Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Special Issues
Advances in Electrical Systems and Power Networks
share announcement

Advances in Electrical Systems and Power Networks

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 12747

Special Issue Editors

Dr. Hongyu Wu

E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Interests: control; operation and planning of power systems; modeling; optimization and simulation of power systems; grid integration of renewable energy; demand-side management; electric vehicles to grid; electricity market analysis and risk management; home/building energy management systems; stochastic modeling and optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Liu

E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, Kansas State University, Manhattan, KS 66506, USA
Interests: smart grid; cybersecurity of smart grids; power system state estimation; data-driven cyberattacks; machine learning applications in smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current research and development activities in electrical systems and power networks are strongly driven by renewable energy and electric vehicles (EVs) due to environmental concerns, global warming, and fossil fuel shortages. However, integrating these technologies into the existing power system is a huge challenge. The high penetration level, dispersed location, and intermittent output of renewable energy challenges the traditional power system planning, design, operation, and control methods. In addition, the integration of large quantities of EVs into the distribution grid would likely challenge the grid's operation and management. It is therefore imperative to understand the fundamentals of integrating renewable energy and EVs into the power system. Furthermore, distribution system state estimation, load forecasting, and cyberattack detectors are effective tools to ensure a reliable and secure grid operation. The objective of this Special Issue is to address the integration of renewable energy and EV into the power system under a reliable and secure grid operation.

The application topics of interest include, but are not limited to:

  • Renewable energy integration and impact;
  • Electric vehicle integration and impact;
  • Operational, planning, market, and policy issues related to distributed energy resources (DER);
  • Load forecasting and scheduling;
  • Distribution system state estimation;
  • Cyber-physical security of vehicle-to-grid systems;
  • Cyber-physical security of renewable energy systems;
  • Machine learning applications in electric power systems.

Dr. Hongyu Wu
Dr. Bo Liu
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. Processes is an international peer-reviewed open access monthly 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 2400 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
  • electric vehicle
  • load forecast and scheduling
  • state estimation
  • cybersecurity
  • machine learning applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (8 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 12460 KiB  
Article
Optimal Dispatch of the Source-Grid-Load-Storage under a High Penetration of Photovoltaic Access to the Distribution Network
by Tao Zhang, Xiaokang Zhou, Yao Gao and Ruijin Zhu
Processes 2023, 11(10), 2824; https://doi.org/10.3390/pr11102824 - 25 Sep 2023
Cited by 3 | Viewed by 1228
Abstract
In the context of carbon peaking and carbon neutralization, distributed photovoltaics is a relatively mature new energy power generation technology that is being widely promoted. However, the randomness and volatility of distributed generation bring severe challenges to the distribution network’s operation. Based on [...] Read more.
In the context of carbon peaking and carbon neutralization, distributed photovoltaics is a relatively mature new energy power generation technology that is being widely promoted. However, the randomness and volatility of distributed generation bring severe challenges to the distribution network’s operation. Based on this, taking the typical scenario of a high proportion of distributed photovoltaic grid connections against the background of a whole-county photovoltaic system as the research object, this paper constructs a source-grid-load-storage coordination optimal scheduling model in distribution networks, considering the spatial distribution of power flow, tie-line power fluctuation, grid loss, and voltage amplitude from the perspective of optimal day-to-day scheduling. Next, the Lehmer weighted and improved multi-mutation cooperation strategy differential evolution (LW-IMCSDE) algorithm is introduced to enhance the differential evolution algorithm based on the weighted Lehmer average, improved multi-mutation cooperation, and population update strategies. The feasibility and effectiveness of the algorithm are investigated by using a test function to verify its effectiveness. Finally, the feasibility and effectiveness of the proposed strategy are verified in two typical power scenarios: summer and winter. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

19 pages, 3296 KiB  
Article
Comprehensive Evaluation Index System and Application of Low-Carbon Resilience of Power Grid Containing Phase-Shifting Transformer under Ice Disaster
by Jing Zhang, Huilin Cheng, Peng Yang, Bingyan Zhang, Shiqi Zhang and Zhigang Lu
Processes 2023, 11(9), 2633; https://doi.org/10.3390/pr11092633 - 4 Sep 2023
Viewed by 1107
Abstract
In view of the high impact of extreme disasters, this paper comprehensively evaluates power grid performance from a new low-carbon toughness perspective. First, considering the increase in carbon emissions and the recovery time of carbon emissions, low-carbon resilience indicators are proposed. At the [...] Read more.
In view of the high impact of extreme disasters, this paper comprehensively evaluates power grid performance from a new low-carbon toughness perspective. First, considering the increase in carbon emissions and the recovery time of carbon emissions, low-carbon resilience indicators are proposed. At the same time, considering the power-regulation effect of the phase-shifter transformer, the fault and response model of a power grid under an ice disaster is established, and then, a comprehensive evaluation index system of low-carbon toughness of the power grid is constructed. The weight determination is carried out using the fuzzy analytic hierarchy process-entropy-based weight method, while the fuzzy comprehensive evaluation center of gravity method is used to evaluate the power grid comprehensively. Finally, examples are presented to verify the feasibility of the proposed method, emphasizing its potential for evaluating the comprehensive performance of low-carbon and toughness of the power grid in the future. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

13 pages, 2312 KiB  
Article
Fault Detection and Location of 35 kV Single-Ended Radial Distribution Network Based on Traveling Wave Detection Method
by Xiaowei Xu, Fangrong Zhou, Yongjie Nie, Wenhua Xu, Ke Wang, Jian OuYang, Kaihong Zhou, Shan Chen and Yiming Han
Processes 2023, 11(8), 2494; https://doi.org/10.3390/pr11082494 - 19 Aug 2023
Cited by 1 | Viewed by 1171
Abstract
With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting [...] Read more.
With the progress of society and the iterative improvement of infrastructure construction, the power grid transmission lines have also entered an era of intelligence. The national distribution system has made ensuring the regular operation of the distribution network as well as prompting troubleshooting and detection its top priority. Research on fault diagnosis for 35 kV single-ended radial distribution networks is still in its infancy compared to other hot topics in the industry, such as short-circuit fault detection and fault node localization. This study adopts the 35 kV single-ended radial distribution network as a model, detects fault lines via the traveling wave method, and accurately locates fault nodes using the wavelet conversion method, hoping to quickly identify and locate fault nodes in distribution networks. The experimental results demonstrate that the research method can quickly identify the faulty line and carry out further fault node location detection. The final obtained fault distance is 1.19 km with an actual error of only 0.16 km; the maximum relative errors are only 0.33 km and 0.21 km when the initial phase angle and transition resistance parameters are changed, respectively; and the error amplitude fluctuations are essentially stable. The experimental results also demonstrate that the research method can quickly identify the faulty line and carry out further fault node location. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

19 pages, 7492 KiB  
Article
Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature
by Jiangpeng Feng, Xiqiang Chang, Yanfang Fan and Weixiang Luo
Processes 2023, 11(8), 2256; https://doi.org/10.3390/pr11082256 - 26 Jul 2023
Cited by 8 | Viewed by 2682
Abstract
The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. [...] Read more.
The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

27 pages, 7658 KiB  
Article
Cluster Optimization for Integrated Energy Systems Considering Multi-Energy Sharing and Asymmetric Profit Allocation: A Case Study of China
by Shiting Cui, Peng Wang, Yao Gao and Ruijin Zhu
Processes 2023, 11(7), 2027; https://doi.org/10.3390/pr11072027 - 6 Jul 2023
Cited by 1 | Viewed by 1116
Abstract
This study proposes a novel integrated energy system (IES) cluster optimization structure that uses multi-energy sharing, multi-Nash games, and asymmetric profit allocation according to the energy supply demand and energy development planning for Tibet. First, it integrates clean energy units such as concentrated [...] Read more.
This study proposes a novel integrated energy system (IES) cluster optimization structure that uses multi-energy sharing, multi-Nash games, and asymmetric profit allocation according to the energy supply demand and energy development planning for Tibet. First, it integrates clean energy units such as concentrated solar power, power to hydrogen to power, and vacuum pressure swing adsorption to build a novel IES including electricity, heat, and oxygen. Second, multiple novel IESs are combined to form an IES cluster and the IES cluster is divided into three stages of optimization: the first stage is to achieve optimal multi-energy sharing under cluster optimization, the second stage is to conduct multi-Nash games to achieve optimal sharing cost, and the third stage is to conduct asymmetric profit allocation. Finally, the case study is conducted and the results show that the multi-Nash games and asymmetric profit allocation can effectively improve the renewable energy consumption of the IES cluster, reduce the operation cost of the cluster, and reduce the cost of multi-energy sharing compared to only considering the cluster energy supply price as the sharing price, thereby improving the economy of multi-energy sharing. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

14 pages, 3156 KiB  
Article
Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm
by Chuan Zhou, Suying Gui, Yan Liu, Junpeng Ma and Hao Wang
Processes 2023, 11(7), 1947; https://doi.org/10.3390/pr11071947 - 27 Jun 2023
Cited by 8 | Viewed by 1573
Abstract
Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. [...] Read more.
Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

21 pages, 12049 KiB  
Article
New Energy Power System Dynamic Security and Stability Region Calculation Based on AVURPSO-RLS Hybrid Algorithm
by Saniye Maihemuti, Weiqing Wang, Jiahui Wu, Haiyun Wang, Muladi Muhedaner and Qing Zhu
Processes 2023, 11(4), 1269; https://doi.org/10.3390/pr11041269 - 19 Apr 2023
Cited by 2 | Viewed by 1404
Abstract
Using a high proportion of new energy is becoming the development trend of the modern power industry, with broad application prospects and potential threats to power system operation safety. This paper proposes a hybrid adaptive velocity update relaxation particle swarm optimization algorithm (AVURPSO) [...] Read more.
Using a high proportion of new energy is becoming the development trend of the modern power industry, with broad application prospects and potential threats to power system operation safety. This paper proposes a hybrid adaptive velocity update relaxation particle swarm optimization algorithm (AVURPSO) and recursive least square (RLS) method to quickly estimate the DSSR boundary using hyper-plane expression. Firstly, the operating point data in the high-dimension nodal injection space are analyzed using the AVURPSO algorithm to identify the key generators, equivalent search space, and critical points, which have relatively great effects on transient angle stability. The hyper-plane expression of the DSSR boundary, which matches the critical points best, is finally fitted by the RLS approach. Hence, the adopted algorithm is applied to rapidly approximate the DSSR boundary by hyper-plane expression in power injection spaces. Finally, the proposed algorithm is validated using a simulation case study on three wind farm regions of the actual Hami Power Grid of China using the DIgSILENT/Power Factory software. Consequently, the mentioned method effectively captures the security stability boundary of the new energy power system and realizes the three-dimensional visualization space of DSSR. By leveraging the DSSR, the state analysis can be conducted rapidly on several parameters, including security and stability assessments in relation to various energy supply capabilities. Meanwhile, these indices are calculated offline and applied online. The findings of this investigation confirm the efficacy and accuracy of the suggested modeling used in the analyzed system, offering technical assistance ensuring the stability of the new energy power system. The DSSR allows the rapid analysis of several parameters, including security and stability assessments with various energy supply capabilities. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

20 pages, 6207 KiB  
Article
Random-Enabled Hidden Moving Target Defense against False Data Injection Alert Attackers
by Bo Liu, Hongyu Wu, Qihui Yang and Hang Zhang
Processes 2023, 11(2), 348; https://doi.org/10.3390/pr11020348 - 21 Jan 2023
Cited by 3 | Viewed by 1569
Abstract
Hidden moving target defense (HMTD) is a proactive defense strategy that is kept hidden from attackers by changing the reactance of transmission lines to thwart false data injection (FDI) attacks. However, alert attackers with strong capabilities pose additional risks to the HMTD and [...] Read more.
Hidden moving target defense (HMTD) is a proactive defense strategy that is kept hidden from attackers by changing the reactance of transmission lines to thwart false data injection (FDI) attacks. However, alert attackers with strong capabilities pose additional risks to the HMTD and thus, it is much-needed to evaluate the hiddenness of the HMTD. This paper first summarizes two existing alert attacker models, i.e., bad-data-detection-based alert attackers and data-driven alert attackers. Furthermore, this paper proposes a novel model-based alert attacker model that uses the MTD operation models to estimate the dispatched line reactance. The proposed attacker model can use the estimated line reactance to construct stealthy FDI attacks against HMTD methods that lack randomness. We propose a novel random-enabled HMTD (RHMTD) operation method, which utilizes random weights to introduce randomness and uses the derived hiddenness operation conditions as constraints. RHMTD is theoretically proven to be kept hidden from three alert attacker models. In addition, we analyze the detection effectiveness of the RHMTD against three alert attacker models. Simulation results on the IEEE 14-bus systems show that traditional HMTD methods fail to detect attacks by the model-based alert attacker, and RHMTD is kept hidden from three alert attackers and is effective in detecting attacks by three alert attackers. Full article
(This article belongs to the Special Issue Advances in Electrical Systems and Power Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop