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Energies
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Data-Driven Large-Scale Power System Operations
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Data-Driven Large-Scale Power System Operations

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

Deadline for manuscript submissions: closed (21 November 2023) | Viewed by 7281

Special Issue Editor

Prof. Dr. Guozheng Han

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, China
Interests: smart distribution grid

Special Issue Information

Dear Colleagues,

The relationship between power systems and data is becoming closer and closer.  A large number of real-time data and historical data have been collected in power systems, providing support in stability and fault handling.  The construction of smart grids has led to the accumulation of many operation data on transmission networks and distribution networks, and marketing systems have gathered a significant number of user energy data.  How to make better use of these data to improve power systems is a very important topic.

The Special Issue aims to present the results of research on data-driven power system operations. Submissions on data-driven power system operation, modeling, fault handling, new energy operation and control, and so on, will be considered. Original research articles, as well as review articles, are welcome.

Prof. Dr. Guozheng Han
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.

Published Papers (5 papers)

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Research

22 pages, 2372 KiB  
Article
Prediction Model of Electric Power Carbon Emissions Based on Extended System Dynamics
by Zhenfen Wu, Zhe Wang, Qiliang Yang and Changyun Li
Energies 2024, 17(2), 472; https://doi.org/10.3390/en17020472 - 18 Jan 2024
Cited by 1 | Viewed by 639
Abstract
In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the “30–60 Dual Carbon”. Under the background of “30–60 Dual Carbon”, this article takes the electric power industry, which [...] Read more.
In response to global climate change, China has committed to peaking carbon emissions by 2030 and achieving carbon neutrality by 2060, commonly known as the “30–60 Dual Carbon”. Under the background of “30–60 Dual Carbon”, this article takes the electric power industry, which is the main industry contributing to China’s carbon emission, as the research object, explores the time and peak value of the carbon peak of the electric power industry, and analyzes whether carbon neutrality can be realized under the peak method, so as to get the carbon neutrality path of the electric power industry and serve as the theoretical basis for the formulation of relevant policies. The Environmental Kuznets Curve inspection and the relationship analysis are carried out, then the system dynamics model is constructed, the carbon emissions from 2020 to 2040 are simulated, and the peak time is predicted. Three different scenarios are set to explore the path of electricity carbon neutralization under the premise of a fixed peak. It is shown that Gross Domestic Product per capita index factors have the largest positive contribution, and thermal power share index factors have the largest negative contribution to electricity carbon emissions. Based on the current efforts of the new policy, carbon emissions can achieve the peak carbon emissions’ target before 2030, and it is expected to peak in 2029, with a peak range of about 4.95 billion tons. After the power industry peaks in 2029, i.e., Scenario 3, from coal 44%, gas 9% (2029) to coal 15%, gas 7% (2060), where the CCUS technology is widely used, this scenario can achieve carbon neutrality in electricity by 2060. Adjusting the power supply structure, strictly controlling the proportion of thermal power, optimizing the industrial structure, and popularization of carbon capture, utilization, and storage technology will all contribute to the “dual carbon” target of the power sector. Full article
(This article belongs to the Special Issue Data-Driven Large-Scale Power System Operations)
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15 pages, 3282 KiB  
Article
Research on the Magnetostrictive Characteristics of Transformers under DC Bias
by Xiaoli Yan, Xia Dong, Guozheng Han, Xiaodong Yu and Fengying Ma
Energies 2023, 16(11), 4457; https://doi.org/10.3390/en16114457 - 31 May 2023
Viewed by 1006
Abstract
Direct current (DC) bias leads to increased vibration and noise in transformers. One of the main causes is the magnetostrictive effect of the transformer core. To address this phenomenon of magnetostriction, firstly, a transmission line model (TLM) of a single-phase transformer under DC [...] Read more.
Direct current (DC) bias leads to increased vibration and noise in transformers. One of the main causes is the magnetostrictive effect of the transformer core. To address this phenomenon of magnetostriction, firstly, a transmission line model (TLM) of a single-phase transformer under DC bias is developed using transmission line theory and Jiles–Atherton (J–A) ferromagnetic hysteresis theory, taking into account the winding copper loss, core eddy current loss, and leakage effect. Secondly, the time-domain simulation of the single-phase transformer based on the Newton–Raphson iterative method is carried out, and the magnetostriction characteristics of the transformer under different DC and its variation law are analyzed. Finally, the results show that the DC bias results in magnetostrictive distortion and vibration acceleration curve distortion, the left and right wings of the magnetostrictive butterfly curve are no longer symmetrical, the slope of the vibration acceleration image increases significantly, and the degree of distortion is positively correlated with the magnitude of the DC. In addition, the peak values of the magnetostrictive deformation and vibration acceleration become larger under DC bias, leading to an increase in the vibration and noise of the transformer. The research object of this paper is the single-phase transformer, and the research method can also be applied to the study of three-phase transformers. Full article
(This article belongs to the Special Issue Data-Driven Large-Scale Power System Operations)
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15 pages, 6526 KiB  
Article
Research on Data-Driven Optimal Scheduling of Power System
by Jianxun Luo, Wei Zhang, Hui Wang, Wenmiao Wei and Jinpeng He
Energies 2023, 16(6), 2926; https://doi.org/10.3390/en16062926 - 22 Mar 2023
Cited by 4 | Viewed by 1300
Abstract
The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a [...] Read more.
The uncertainty of output makes it difficult to effectively solve the economic security dispatching problem of the power grid when a high proportion of renewable energy generating units are integrated into the power grid. Based on the proximal policy optimization (PPO) algorithm, a safe and economical grid scheduling method is designed. First, constraints on the safe and economical operation of renewable energy power systems are defined. Then, the quintuple of Markov decision process is defined under the framework of deep reinforcement learning, and the dispatching optimization problem is transformed into Markov decision process. To solve the problem of low sample data utilization in online reinforcement learning strategies, a PPO optimization algorithm based on the Kullback–Leibler (KL) divergence penalty factor and importance sampling technique is proposed, which transforms on-policy into off-policy and improves sample utilization. Finally, the simulation analysis of the example shows that in a power system with a high proportion of renewable energy generating units connected to the grid, the proposed scheduling strategy can meet the load demand under different load trends. In the dispatch cycle with different renewable energy generation rates, renewable energy can be absorbed to the maximum extent to ensure the safe and economic operation of the grid. Full article
(This article belongs to the Special Issue Data-Driven Large-Scale Power System Operations)
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16 pages, 2159 KiB  
Article
Research on Transformer Voiceprint Anomaly Detection Based on Data-Driven
by Da Yu, Wei Zhang and Hui Wang
Energies 2023, 16(5), 2151; https://doi.org/10.3390/en16052151 - 23 Feb 2023
Cited by 4 | Viewed by 1824
Abstract
Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for [...] Read more.
Condition diagnosis of power transformers using acoustic signals is a nonstop, contactless method of equipment maintenance that can diagnose the transformer’s type of abnormal condition. To heighten the accuracy and efficiency of the abnormal method of diagnosing abnormalities by sound, a method for abnormal diagnosis of power transformers based on the Attention-CNN-LSTM hybrid model is proposed. This collects the sound signals emitted by the real power transformer in the normal state, overload, and the discharge condition. It preprocesses the sound signals to obtain the MFCC characteristics of the sound signals. It is then grouped into a set of sound feature vectors by the first- and second-order differences, and enters the Attention-CNN-LSTM hybrid model for training. The training results show that the Attention-CNN-LSTM hybrid model can be used for the status sound detection of power transformers, and the recognition of the three states can achieve an accuracy rate of more than 99%. Full article
(This article belongs to the Special Issue Data-Driven Large-Scale Power System Operations)
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20 pages, 3965 KiB  
Article
Topology Identification of Low-Voltage Power Lines Based on IEC 61850 and the Clustering Method
by Lingyan Sun, Yu Chen, Qinjun Du, Rui Ding, Zhidong Liu and Qian Cheng
Energies 2023, 16(3), 1126; https://doi.org/10.3390/en16031126 - 19 Jan 2023
Cited by 2 | Viewed by 1915
Abstract
The large-scale access of distributed power puts forward higher requirements for the monitoring of the distribution networks, and the topology identification of low-voltage power lines can effectively promote the integration of monitoring data and the distribution network information, effectively realizing the rapid identification [...] Read more.
The large-scale access of distributed power puts forward higher requirements for the monitoring of the distribution networks, and the topology identification of low-voltage power lines can effectively promote the integration of monitoring data and the distribution network information, effectively realizing the rapid identification of faults and ensuring the safety of users. In this paper, the method of graph theory was used to simplify the analysis of low-voltage lines, and the full topology identification strategy was proposed. Based on IEC 61850 SCL topology configuration information, line topology identification within the region was realized, and the correlation between regions was determined by the injection method. According to the configuration information, regional association information, and user’s collection information, the low-voltage station area line topology was divided into known regional topology and unknown regional topology. Aiming for the identification of line topology in the unknown region, according to the similarity of voltage fluctuations over short electrical distances, clustering analysis of user’s voltage data in the unknown region was carried out based on the k-means clustering algorithm. The test results showed that this scheme can realize the identification of topology in the region. Full article
(This article belongs to the Special Issue Data-Driven Large-Scale Power System Operations)
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