimal-power-shaped hull from the literature.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2758-2768"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219462","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}
Heating, ventilation, and air conditioning (HVAC) systems constitute a large proportion of building energy consumption and provide considerable potential for power grid regulation. While the HVAC power consumption forecasting task is generally straightforward with sufficient historical data, it becomes challenging when dealing with scarce data. Such situation is common in cases of intermittent data collection or early system implementations, where precise forecasting is required despite limited data available. Considering accessible datasets from nearby or similar HVAC systems through energy management systems, this paper proposes an adaptive transfer learning framework to tackle this issue. Specifically, the framework leverages diverse source domains, employing model-level regularizers to quantify domain discrepancies and an adaptive parameter regulation mechanism to dynamically align source domains with the target domain. Embedded within the framework, a unique deep learning architecture with attention mechanisms is proposed, capable of identifying complex temporal patterns and hierarchical features in HVAC systems. Experiments on public HVAC datasets demonstrate the generalization, accuracy and robustness of our methodology under diverse data-scarce scenarios.
{"title":"An Adaptive Transfer Learning Framework for Data-Scarce HVAC Power Consumption Forecasting","authors":"Yanan Zhang;Gan Zhou;Zhan Liu;Li Huang;Yucheng Ren","doi":"10.1109/TSTE.2024.3444689","DOIUrl":"10.1109/TSTE.2024.3444689","url":null,"abstract":"Heating, ventilation, and air conditioning (HVAC) systems constitute a large proportion of building energy consumption and provide considerable potential for power grid regulation. While the HVAC power consumption forecasting task is generally straightforward with sufficient historical data, it becomes challenging when dealing with scarce data. Such situation is common in cases of intermittent data collection or early system implementations, where precise forecasting is required despite limited data available. Considering accessible datasets from nearby or similar HVAC systems through energy management systems, this paper proposes an adaptive transfer learning framework to tackle this issue. Specifically, the framework leverages diverse source domains, employing model-level regularizers to quantify domain discrepancies and an adaptive parameter regulation mechanism to dynamically align source domains with the target domain. Embedded within the framework, a unique deep learning architecture with attention mechanisms is proposed, capable of identifying complex temporal patterns and hierarchical features in HVAC systems. Experiments on public HVAC datasets demonstrate the generalization, accuracy and robustness of our methodology under diverse data-scarce scenarios.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2815-2825"},"PeriodicalIF":8.6,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219460","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 : 2024-08-16DOI: 10.1109/tste.2024.3444794
Xin Wang, Qi Guo, Chunming Tu, Liang Che, Zhong Xu, Fan Xiao, Tianlin Li, Leiqi Chen
{"title":"A Comprehensive Control Strategy for F-SOP Considering Three-phase Imbalance and Economic Operation in ISLDN","authors":"Xin Wang, Qi Guo, Chunming Tu, Liang Che, Zhong Xu, Fan Xiao, Tianlin Li, Leiqi Chen","doi":"10.1109/tste.2024.3444794","DOIUrl":"https://doi.org/10.1109/tste.2024.3444794","url":null,"abstract":"","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"43 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219461","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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