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Applied Sciences
Special Issues
Electrochemical Energy Storage in New Power Systems
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Electrochemical Energy Storage in New Power Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Green Sustainable Science and Technology".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 3027

Special Issue Editors

Prof. Dr. Chaolong Zhang

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Guest Editor
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing 211169, China
Interests: battery state estimation; battery RUL prediction; fault diagnosis
Dr. Xiong Xiong

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: new energy; automation; machine learning
Prof. Dr. Aihong Tang

E-Mail Website
Guest Editor
School of Automation, Wuhan University of Technology, Wuhan 430070, China
Interests: power system operation and intelligent control; power system stability control; distributed generation system; microgrid
Dr. Weihang Yan

E-Mail Website
Guest Editor
Grid Integration Research Group, National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: grid-interface for energy storage system; wind-storage integrated system design
Dr. Xiao Wang

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Interests: energy storage technology; virtual storage technology; storage-assisted power system operation and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New power systems (NPSs) significantly increase the penetration of renewable energy. Compared with traditional thermal power, renewable energy is limited by geographical and natural conditions to a large extent, with intermittent time and uneven distribution in space. If the generated electric energy is directly input into the power grid, it will inevitably strongly impact the power grid. The construction of energy storage systems in NPSs is conducive to the large-scale, stable and sustainable utilization of renewable energy, which has become the key supporting technology of the energy revolution. Therefore, in recent years, more and more attention has been paid to the research of energy storage technology. Electrochemical energy storage (EES) has mature technology, a short construction cycle and fast charging and discharging speed. Its power and energy can be flexibly configured according to different needs, and therefore it is widely used in the peak and frequency modulation of NPSs.

This Research Topic focuses on all aspects of the advanced techniques, methods and systems related to EES in NPSs. Both high-quality original research papers and review articles are welcome which cover the latest progress and potential research applications of the relevant areas with interest in materials, modeling, control, and monitoring of EES in NPSs.

Prof. Dr. Chaolong Zhang
Dr. Xiong Xiong
Prof. Dr. Aihong Tang
Dr. Weihang Yan
Dr. Xiao Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electrochemical energy storage
  • new power systems
  • state estimation
  • diagnostics and prognostics
  • digital twin

Published Papers (2 papers)

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Research

23 pages, 8598 KiB  
Article
A Transferable Prediction Approach for the Remaining Useful Life of Lithium-Ion Batteries Based on Small Samples
by Haochen Qin, Xuexin Fan, Yaxiang Fan, Ruitian Wang, Qianyi Shang and Dong Zhang
Appl. Sci. 2023, 13(14), 8498; https://doi.org/10.3390/app13148498 - 23 Jul 2023
Viewed by 976
Abstract
Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This [...] Read more.
Predicting the remaining useful life (RUL) of batteries can help users optimize battery management strategies for better usage planning. However, the RUL prediction accuracy of lithium-ion batteries will face challenges due to fewer data samples available for the new type of battery. This paper proposed a transferable prediction approach for the RUL of lithium-ion batteries based on small samples to reduce time in preparing battery aging data and improve prediction accuracy. This approach, based on improvements from the adaptive boosting algorithm, is called regression tree transfer adaptive boosting (RT-TrAdaBoost). It combines the advantages of ensemble learning and transfer learning and achieves high computational efficiency. The RT-TrAdaBoost approach takes the charging voltage and temperature curve as input and utilizes the classification and regression tree (CART) as the base learner, which has better feature capture ability. In the experiment, the working condition migration experiment and battery type migration experiment are conducted on non-overlapping datasets. The verified results revealed that the RT-TrAdaBoost approach could transfer not only the battery aging knowledge between various working conditions but also realize the RUL migration prediction from lithium iron phosphate battery to lithium cobalt oxide battery. The analysis of error and computation time demonstrates the proposed method’s high efficiency and speed. Full article
(This article belongs to the Special Issue Electrochemical Energy Storage in New Power Systems)
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12 pages, 2045 KiB  
Communication
A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
by Peihua Xu, Maoyuan Zhang, Zhenhong Chen, Biqiang Wang, Chi Cheng and Renfeng Liu
Appl. Sci. 2023, 13(6), 4042; https://doi.org/10.3390/app13064042 - 22 Mar 2023
Cited by 7 | Viewed by 1396
Abstract
Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep [...] Read more.
Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three different power stations and compared their performance with the shallow neural network model. Experimental analysis shows that this model is more competitive in forecasting accuracy and stability. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively. Full article
(This article belongs to the Special Issue Electrochemical Energy Storage in New Power Systems)
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