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In order to solve the problem of the inability of the existing network security protection system for new energy stations to meet the needs of network anomaly monitoring and alarm caused by the large-scale integration of new energy, a new energy station network security assessment method based on an improved long short-term memory network was proposed. Firstly, based on the architecture of the new energy station network system, the reasons for network security incidents were analyzed. Secondly, based on the random forest algorithm, the Gini coefficient of new energy station network traffic was solved, and then the important coefficients of all network traffic features were calculated to select important features. Finally, important features were input into the long short-term memory network, and attention mechanisms were used to adaptively allocate data time and features, strengthening the emphasis on important time and features in network traffic, thereby improving the accuracy of the model for network security assessment. The experimental results show that this method can accurately evaluate the network security status of new energy power stations. Compared with support vector machines, convolutional neural networks, and traditional long short-term memory networks, the evaluation accuracy has been improved by 12.65%, 9.34% and 8.79%, respectively, enhancing the perception, evaluation, and alarm capabilities of network security status in new energy power systems.

In order to improve the stability of integrating renewable energy sources into the power grid， it is essential to deploy energy storage devices. However， excessive deployment can lead to higher construction costs and lower utilization efficiency. To tackle this challenge， this paper proposes an optimal deployment approach that leverages idle energy storage in new energy stations to reduce the construction costs of third-party energy storage power stations. This approach comprises two stages： In the first stage， an optimal scheduling model for the energy storage devices in new energy stations is developed， with the goal of maximizing the consumption of wind and solar power. In the second stage， after assessing the remaining storage capacity of new energy stations， the deployment of the energy storage power station capacity is optimized to maximize the operational revenue of third-party energy storage power stations. Case analysis demonstrates that third-party energy storage power stations following this approach exhibit reduced energy storage configuration capacity and significantly lower construction and maintenance costs compared to independently constructed energy storage power stations.

Abstract To explore the optimization method of grid-connected voltage support technology in new energy stations, this study first analyzes and discusses this technology. Second, this study describes the deep learning model architecture and feature selection in detail and determines the framework used for the optimization model proposed here. Lastly, the development of optimization and control strategies is investigated, and the optimized model’s effectiveness is verified through experiments. The results reveal that the optimized model's accuracy, precision, recall, and F1 score are higher than those of the comparison model in the performance comparison experiment, reaching the highest values of 0.890, 0.888, 0.878, and 0.883, respectively. This reflects that the optimized model shows high performance on small datasets, and its performance benefits become more pronounced as the data volume increases. This feature is particularly significant because, in practical applications, power systems often need to process large amounts of data to achieve efficient voltage support. In simulation experiments, the optimized model demonstrates excellent performance in terms of response time, stability, robustness, and energy consumption. Moreover, this model effectively addresses various data challenges and uncertainties encountered in grid-connected voltage support technology for power systems, thereby providing robust support for stable and efficient voltage regulation. In light of the findings, this study offers substantial insights for advancing research in the realms of power systems and new energy technologies. The exploration into the application of deep learning and intelligent control strategies within power systems reveals significant potential for transforming grid optimization practices. This study accentuates how data-driven methodologies can revolutionize energy management, paving the way for smarter and more efficient energy systems. By enhancing both the responsiveness and operational efficiency of power grids, the study contributes to the acceleration of digital transformation within the energy sector, fostering innovation and laying a robust foundation for future advancements in energy informatics.

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Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To explore the optimization method of grid-connected voltage support technology in new energy stations, this study first analyzes and discusses this technology. Second, this study describes the deep learning model architecture and feature selection in detail and determines the framework used for the optimization model proposed here. Lastly, the development of optimization and control strategies is investigated, and the optimized model's effectiveness is verified through experiments. The results reveal that the optimized model's accuracy, precision, recall, and F1 score are higher than those of the comparison model in the performance comparison experiment, reaching the highest values of 0.890, 0.888, 0.878, and 0.883, respectively. This reflects that the optimized model shows high performance on small datasets, and its performance benefits become more pronounced as the data volume increases. This feature is particularly significant because, in practical applications, power systems often need to process large amounts of data to achieve efficient voltage support. In simulation experiments, the optimized model demonstrates excellent performance in terms of response time, stability, robustness, and energy consumption. Moreover, this model effectively addresses various data challenges and uncertainties encountered in grid-connected voltage support technology for power systems, thereby providing robust support for stable and efficient voltage regulation. In light of the findings, this study offers substantial insights for advancing research in the realms of power systems and new energy technologies. The exploration into the application of deep learning and intelligent control strategies within power systems reveals significant potential for transforming grid optimization practices. This study accentuates how data-driven methodologies can revolutionize energy management, paving the way for smarter and more efficient energy systems. By enhancing both the responsiveness and operational efficiency of power grids, the study contributes to the acceleration of digital transformation within the energy sector, fostering innovation and laying a robust foundation for future advancements in energy informatics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Electric Power Science & Technology is the property of Changsha University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inspired from sharing economy and advanced energy storage technologies, hybrid shared energy storage (HSES), as an innovative business model, can provide flexible storage leasing services to new energy stations (NESs) and bring additional profits to the energy storage owner. Under this business model, pricing and planning issues are the main focus of the HSES operator to increase revenues but are rarely considered in current studies. Therefore, a Stackelberg game-based three-stage optimal pricing and planning strategy of HSES is formulated for the operator. First, an HSES model considering two leasing options is developed to provide two kinds of short-term use rights of energy storage resources for NESs. Then, the interactions between selfish NESs and the HSES operator are characterized as a Stackelberg game, and a bi-level pricing and planning strategy optimization model is developed to help the HSES operator make optimal decisions. Finally, considering different characteristics in each stage of the Stackelberg game, a three-stage solution method based on the genetic algorithm (GA) and mixed-integer linear programming (MILP) models is proposed to solve the optimization problem. Case studies on six NESs in a certain region are taken to verify the effectiveness of the proposed strategy. Simulation results show that the HSES operator can obtain maximum profit under the proposed pricing and planning strategy. In addition, the proposed HSES leasing model can provide additional benefits to both the operator and NESs.