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Special Issue "Data-Intensive Computing in Smart Microgrids: Volume II"

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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: 30 September 2023 | Viewed by 3134

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Special Issue Editors

Dr. Herodotos Herodotou

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Guest Editor

Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
Interests: smart grids; data analytics; data-intensive computing; data processing systems
Special Issues, Collections and Topics in MDPI journals

Dr. Sheraz Aslam

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Guest Editor

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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microgrids have recently emerged as the building blocks of smart grids, combining distributed renewable energy sources, energy storage devices, and load management to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, and consumption data). The application of big data analysis techniques (e.g., forecasting, classification, and clustering) to such data can optimize power generation and operation in real-time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. The efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use.

Furthermore, 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 several emerging technologies (big data analytics, blockchain, deep learning, edge computing, etc.). In this context, studies are needed to address current challenges related to efficient decision support for all producers, operators, customers, and regulators in smart microgrids to achieve holistic smart energy management, including energy generation, transmission, distribution, environmental friendliness, sustainable generation, and demand-side management. We are inviting submissions of relevant original research articles or comprehensive reviews to this Special Issue of Energies on “Data-Intensive Computing in Smart Microgrids: Volume II”. The topics of the Special Issue include, but are not limited to, the following:

Energy-data-intensive analytics in smart microgrids;
Data-driven management and control of smart microgrids;
Big data management in smart grids and microgrids;
Applications of network science in the modeling and analysis of smart microgrids;
Data-driven dynamic pricing mechanisms and strategies in smart grids;
Intelligent decision-making in smart microgrids;
Demand-side management in smart microgrids;
Energy load/demand forecasting for residential, commercial, and/or industrial consumers;
Electricity price forecasting for residential, commercial, and/or industrial consumers;
Power forecasting from renewable energy resources (e.g., solar, wind);
Management of data for advanced metering infrastructure (AMI) in smart grids;
Data-driven renewable energy integration in smart grids;
Smart grid data visualization;
Renewable energy, battery storage systems, electric vehicles;
Power economics;
Electricity theft detection;
Prediction and classification for smart grid applications;
Data security and privacy for smart grid applications;
Energy policies for power generation/transmission/consumption;
Machine/deep learning applications for smart grids/microgrids;
Cloud/fog/edge computing applications for smart grids/microgrids;
Blockchain applications for smart grids/microgrids.

Dr. Herodotos Herodotou
Dr. Sheraz Aslam
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

smart grids/microgrids
renewable energy
energy analytics
data-driven management
electricity theft detection
energy load/demand/generation forecasting
advanced metering infrastructure
blockchain in smart grids/microgrids

Published Papers (3 papers)

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Editorial

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Open AccessEditorial

Data-Intensive Computing in Smart Microgrids: Volume II

Herodotos Herodotou

and

Sheraz Aslam

Energies 2022, 15(16), 5833; https://doi.org/10.3390/en15165833 - 11 Aug 2022

Viewed by 684

Abstract

Power grids play an important role in modern societies by providing an uninterrupted energy supply and have become a key driving force behind the growth of the world’s economies [...] Full article

(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids: Volume II)

Research

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Open AccessArticle

Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation

Muhammad Asjad Salahuddin

Muhammad Farooq Umer

Mujtaba Humayon

and

Haris Shaheer

Energies 2023, 16(14), 5498; https://doi.org/10.3390/en16145498 - 20 Jul 2023

Viewed by 447

Abstract

With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency. Full article

(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids: Volume II)

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Open AccessArticle

Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer

Tayyab Ali

Suheel Abdullah Malik

and

Energies 2023, 16(5), 2086; https://doi.org/10.3390/en16052086 - 21 Feb 2023

Cited by 4 | Viewed by 1462

Abstract

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of Â ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS. Full article

(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids: Volume II)

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