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
New Challenges and Solutions to Improve Energy and Computational Efficiency...
share announcement

New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 5031

Special Issue Editors

Dr. Qianzhi Zhang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
Interests: smart grid; advanced optimization and artificial intelligence in power systems; transportation electrification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yujian Ye

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: energy system economics; transportation electrification; artificial intelligence in power systems
Special Issues, Collections and Topics in MDPI journals
Dr. Chong Wang

E-Mail Website
Guest Editor
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Interests: power system resilience; uncertainty analysis and control of power system; integrated energy power system modeling and optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dan Wu

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: power system dynamics and optimization, natural gas systems, advanced mathematical tools in energy system analysis
Dr. Chunyu Chen

E-Mail Website
Guest Editor
School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
Interests: power system operation and contol
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart grids represent a critical component of our modern power systems, integrating advanced technologies to optimize power generation, transmission and consumption. However, as smart grids continue to transform the power and energy landscape, energy and computational efficiency becomes an ongoing challenge. With higher integration of renewable energy sources and end-use electrifications, managing their intermittent nature becomes a challenge. Smart grids are also vulnerable to extreme events and cyberattacks, which can disrupt energy supply and compromise data integrity. In addition, smart grids generate vast amounts of data from various sensors and devices, and the efficiency of data collection, storage, processing, and analysis is essential for optimizing grid operation. Therefore, innovative solutions are required to address the above complexities for improving energy and computational efficiency in smart grids. 

This Special Issue on ‘New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids’ calls for state-of-the-art works on this promising research area, which aims to explore the latest challenges, innovations, and solutions in the quest to enhance both energy and computational efficiency within smart grids. This Special Issue invites researchers, engineers, and industrial practitioners to submit original research and review articles that shed light on, but are not limited to, the following topics:

  • Innovations in grid control and smart grid technology, including model-based optimization and model-free learning-based algorithm to improve decision making and computational efficiency;
  • Novel approaches for improving data collection and communication with advanced metering infrastructure to enhance energy monitoring and management;
  • Strategies for integrating distributed energy resources to optimize energy efficiency and grid stability, including renewable energy resources, inverter-based resources, energy storage systems, microgrids, and demand-side management;
  • Assessments of the impact of end-use electrification on energy efficiency in smart grids and proposals for improvements, including transportation and building electrifications;
  • Methods for enhancing resilience of smart grid systems against extreme events while ensuring energy efficiency;
  • Methods for enhancing resilience of smart grid systems against cyber threats while ensuring computational efficiency.

Dr. Qianzhi Zhang
Prof. Dr. Yujian Ye
Dr. Chong Wang
Prof. Dr. Dan Wu
Dr. Chunyu Chen
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

  • smart grid technology
  • distributed energy resources
  • end-use electrification
  • intelligent decision making
  • advanced metering infrastructure
  • resilience enhancement

Published Papers (6 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

15 pages, 1644 KiB  
Article
A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks
by Jing Xu, Juan Li, Liang Zhang, Chaoming Huang, Hao Yu and Haoran Ji
Processes 2024, 12(7), 1328; https://doi.org/10.3390/pr12071328 - 26 Jun 2024
Viewed by 925
Abstract
Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge [...] Read more.
Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

15 pages, 1068 KiB  
Article
Technical Support System for High Concurrent Power Trading Platforms Based on Microservice Load Balancing
by Ping Shao, Longda Huang, Liguo Weng and Ziheng Liu
Processes 2024, 12(6), 1270; https://doi.org/10.3390/pr12061270 - 20 Jun 2024
Viewed by 433
Abstract
With the booming development of the electricity market, market factors such as electricity trading varieties are growing rapidly. The frequency of transactions has become increasingly real-time, and transaction clearing and settlement tasks have become more complex. The increasing demands for concurrent access and [...] Read more.
With the booming development of the electricity market, market factors such as electricity trading varieties are growing rapidly. The frequency of transactions has become increasingly real-time, and transaction clearing and settlement tasks have become more complex. The increasing demands for concurrent access and carrying capacity in trading systems have made it increasingly difficult for existing systems to support business. This article proposes a transaction support system for large-scale electricity trading market entities, which solves the problems of high concurrency access and massive access data calculation while ensuring system security through business isolation measures. The system uses microservices to treat various functional modules as independent service modules, thus making service segmentation and composition more flexible. By using read–write separation, caching mechanisms, and several data reliability assurance measures, data can be stored and accessed quickly and securely. The use of a three-layer load balancing module consisting of an OpenResty access entry layer, a gateway routing gateway layer, and a WebClient service inter-resource invocation layer can effectively improve the system’s ability to handle concurrent access. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

19 pages, 2610 KiB  
Article
Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy
by Xiaodong Yuan, Xize Jiao, Mingshen Wang, Huachun Han, Shukang Lv and Fei Zeng
Processes 2024, 12(6), 1230; https://doi.org/10.3390/pr12061230 - 15 Jun 2024
Viewed by 469
Abstract
In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in [...] Read more.
In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in the distribution network, their aggregation operators will play a more prominent role in pricing management and charging behavior, and setting an appropriate charging price can achieve a win–win situation for operators and electric vehicle users. At the same time, the proportion of scenery in the distribution network is relatively high, and the uncertainty of self-output has a certain impact on the pricing strategy of operators and the charging behavior of electric vehicle users, which has become an important research topic. Based on the above background, an EV operator pricing strategy considering the landscape uncertainty is proposed, a Stackelberg game model is established to maximize the respective benefits of operators and EV users, and the two-layer model is further transformed into a single-layer model through the Karush–Kuhn–Tucker (KKT) condition and duality theorem. Finally, the IEEE 33 system is simulated with the CPLEX solver, and the global optimal pricing strategy is obtained. Simulation results prove that electric vehicle operators experience a maximum profit increase of 2.6% due to the impact of maximum capacity of energy storage equipment and the uncertainty of renewable energy output can result in electric vehicle operators losing approximately 20% of their profits at most. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

18 pages, 3805 KiB  
Article
Anomaly Identification for Photovoltaic Power Stations Using a Dual Classification System and Gramian Angular Field Visualization
by Zihan Wang, Qiushi Cui, Zhuowei Gong, Lixian Shi, Jie Gao and Jiayong Zhong
Processes 2024, 12(4), 690; https://doi.org/10.3390/pr12040690 - 29 Mar 2024
Viewed by 700
Abstract
With the increasing scale of photovoltaic (PV) power stations, timely anomaly detection through analyzing the PV output power curve is crucial. However, overlooking the impact of external factors on the expected power output would lead to inaccurate identification of PV station anomalies. This [...] Read more.
With the increasing scale of photovoltaic (PV) power stations, timely anomaly detection through analyzing the PV output power curve is crucial. However, overlooking the impact of external factors on the expected power output would lead to inaccurate identification of PV station anomalies. This study focuses on the discrepancy between measured and expected PV power generation values, using a dual classification system. The system leverages two-dimensional Gramian angular field (GAF) data and curve features extracted from one-dimensional time series, along with attention weights from a CNN network. This approach effectively classifies anomalies, including normal operation, aging pollution, and arc faults, achieving an overall classification accuracy of 95.83%. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

22 pages, 2689 KiB  
Article
A Distributionally Robust Optimization Strategy for a Wind–Photovoltaic Thermal Storage Power System Considering Deep Peak Load Balancing of Thermal Power Units
by Zhifan Zhang and Ruijin Zhu
Processes 2024, 12(3), 534; https://doi.org/10.3390/pr12030534 - 7 Mar 2024
Cited by 1 | Viewed by 828
Abstract
With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces [...] Read more.
With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system’s peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system’s peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

16 pages, 5090 KiB  
Article
Line–Household Relationship Identification Method for a Low-Voltage Distribution Network Based on Voltage Clustering and Electricity Consumption Characteristics
by Lei Yao, Jincheng Huang and Wei Zhang
Processes 2024, 12(2), 288; https://doi.org/10.3390/pr12020288 - 28 Jan 2024
Viewed by 942
Abstract
To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line–household relationship identification (LHRI), a method for identifying line–household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper [...] Read more.
To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line–household relationship identification (LHRI), a method for identifying line–household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption characteristic vector of user categories, thereby revealing the line–household relationship for all users. On the test dataset, the LHRI algorithm proposed in this article achieved 100% accuracy, within an allowable error range of 0.2%, and improved the accuracy by 20% compared to the traditional identification method. Finally, the LVDN simulation model established by OpenDSS 9.4.0.3 was used to verify the effectiveness of the proposed method, confirming its potential and advantages in practical applications. Full article
(This article belongs to the Special Issue New Challenges and Solutions to Improve Energy and Computational Efficiency in Smart Grids)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop