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Advances in Cooperative Control and State Estimation of Power Systems with Large Scale Renewable Energy Sources: 2nd Edition

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

Deadline for manuscript submissions: 25 April 2025 | Viewed by 785

Special Issue Editors


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Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Interests: power system operation and control; renewable energy integration into distribution systems; distributed algorithms; deep reinforcement learning and its application in networked systems
Special Issues, Collections and Topics in MDPI journals
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: integrated energy system; transportation electrification
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School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China
Interests: power system resilience enhancement; micro-grids, machine learning; smart grids; renewable energy integration; forecasting
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: data analytics for condition monitoring and fault diagnosis of power equipment; state estimation of electrical power systems; asset operation optimization and assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy sources (RESs), such as wind and photovoltaic (PV) power, are rapidly growing due to a drop in their generation cost and their environmentally friendly nature, and this will become the theme of the next generation of power systems within the goal of carbon neutrality. However, as the penetration of RESs climbs, reverse power flows and distributed power iterations may challenge both the traditional voltage regulation system and protection scheduling in distribution systems. Furthermore, such an ongoing process of RES integration will induce significant power fluctuations on the power supply side, and it will simultaneously also continuously reduce the inertia of the power system. The combined effect of low inertia and significant power fluctuations challenges the frequency stability even in large power systems.

In addition, uncertainties of RESs are significantly introduced into both the generation side and demand side, which consequently challenge the effectiveness of traditional methods in the condition assessment, fault diagnosis, and life cycle management of power components in power systems. This requires innovations in the modeling, control, and state estimation of power systems, as well as life prediction, extension, and condition assessment.

This Special Issue aims to present and disseminate the most recent advances that are able to address challenges induced by large-scale RES integration, as discussed above.

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

  • Operation and control of power systems with new energy sources.
  • Measurement and control of transportation energy integrated systems.
  • Modeling and control of renewable power generation.
  • Life cycle assessment, pricing, policies, and energy planning.
  • Artificial intelligence for renewable energies.
  • Advanced monitoring, diagnosis, and big data analytic methods of electrical equipment.
  • Ultra-low- and near-zero-energy consumption buildings with renewable energy integration.
  • Power electronic converters and drives.
  • Modeling of communication–control coupled systems.
  • Frequency regulation in low-inertia systems with high wind penetration.
  • Big data for industrial and energy systems.
  • Smart metering, measurement, instrumentation, and control.
  • Artificial intelligence for industrial process optimization.
  • Optimization of industrial applications and energy systems.

Dr. Licheng Wang
Dr. Ying Han
Dr. Fang Yao
Dr. Shuaibing 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 sources (RESs)
  • photovoltaic (PV)
  • voltage control
  • protection
  • low-inertia system
  • state estimation
  • condition assessment
  • carbon reduction

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Related Special Issue

Published Papers (1 paper)

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Research

17 pages, 5329 KiB  
Article
Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods
by Yue Guo, Yu Song, Zilong Lai, Xuyang Wang, Licheng Wang and Hui Qin
Energies 2025, 18(2), 308; https://doi.org/10.3390/en18020308 - 12 Jan 2025
Viewed by 614
Abstract
In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are then combined with the meteorological sequence features extracted by [...] Read more.
In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are then combined with the meteorological sequence features extracted by the Attention-TCN module. The model leverages the strengths of the TCN, the LSTM, and the self-attention mechanism to enhance prediction accuracy and construct reliable prediction intervals. Aiming to optimize both performance and efficiency, the PSO algorithm is used for hyperparameter optimization. Ablation studies and comparisons with other models confirm the effectiveness, accuracy and robustness of the proposed model. This hybrid approach contributes to improved renewable energy integration, offering a more stable and reliable energy supply. Future work will focus on incorporating intelligent systems for autonomous risk management and real-time control of dynamic PV output fluctuations. Full article
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