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Processes
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New Challenges and Solutions to Improve Energy and Computational Efficiency...
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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: 15 May 2024 | Viewed by 2139

Special Issue Editors

Dr. Qianzhi Zhang

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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 (3 papers)

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Research

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 423
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)
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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 - 07 Mar 2024
Viewed by 551
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)
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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 746
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)
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