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Advances in the Monitoring, Evaluation, Operation and Development of High-Penetration Renewable Energy Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 649

Special Issue Editors


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Guest Editor
Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Interests: high voltage; electrical insulation; heat sink; PCM; multiphysics coupling; thyristor; temperature field; cellulose insulation; transformers
Special Issues, Collections and Topics in MDPI journals
Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: optimization for power system operation; transient stability; power system analysis; optimal power flow
Special Issues, Collections and Topics in MDPI journals
School of Electrical Engineering, Guangxi University, Nanning 530004, China
Interests: long-term planning for new power systems; electric vehicle charging load forecasting and control; power system optimization operation; electricity market; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growing global focus on renewable energy, high-penetration renewable energy power systems are rapidly gaining traction. However, the monitoring, evaluation, and operation of high-penetration renewable energy power systems face a complex set of technical and management challenges. These include a mismatch between energy production and consumption, uncertain renewable energy forecasts, the dynamic scheduling and stability of power networks, transformer status assessment, the electricity–water–carbon nexus, electricity market operation assessment, etc. To address these challenges and achieve reliable operation of high-penetration renewable energy power systems, in-depth research and technological innovation are needed, as well as feasible policies and measures to ensure the sustainable use of renewable energy, reduce carbon emissions, and achieve a secure and reliable supply of energy.

This Special Issue aims to present the latest developments related to advances in the monitoring, evaluation, and operation of high-penetration renewable energy power systems.

Topics of interest for publication include, but are not limited to:

  • High penetration of renewable energy;
  • Techniques for the monitoring, evaluation, and operation of power systems;
  • On-line and off-line condition monitoring techniques;
  • Condition monitoring and evaluation technology for transformers;
  • The electricity–water–carbon nexus and other green energy fields;
  • Renewable energy methodology;
  • Operational assessment of the electricity market;
  • Renewable energy operation technology;
  • Advanced modeling approaches;
  • Condition assessment techniques of transformer;
  • Condition assessment techniques of power system;
  • Operational assessment of power system.

Prof. Dr. Yiyi Zhang
Dr. Sen Guo
Dr. Yude Yang
Dr. Bo Li
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. Energies 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 2600 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

  • renewable energy
  • power systems
  • electricity–water–carbon nexus
  • renewable energy forecasts
  • electricity market

Published Papers (1 paper)

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Research

15 pages, 7134 KiB  
Article
Prediction Model for Trends in Submarine Cable Burial Depth Variation Considering Dynamic Thermal Resistance Characteristics
by Zhenxing Hu, Xueyong Ye, Xiaokang Luo, Hao Zhang, Mingguang He, Jiaxing Li and Qian Li
Energies 2024, 17(9), 2127; https://doi.org/10.3390/en17092127 - 29 Apr 2024
Viewed by 340
Abstract
Fault problems associated with submarine cables caused by variations in their burial depth are becoming increasingly prominent. To address the difficulty of detecting the burial depth of submarine cables and trends in its variation, a prediction model for submarine cable burial depth was [...] Read more.
Fault problems associated with submarine cables caused by variations in their burial depth are becoming increasingly prominent. To address the difficulty of detecting the burial depth of submarine cables and trends in its variation, a prediction model for submarine cable burial depth was proposed which considers the dynamic characteristics of thermal resistance. First, a parallel thermal circuit model of a three-core submarine cable was established, and a formula for calculating the submarine cable’s burial depth was derived based on a formula for calculating the submarine cable’s core temperature. Then, the calculation result was corrected by considering the dynamic characteristics of the thermal resistance of the submarine cable’s structural materials. On this basis, feature vectors associated with the seabed cable burial depth calculation data and time nodes were mined by a convolutional neural network and used as the input parameters of a long short-term memory network for optimization and training, and a prediction model for trends in seabed cable burial depth variation was obtained. Finally, an example analysis was carried out based on the actual electrical parameter data of submarine cables buried by an offshore oil and gas platform. The results showed that the prediction model for trends in variations in the burial depth of submarine cables based on the CNN-LSTM neural network can achieve high prediction accuracy and prediction efficiency. Full article
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