IEEE Transactions on Sustainable Energy最新文献_第5页

Virtuality-Reality Combination Control for Wind Farm Maximum Power Generation With Wake Model Dynamic Calibration

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-13 DOI: 10.1109/TSTE.2024.3497013

Jinxin Xiao;Pengda Wang;Sheng Huang;Qiaoqiao Luo;Weimin Chen;Juan Wei

{"title":"Virtuality-Reality Combination Control for Wind Farm Maximum Power Generation With Wake Model Dynamic Calibration","authors":"Jinxin Xiao;Pengda Wang;Sheng Huang;Qiaoqiao Luo;Weimin Chen;Juan Wei","doi":"10.1109/TSTE.2024.3497013","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3497013","url":null,"abstract":"Due to the time delay characteristic of wake effect, the future state information of downstream wind turbines (WTs) is required for wind farm (WF) dynamic optimization but cannot be directly measured. To address the issue, this study proposes a novel virtuality-reality combination control scheme for WF dynamic maximum power generation control (DMPGC). The wind speed in VWF is calculated by wake model without time delay, thus the future state information corresponding to the current freestream wind speed of RWF can be obtained in advance. To ensure consistency of state information between RWF and VWF, meanwhile to enhance the precision of WF optimization model, a wake model dynamic calibration method is proposed to improve the prediction accuracy of wake wind speed. Thereafter, an active wake control strategy based on calibrated wake model is implemented to maximize the total power generation of VWF, and the optimal control commands are delay dispatched to RWF according to the wake delay time. Simulation results show that the proposed scheme improves calculation accuracy of wake model, increases total power generation and owns better fatigue load distribution of WF under different wind conditions.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1007-1020"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675952","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}

引用次数: 0

Machine Learning-Accelerated Method for Real-Time Optimization of Micro Energy-Water-Hydrogen Nexus

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-13 DOI: 10.1109/TSTE.2024.3496912

Mostafa Goodarzi;Qifeng Li

{"title":"Machine Learning-Accelerated Method for Real-Time Optimization of Micro Energy-Water-Hydrogen Nexus","authors":"Mostafa Goodarzi;Qifeng Li","doi":"10.1109/TSTE.2024.3496912","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3496912","url":null,"abstract":"This paper explores the micro Energy-Water- Hydrogen (<italic>m</i>-EWH) nexus, an engineering system designed to reduce carbon emissions in the power sector. The <italic>m</i>-EWH nexus leverages renewable energy sources (RES) to produce hydrogen via electrolysis, which is then combined with carbon captured from fossil fuel power plants to mitigate emissions. To address the uncertainty challenges posed by RES, this paper proposes a real-time decision-making framework for the <italic>m</i>-EWH nexus, which requires the rapid solution of large-scale mixed-integer convex programming (MICP) problems. To this end, we develop a machine learning-accelerated solution method for real-time optimization (MARO), comprising three key modules: (1) an active constraint and integer variable prediction module that rapidly solves MICP problems using historical optimization data; (2) an optimal strategy selection module based on feasibility ranking to ensure solution feasibility; and (3) a feature space extension and refinement module to improve solution accuracy by generating new features and refining existing ones. The effectiveness of the MARO method is validated through two case studies of the <italic>m</i>-EWH nexus, demonstrating its capability to swiftly and accurately solve MICP problems for this complex system.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"995-1006"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676006","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}

引用次数: 0

An Efficient Affine Arithmetic-Based Optimal Dispatch Method for Active Distribution Networks With Uncertainties of Electric Vehicles

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-13 DOI: 10.1109/TSTE.2024.3497659

Wei Dai;Hongzhou Li;Hui Liu;Hui Hwang Goh;Xiansong Yuan;Yuelin Liu;Baicheng Chen

{"title":"An Efficient Affine Arithmetic-Based Optimal Dispatch Method for Active Distribution Networks With Uncertainties of Electric Vehicles","authors":"Wei Dai;Hongzhou Li;Hui Liu;Hui Hwang Goh;Xiansong Yuan;Yuelin Liu;Baicheng Chen","doi":"10.1109/TSTE.2024.3497659","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3497659","url":null,"abstract":"Affine Arithmetic (AA) is an effective interval analysis method for addressing uncertainties in power systems. However, previous research on AA-based optimization problems has struggled to accurately capture the uncertainties associated with electric vehicles (EVs) and the cumulative impact of uncertainties on energy storage systems (ESSs). Moreover, the reformulated AA model presents a significant computational challenge due to the high number of variables and constraints. This study proposes an efficient AA-based economic dispatch (AAED) method for active distribution networks incorporating EVs and ESSs while accounting for uncertainties. Specifically, an EV charging load-interval (CLI) model is developed to effectively capture the randomness of plug-in/plug-out times and initial/target energy. A confidence level is defined to prevent excessive conservatism in the CLI model. An ESS model is also formulated within the AA domain to address the cumulative impact of persistent uncertainty, ensuring an accurate state of charge monitoring. To enhance the computational efficiency of the AAED model without sacrificing accuracy, a fast-solving strategy is introduced. This strategy involves eliminating many state variables and constraints and replacing them with derived analytical partial deviation formulations that map the relationship between state and decision variables. Simulation results confirm the effectiveness of the proposed model and method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1021-1036"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675950","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}

引用次数: 0

Enhancing Fault Ride-Through Capability of DFIG-Based WECS Using Dynamic Reconfiguration Hybrid Interlinking Transformer Technique

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-13 DOI: 10.1109/TSTE.2024.3497914

Jinmu Lai;Yang Liu;Xin Yin;Lin Jiang;Wei Yao;Fan Xiao;Jiaxuan Hu;Zia Ullah

{"title":"Enhancing Fault Ride-Through Capability of DFIG-Based WECS Using Dynamic Reconfiguration Hybrid Interlinking Transformer Technique","authors":"Jinmu Lai;Yang Liu;Xin Yin;Lin Jiang;Wei Yao;Fan Xiao;Jiaxuan Hu;Zia Ullah","doi":"10.1109/TSTE.2024.3497914","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3497914","url":null,"abstract":"The abnormal grid voltages, such as sags, swells, and harmonics caused by grid faults, seriously threaten the safe operation of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS). To enhance the fault ride-through (FRT) capability of DFIG and improve the converter capacity utilization, this paper proposes a novel DFIG-based WECS using a dynamic reconfiguration hybrid interlinking transformer (DR-HIT) technique for performance improvement under grid faults. Multiple operating modes and flexible switching strategies were developed based on the analysis of the proposed topology and principles. The proposed DR-HIT approach smooths the DFIG ’s output power fluctuations through the cooperative control of the multifunctional converter (MFC) and grid-side converter (GSC) in shunt mode when the grid voltage is stable. Upon the occurrence of a grid voltage fault, the DR-HIT flexibly switches from shunt mode to series mode, maintaining the terminal voltage at a constant value. Additionally, once grid voltage recovers, the DR-HIT reverts flexibly to its shunt mode. Finally, simulations and experimental results demonstrate that the proposed scheme can achieve accurate control of the system and flexible switching between different modes.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1037-1055"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667492","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}

引用次数: 0

Cooperative Strategies for Frequency Control of Wind Turbines to Mitigate Secondary Frequency Dip: Coefficient Allocation and Exit Techniques

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-13 DOI: 10.1109/TSTE.2024.3498006

Zishuo Huang;Wenchuan Wu;Chenhui Lin;Zizhen Guo

{"title":"Cooperative Strategies for Frequency Control of Wind Turbines to Mitigate Secondary Frequency Dip: Coefficient Allocation and Exit Techniques","authors":"Zishuo Huang;Wenchuan Wu;Chenhui Lin;Zizhen Guo","doi":"10.1109/TSTE.2024.3498006","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3498006","url":null,"abstract":"With the increasing integration of wind power into the power system, the incorporation of wind turbines into the grid's primary frequency regulation through inertia and droop control has been proven effective. However, a phenomenon known as secondary frequency dip (SFD) occurs when wind generators exit frequency regulation to restore the turbines’ speeds. This paper introduces a cooperative approach to mitigate SFD. Initially, a system frequency response model is established, incorporating the combined effects of synchronous generators and wind turbines. Subsequently, a model to forecast the rotational speed of each wind turbine in response to load changes is developed. Based on these models, the droop and inertia coefficients of different turbines in a wind farm are optimized to minimize overall wind energy loss during frequency regulation, thereby alleviating SFD, while ensuring the rotational speed remains within a safe range. Additionally, a smooth transition strategy based on a low-pass filter is proposed to prevent an abrupt decrease in active power as turbines exit frequency regulation. Finally, to prevent a simultaneous drop in active power among a large number of wind turbines, a sequential exit strategy from frequency regulation is proposed. Simulation results validate the effectiveness of the proposed methods in mitigating SFD.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"1056-1067"},"PeriodicalIF":8.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667489","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}

引用次数: 0

System Strength Constrained Grid-Forming Energy Storage Planning in Renewable Power Systems

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-08 DOI: 10.1109/TSTE.2024.3494259

Yun Liu;Yue Chen;Huanhai Xin;Jingzhe Tu;Lin Zhang;Meiyi Song;Jizhong Zhu

{"title":"System Strength Constrained Grid-Forming Energy Storage Planning in Renewable Power Systems","authors":"Yun Liu;Yue Chen;Huanhai Xin;Jingzhe Tu;Lin Zhang;Meiyi Song;Jizhong Zhu","doi":"10.1109/TSTE.2024.3494259","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3494259","url":null,"abstract":"With more inverter-based renewable energy resources replacing synchronous generators, the system strength of modern power networks significantly decreases, which may induce small-signal stability (SS) issues. It is commonly acknowledged that grid-forming (GFM) converter-based energy storage systems (ESSs) enjoy the merits of flexibility and effectiveness in enhancing system strength, but how to simultaneously consider the economic efficiency and system-strength support capability in the planning stage remains unexplored. To bridge the research gap, this paper develops a system strength constrained optimal planning approach of GFM ESSs to achieve a desired level of SS margin. To this end, the influence of GFM ESS power capacities and locations on the system strength is firstly quantified based on the framework of generalized short-circuit ratio. On this basis, system strength constrained optimal placement and sizing of GFM ESSs is formulated into optimization problems with eigenvalue constraints. Two practical scenarios with and without a limit on the number of selected sites are considered. Finally, quadratic support function based iterative optimization approaches are developed to address the planning problems. Case studies in the modified IEEE 39-bus and 118-bus systems validate the effectiveness and efficiency of the proposed approaches under different scenarios by comparing with two other benchmarks.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"981-994"},"PeriodicalIF":8.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675957","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}

引用次数: 0

Pitch Control Scheme Considering Entire Dynamics and Full-Load Region in PMSG-Based Wind Turbines

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-07 DOI: 10.1109/TSTE.2024.3493961

Ali Safaeinejad;Mohsen Rahimi;Dao Zhou;Frede Blaabjerg

{"title":"Pitch Control Scheme Considering Entire Dynamics and Full-Load Region in PMSG-Based Wind Turbines","authors":"Ali Safaeinejad;Mohsen Rahimi;Dao Zhou;Frede Blaabjerg","doi":"10.1109/TSTE.2024.3493961","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3493961","url":null,"abstract":"Large-scale wind turbines (WTs) are built with light-strength materials, which would otherwise cost more than the economic benefits of power generation. Hence, these turbines with huge rotors and slender towers are more exposed to external forces such as gust winds and the wake effect during their operational lifetime. This paper strives to establish a bridge between the design principles of the pitch control system (PCS) and the inherent dynamics of the drivetrain, blades, and tower in a grid-tied 5MW PMSG-based WT. Based on this purpose, the dynamic representation of the PCS is described in more detail, then the pitch controller is designed based on the complete dynamic model of the WT using a gain-scheduled PI controller to be capable of providing desirable dynamical performance throughout the pitch actuation region. The parameters of the proposed controller are calculated according to the current operating point of the WT with the aim of ensuring the acceptable stability margin and reducing the WT loading as much as possible. The controller design process is accomplished by analyzing the linearized dynamic model of the PCS under various scenarios using responses resulting from the frequency domain, polar coordinate, and modal analysis. Finally, nonlinear simulations illustrate that the intended pitch controller has a superior response over the traditional PI controllers.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"955-969"},"PeriodicalIF":8.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676071","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}

引用次数: 0

A Robust Photovoltaic Power Forecasting Method Based on Multimodal Learning Using Satellite Images and Time Series

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-07 DOI: 10.1109/TSTE.2024.3494266

Kai Wang;Shuo Shan;Weijing Dou;Haikun Wei;Kanjian Zhang

{"title":"A Robust Photovoltaic Power Forecasting Method Based on Multimodal Learning Using Satellite Images and Time Series","authors":"Kai Wang;Shuo Shan;Weijing Dou;Haikun Wei;Kanjian Zhang","doi":"10.1109/TSTE.2024.3494266","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3494266","url":null,"abstract":"Ultra-short-term photovoltaic (PV) power forecasting holds significant importance in enhancing grid stability. Most PV power forecasting methods based on satellite images rely on pixel-level predictions, which are inefficient and redundant. Meanwhile, current deep-learning approaches struggle to establish correlations between large-scale cloud features and PV generation patterns. In this paper, an end-to-end model based on multimodal learning is proposed for directly obtaining multi-step PV power forecasts from satellite images and time series. To capture cloud dynamics and features within the region of interest (RoI), ConvLSTM-RICNN is utilized to encode satellite images. To mitigate the impact of noise and missing data in PV power, a robust fusion approach named DCCA-LF is introduced. This approach integrates deep canonical correlation analysis (DCCA) into late fusion (LF) to strengthen cross-modal feature alignment. The proposed model is verified using publicly available data from BP Solar in Alice Springs and Himawari-8, from January 1, 2020, to October 8, 2022. Comparison with current state-of-the-art research shows that the suggested model achieves the best RMSE and MAE with minimal complexity across all cloud conditions. Moreover, the proposed approach is robust to noise and missing data.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"970-980"},"PeriodicalIF":8.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675891","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}

引用次数: 0

Cooperative Control for DFIG-Based Wind Turbine Generation System Covering All Operating Regions

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-06 DOI: 10.1109/TSTE.2024.3492726

Yong Wan

{"title":"Cooperative Control for DFIG-Based Wind Turbine Generation System Covering All Operating Regions","authors":"Yong Wan","doi":"10.1109/TSTE.2024.3492726","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3492726","url":null,"abstract":"This paper addresses the multiple control problems over the whole wind speed range for the wind turbine generation system (WTGS), including the safety constraints on the rotational speed and the mechanical power, and the maximum power point tracking, by proposing a novel estimator-based cooperative control scheme for doubly fed induction generator (DFIG) and wind turbine. Firstly, we develop a global estimator to obtain precisely the unmeasurable shift-area turbine speed, which is effective for all wind speed zones and is employed to support the decision-making of the actual operating region and the desired reference signals. Secondly, based on this, a nonlinear adaptive DFIG controller and a pitch angle controller are designed to track the reference point, which ensures the completeness of the control tasks of the corresponding identified operating region. Thirdly, we apply the universal barrier Lyapunov function to the control synthesis of DFIG to impose the physical security constraints with any adjustable safety margin on WTGS. The superiority of the proposed framework is evaluated comparatively on the detailed WTGS model using multiple Monte Carlo simulations with three kinds of stochastic wind speed processes.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"945-954"},"PeriodicalIF":8.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676073","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}

引用次数: 0

Short-Term Wind Power Prediction Based on Wind2vec-BERT Model

IF 8.6 1区工程技术

IEEE Transactions on Sustainable Energy Pub Date : 2024-11-06 DOI: 10.1109/TSTE.2024.3492497

Miao Yu;Jinyang Han;Honghao Wu;Jiaxin Yan;Runxin Zeng

{"title":"Short-Term Wind Power Prediction Based on Wind2vec-BERT Model","authors":"Miao Yu;Jinyang Han;Honghao Wu;Jiaxin Yan;Runxin Zeng","doi":"10.1109/TSTE.2024.3492497","DOIUrl":"https://doi.org/10.1109/TSTE.2024.3492497","url":null,"abstract":"In the era of new energy development, the requirements for all aspects of short-term wind power forecasting tasks are increasing day by day. However, the power condition of wind farms is naturally stochastic and variable as it is affected by multiple factors. Current neural network approaches focus only on the propagation of unidirectional attention and ignore the interaction of input variables. To further improve the accuracy of wind power prediction, this paper explores the application of the Bidirectional Encoder Representations from Transformers (BERT) algorithm in wind power prediction. At the same time, GARCH series models are used for analysis and optimization after the prediction results are obtained to address the challenges posed by the inherent variability of wind. Meanwhile, Wind2vec, a new variable embedding method for wind power forecasting tasks, is proposed which can more efficiently fit the relationship between time series forecasting variables. The parameters are subsequently fine-tuned for the backbone layer of the BERT using the Adaptive Computation Time (ACT) method to make it more adaptive to the inputs of the power sequences of the power system. By BERT's bidirectional attention mechanism and transformer architecture, and refining it for the input layer, we aim to enhance the accuracy of wind power forecasts by capturing nuanced temporal dependencies within historical wind data. Using China Southern Power Grid real datasets demonstrates the effectiveness and correctness of the BERT-GARCH-M-based model in outperforming traditional forecasting methods. This research not only shows the adaptability of BERT to wind power prediction but also contributes to advancing the precision and reliability of renewable energy forecasts, paving the way for more sustainable energy utilization in the evolving landscape of new energy paradigms.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"16 2","pages":"933-944"},"PeriodicalIF":8.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675890","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}

引用次数: 0