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Power System Operation and Control Technology
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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: closed (30 April 2025) | Viewed by 4889

Special Issue Editor

Dr. Zhiwei Liao

E-Mail Website
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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

16 pages, 2926 KiB  
Article
Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method
by Tianxiao Mo, Jun Liu, Jiacheng Liu, Guangyao Wang, Yuting Li and Kaiwei Lin
Energies 2024, 17(23), 5864; https://doi.org/10.3390/en17235864 - 22 Nov 2024
Viewed by 750
Abstract
The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria [...] Read more.
The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria have strong interpretability, but they also have great limitations. They are still difficult to apply to complex power systems and are in urgent need of improvement. Artificial intelligence methods have high accuracy in stability judgment, but they have problems such as poor interpretability, and their stability judgment results are often difficult to explain. Based on the transient stability judgment mechanism of the response-driven transient energy function, this paper proposes a transient energy function stability judgment method based on a two-machine equivalent model and enhanced by a convolutional neural network. Firstly, the ST-kmeans method is used to cluster the generator sets, and the S-transformation is performed on the power angle changes of the generator sets to extract features. Then, the principal component analysis method is used to reduce the dimension of the feature data. Based on the k-means clustering method, the IEEE-39 node system generator synchronization units are grouped according to the power angle change trend of each generator after the fault. On the basis of the above methods, a two-machine equivalent model of the IEEE-39 node system is established, and the transient energy function of the two-machine system is derived. Based on the convolutional neural network, the critical energy is enhanced, and the fixed critical energy threshold is replaced by the corrected critical energy. The example results show that the transient stability prediction framework proposed in this paper can improve the scope of the application of mechanism discrimination and enhance the interpretability of the results of the intelligent method. Full article
(This article belongs to the Special Issue Power System Operation and Control Technology)
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22 pages, 1943 KiB  
Article
Sensitivity Analysis and Distribution Factor Calculation under Power Network Branch Power Flow Exceedance
by Shuqin Sun, Zhenghai Yuan, Weiqiang Liang, Xin Qi and Guanghao Zhou
Energies 2024, 17(17), 4374; https://doi.org/10.3390/en17174374 - 1 Sep 2024
Viewed by 1335
Abstract
As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This [...] Read more.
As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system. Full article
(This article belongs to the Special Issue Power System Operation and Control Technology)
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12 pages, 4224 KiB  
Article
Flexible Common-Mode Voltage Modulation Strategy for Differential Boost Inverters
by Zhaozhe Deng, Hao Qin, Wenbo Zhu, Qi Qiu, Yan Deng and Xiangning He
Energies 2024, 17(16), 3891; https://doi.org/10.3390/en17163891 - 7 Aug 2024
Viewed by 910
Abstract
Photovoltaic power generation and energy storage technology are current hotspots in the clean energy industry. As a core piece of equipment, an inverter is subjected to higher demands for its voltage regulation range and output performance. A differential boost inverter features a boosting [...] Read more.
Photovoltaic power generation and energy storage technology are current hotspots in the clean energy industry. As a core piece of equipment, an inverter is subjected to higher demands for its voltage regulation range and output performance. A differential boost inverter features a boosting function not found in traditional inverters, effectively widening the input voltage range, with characteristics of a single-stage structure and simple modulation. However, the differential boost inverter faces the issue of high common-mode voltage. Considering that the magnitude of the common-mode voltage changes with different modulation methods, a novel modulation strategy with a flexible common-mode voltage is proposed, which can effectively suppress the common-mode voltage under the same gain without affecting the output power quality. This article first introduces the topology, working principle, and traditional modulation methods of the differential boost inverter. Then, the new modulation strategy is defined, and the gain equation is derived. The new modulation method’s applicable scenarios are discussed and compared to other modulation methods based on the gain curve, concluding that the new method has advantages in terms of common-mode voltage amplitude and power quality under low boost gain. Finally, the effectiveness of the proposed method is verified through simulation and experiments. Full article
(This article belongs to the Special Issue Power System Operation and Control Technology)
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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
Cited by 3 | Viewed by 1318
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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