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

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 7827

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Dr. Sheraz Aslam

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Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
Interests: smart grids; data analytics; sustainable energy generations; intelligent transportation systems; sea transportation
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Special Issue Information

Dear Colleagues,

More than 80% of the world's energy needs are met with fossil fuels at the cost of high CO₂ emissions that accelerate global warming. Since the beginning of this millennium, the warmest years and most abrupt temperature changes have been recorded. Human progress and prosperity are linked to energy consumption, but survival is a challenge in the face of increasing global warming. Technological advances have influenced energy consumption patterns and behaviors at the individual and group levels. Smart energy generation, transmission, and consumption, as well as smart lifestyles, are helping to optimize energy use while reducing environmental damage and costs. Smart systems make effective decisions by performing analyses based on big data. In this context, studies are needed to address data analytics for smart energy generation, transmission, and consumption considering cost efficiency, environmental friendliness, availability, and sustainability from local, sectoral, regional, and global perspectives. This Special Issue focuses on the following topics: Smart grid data analytics for power generation; Smart grid data analytics for power transmission; Smart grid data analytics for power consumption; Smart grid data analytics for electricity theft detection; Smart grid data analytics for power-sharing; Smart grid data analytics for demand-side management; Smart grid data analytics for supply-side management; Smart grid data analytics for smart metering data;

Smart grid management;
Smart grid data visualization;
Renewable energy, battery storage system, electric vehicle;
Power economics;
Prediction and classification for smart grid applications;
Data security and privacy for smart grid applications;
Energy policies for power generation/transmission/consumption;
Integration of a variety of power sources;
Machine/deep learning applications for the smart grid;
Cloud/fog/edge computing applications in smart grids;
Blockchain applications in smart grids.

Dr. Sheraz Aslam
Guest Editor

Manuscript Submission Information

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Keywords

smart grids
energy big data analytics
smart grid management
smart grid data visualization
power economics

Published Papers (4 papers)

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Research

24 pages, 2436 KiB

Open AccessArticle

Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques

by Ru-Guan Wang, Wen-Jen Ho, Kuei-Chun Chiang, Yung-Chieh Hung, Jen-Kuo Tai, Jia-Cheng Tan, Mei-Ling Chuang, Chi-Yun Ke, Yi-Fan Chien, An-Ping Jeng and Chien-Cheng Chou

Energies 2023, 16(19), 6893; https://0-doi-org.brum.beds.ac.uk/10.3390/en16196893 - 29 Sep 2023

Viewed by 1126

Abstract

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. Full article

(This article belongs to the Special Issue Energy Big Data Analytics for Smart Grid Applications)

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16 pages, 4225 KiB

Open AccessArticle

Evaluation of Operation State of Power Grid Based on Random Matrix Theory and Qualitative Trend Analysis

by Jie Yang, Weiqing Sun and Meiling Ma

Energies 2023, 16(6), 2855; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062855 - 20 Mar 2023

Cited by 4 | Viewed by 1267

Abstract

Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus system. Full article

(This article belongs to the Special Issue Energy Big Data Analytics for Smart Grid Applications)

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34 pages, 7698 KiB

Open AccessArticle

Dandelion Optimizer-Based Combined Automatic Voltage Regulation and Load Frequency Control in a Multi-Area, Multi-Source Interconnected Power System with Nonlinearities

by Tayyab Ali, Suheel Abdullah Malik, Amil Daraz, Sheraz Aslam and Tamim Alkhalifah

Energies 2022, 15(22), 8499; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228499 - 14 Nov 2022

Cited by 19 | Viewed by 1955

Abstract

Frequency, voltage, and power flow between different control zones in an interconnected power system are used to determine the standard quality of power. Therefore, the voltage and frequency control in an IPS is of vital importance to maintaining real and reactive power balance under varying load conditions. In this paper, a dandelion optimizer (DO)-based proportional-integral-proportional-derivative (PI-PD) controller is investigated for a realistic multi-area, multi-source, realistic IPS with nonlinearities. The output responses of the DO-based PI-PD were compared with the hybrid approach using artificial electric field-based fuzzy PID algorithm (HAEFA), Archimedes optimization algorithm (AOA)-based PI-PD, learning performance-based behavior optimization (LPBO)-based PI-PD and modified particle swarm optimization (MPSO)-based PI-PD control schemes in a two-area network with 10% step load perturbation (SLP). The proposed strategy was also investigated in a two- and three-area IPS in the presence of different nonlinearities and SLPs. The simulation results and the comprehensive comparison between the different control schemes clearly confirm that the proposed DO-based PI-PD is very effective for realistic, multi-area multi-source IPS with nonlinearities. Full article

(This article belongs to the Special Issue Energy Big Data Analytics for Smart Grid Applications)

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22 pages, 12398 KiB

Open AccessArticle

Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses

by Kalim Ullah, Quanyuan Jiang, Guangchao Geng, Rehan Ali Khan, Sheraz Aslam and Wahab Khan

Energies 2022, 15(9), 3274; https://0-doi-org.brum.beds.ac.uk/10.3390/en15093274 - 29 Apr 2022

Cited by 9 | Viewed by 2645

Abstract

The number of microgrids within a smart distribution grid can be raised in the future. Microgrid-based distribution network reconfiguration is analyzed in this research by taking demand response programs and power-sharing into account to optimize costs and reduce power losses. The suggested method determined the ideal distribution network configuration to fulfil the best scheduling goals. The ideal way of interconnecting switches between microgrids and the main grid was also identified. For each hour of operation, the ideal topology of microgrid-based distribution networks was determined using optimal power flow. The results were produced with and without the use of a demand response program and power-sharing in each microgrid. Different load profiles, such as residential, industrial, commercial, and academic, were taken into account and modified using appropriate demand response programs and power-sharing using the Artificial Bee Colony algorithm. Various scenarios were explored independently to suit the diverse aims considered by the distribution network operator for improved observation. The ABC optimization in this research attempted to reduce the system’s total operation costs and power losses through efficient networked microgrid reconfiguration. The results of optimal microgrid topology revealed the effects of power-sharing and demand response (TOU) programs. The results obtained in the proposed idea shows that costs were reduced by 8.3% and power losses were reduced by 4%. The IEEE 33-bus test system was used to demonstrate the effectiveness of the proposed approach. Full article

(This article belongs to the Special Issue Energy Big Data Analytics for Smart Grid Applications)

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