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Power System Operation and Control Technology

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 535

Special Issue Editor


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Guest Editor
Department of Power Engineering, South China University of Technology, Guangzhou, China
Interests: power system operation and control; application of intelligent control and big data in power systems; planning and reliability assessment of new energy and power systems

Special Issue Information

Dear Colleagues,

I am pleased to announce a call for submissions to a Special Issue of Energies on the topic of "Power System Operation and Control Technology". The purpose of this Special Issue is to explore major challenges and methodological research in the areas of power system operation and maintenance, energy structure transformation on the generation side, the digital transformation of power systems, and strategic planning for power system development in the context of a global consensus of the need to reduce carbon emissions.

In the face of increasingly severe climate and environmental problems and fossil energy crises, China has put forward the development goals of reaching its "carbon peak" by 2030 and "carbon neutrality" by 2060. Power systems are indispensable infrastructure in modern society; their safe and stable operation not only involves a reliable power supply but also national security and social stability. Therefore, it is essential to realize the stable control and safe operation of power systems under the condition that their characteristics change due to the high proportion of renewable energy connected to the grid. Researchers and engineers are working together to explore smart grid technologies, build new power systems geared toward sustainability, and drive a transition in energy composition that will enable the development of advanced technologies for power systems and advance the achievement of global environmental goals.

This Special Issue invites original research papers, review articles, and case studies that encompass a broad range of topics related to power system operation and control technologies. Potential topics of interest include, but are not limited to, the following:

  • The analysis of new power system operation characteristics.
  • Research on power system load and generation power prediction.
  • New energy power system scheduling strategies.
  • Offshore wind power cluster operation and maintenance.
  • Smart grid technology for data monitoring and automatic control.
  • Digital twin technology for power system operation and control.
  • Power system fault prediction and diagnosis based on big data technology.
  • Power system planning and optimization analysis.
  • Energy storage technology under new power systems.
  • Energy storage planning and the operation control of new power systems.
  • The assessment of power grid stability and reliability.

Dr. Zhiwei Liao
Guest Editor

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

  • power systems
  • control technology
  • smart grid
  • energy transformation
  • big data analysis
  • new energy
  • power regulation
  • reliability assessment
  • new power systems
  • wind storage capacity planning
  • power system planning
  • energy storage technology

Published Papers (1 paper)

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Research

17 pages, 4257 KiB  
Article
Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM
by Zhiwei Liao, Wenlong Min, Chengjin Li and Bowen Wang
Energies 2024, 17(12), 2969; https://doi.org/10.3390/en17122969 - 17 Jun 2024
Viewed by 385
Abstract
Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and [...] Read more.
Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved. Full article
(This article belongs to the Special Issue Power System Operation and Control Technology)
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