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Energies
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Optimizing Power Quality in Smart Grid Systems
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Optimizing Power Quality in Smart Grid Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2714

Special Issue Editors

Prof. Dr. Yanhong Luo

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: distribution network; smart grid; energy internet
Dr. Bowen Zhou

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: power and energy system operation and control; vehicle-to-grid; virtual energy storage and demand response; intelligent control of industrial loads; renewable energy; energy internet
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The optimization of power quality in smart grid systems has become a significant area of research and development in recent years. Smart grid systems offer advanced monitoring, control, and communication capabilities that enable the implementation of various techniques to optimize power quality. These techniques aim to detect and mitigate power quality disturbances in real time, ensuring a stable and high-quality power supply.

One approach to optimizing power quality in smart grid systems is through the deployment of advanced monitoring devices and sensors. These devices collect data on various power quality parameters, such as voltage, current, and frequency, allowing real-time monitoring and analysis. With this information, utilities can identify and locate power quality issues quickly, enabling prompt corrective action.

Another strategy involves the use of advanced control algorithms and intelligent devices. These algorithms analyze the collected data and make decisions to mitigate power quality disturbances. For example, when a voltage sag is detected, an intelligent device can quickly compensate by injecting reactive power to maintain voltage stability.

Furthermore, communication technologies play a crucial role in power quality optimization. Smart grid systems rely on robust communication networks to facilitate the exchange of information between different components, such as sensors, control devices, and utility control centers. This enables coordinated control and management of power quality across the grid, ensuring efficient and effective responses to disturbances.

Overall, this Special Issue aims to gather research papers and reviews of the strategies, technologies, and challenges associated with optimizing power quality in smart grid systems. It brings together the latest research and practical insights from experts in the field, offering valuable guidance for utilities, researchers, and policymakers. By optimizing power quality, smart grid systems can ensure reliable, efficient, and sustainable power delivery, meeting the evolving needs of the modern electricity grid.

Prof. Dr. Yanhong Luo
Dr. Bowen Zhou
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

  • power quality
  • smart grid
  • distributed power generation
  • energy management
  • renewable energy
  • optimal control
  • low voltage
  • intelligent diagnosis

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Published Papers (3 papers)

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Research

19 pages, 6817 KiB  
Article
Identification and Evaluation of Vulnerable Links in a Distribution Network with Renewable Energy Source Based on Minimum Discriminant Information
by Kejian Shi, Ting Wang, Zikuo Dai, Ye Tian, Pu Yang and Haifeng Li
Energies 2024, 17(17), 4495; https://doi.org/10.3390/en17174495 - 7 Sep 2024
Viewed by 843
Abstract
With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in [...] Read more.
With the increase in the proportion of photovoltaic and wind power access, the scale and form of distribution networks are becoming more and more complex. The traditional single distribution network vulnerability assessment method is difficult to use to identify the vulnerable links in the distribution network. Therefore, this paper proposes a method for identifying and evaluating vulnerable links in distribution networks based on minimum discriminant information. First, considering the influence of distributed grid connection, an improved probabilistic power flow calculation method is proposed, which improves the calculation efficiency and accuracy. Second, considering the correlation degree, transmission capacity, and voltage stability of branches in the distribution network, the identification index of vulnerable lines is defined. Based on power quality and operating state, the identification index of vulnerable nodes in a distribution network is defined. Finally, based on the indicators of vulnerable nodes and vulnerable lines, the vulnerable links in the distribution network are comprehensively evaluated based on the principle of minimum discriminant information, and the vulnerable links of the entire distribution network are evaluated according to different degrees of vulnerability. The rationality and effectiveness of the proposed method are verified via an example analysis of actual power grid data. Full article
(This article belongs to the Special Issue Optimizing Power Quality in Smart Grid Systems)
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15 pages, 5098 KiB  
Article
Distribution System State Estimation Based on Power Flow-Guided GraphSAGE
by Baitong Zhai, Dongsheng Yang, Bowen Zhou and Guangdi Li
Energies 2024, 17(17), 4317; https://doi.org/10.3390/en17174317 - 28 Aug 2024
Viewed by 715
Abstract
Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper [...] Read more.
Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation. Full article
(This article belongs to the Special Issue Optimizing Power Quality in Smart Grid Systems)
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17 pages, 7161 KiB  
Article
An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting
by Hengyu Liu, Zuoxia Xing, Qingqi Zhao, Yang Liu and Pengfei Zhang
Energies 2024, 17(17), 4284; https://doi.org/10.3390/en17174284 - 27 Aug 2024
Viewed by 734
Abstract
Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and [...] Read more.
Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness. Full article
(This article belongs to the Special Issue Optimizing Power Quality in Smart Grid Systems)
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