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Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and...
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Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and Control

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 (31 January 2025) | Viewed by 5551

Special Issue Editor

Prof. Dr. Hossam A. Gaber

E-Mail Website
Guest Editor
Department of Energy and Nuclear Engineering, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1G 0C5, Canada
Interests: esilient smart energy grid and micro-energy grid planning, control, and protection; advanced plasma generation and applications in fusion energy; advanced safety and control systems for nuclear power plants; safety engineering, fault diagnosis, and real-time simulation; risk-based energy conservation; smart green buildings; process systems engineering of the energy and nuclear facilities and oil and gas production plants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

This Special Issue will present the latest research methods, system developments, and technologies relating to intelligent energy management systems and their implementations within smart grids and community applications. Topics of interest include, but are not limited to, the following:

  • Applied AI techniques for smart energy systems;
  • Hybrid energy systems’ design, modelling, simulation, control, integration, planning, and management;
  • Applied AI for energy policies;
  • Hydrogen process technologies and infrastructure;
  • Carbon capturing and storage technologies;
  • Applied quantum AI and quantum energy;
  • Intelligent energy management systems;
  • Smart energy–water systems;
  • Smart energy for clean transportation;
  • Smart waste-to-energy process technologies;
  • Interconnected infrastructure;
  • Smart electronics.

Contributions from researchers, students, and professionals are welcomed to facilitate the discussion on state-of-the-art research and developments in these areas and to reflect potential implementations and projects in urban, remote, and waterfront communities, as well as industrial applications.

Prof. Dr. Hossam A. Gaber
Guest Editor

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 energy
  • energy management system
  • applied AI
  • hybrid energy systems
  • quantum energy
  • smart grid
  • hydrogen process technologies
  • energy–water
  • clean transportation

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

Published Papers (4 papers)

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Research

11 pages, 3560 KiB  
Article
Nonlinear Self-Synchronizing Current Control for Single-Phase AC Inverters
by Shruti Pandey and Michael Mclntyre
Energies 2025, 18(4), 941; https://doi.org/10.3390/en18040941 - 16 Feb 2025
Viewed by 327
Abstract
Grid-connected single-phase inverters require accurate phase detection for synchronization and power control. Traditionally, phase-locked loops (PLLs) are used to estimate grid parameters. This paper proposes a novel approach that determines the grid phase angle using only current feedback, eliminating the need for grid [...] Read more.
Grid-connected single-phase inverters require accurate phase detection for synchronization and power control. Traditionally, phase-locked loops (PLLs) are used to estimate grid parameters. This paper proposes a novel approach that determines the grid phase angle using only current feedback, eliminating the need for grid voltage measurements or cascaded control schemes. The proposed method integrates a phase angle observer with a current controller to regulate real and reactive power. Lyapunov stability analysis and hardware experiments validate the effectiveness of the approach. Full article
(This article belongs to the Special Issue Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and Control)
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22 pages, 8900 KiB  
Article
Technology Selection of High-Voltage Offshore Substations Based on Artificial Intelligence
by Tiago A. Antunes, Rui Castro, Paulo J. Santos and Armando J. Pires
Energies 2024, 17(17), 4278; https://doi.org/10.3390/en17174278 - 27 Aug 2024
Viewed by 1128
Abstract
This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory [...] Read more.
This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency. Full article
(This article belongs to the Special Issue Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and Control)
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32 pages, 11896 KiB  
Article
Dual Active Bridge Converter with Interleaved and Parallel Operation for Electric Vehicle Charging
by Burak Muhammetoglu and Mohsin Jamil
Energies 2024, 17(17), 4258; https://doi.org/10.3390/en17174258 - 26 Aug 2024
Cited by 3 | Viewed by 2423
Abstract
This paper presents the design and optimization of a bidirectional Dual Active Bridge (DAB) converter for electric vehicle battery charging applications, encompassing both heavy and light electric vehicles. The core of this study is a 5.6 kW DAB converter that can seamlessly transition [...] Read more.
This paper presents the design and optimization of a bidirectional Dual Active Bridge (DAB) converter for electric vehicle battery charging applications, encompassing both heavy and light electric vehicles. The core of this study is a 5.6 kW DAB converter that can seamlessly transition between 3.7 kW and 11.2 kW power outputs to accommodate different vehicle requirements without the need for circuit component changes. This flexibility is achieved through the novel integration of interleaved and parallel operation capabilities, allowing for efficient operation across a broad power range. Key innovations include the design of a high-frequency transformer with dual secondary outputs to facilitate power transfer at high currents up to 30 A, an optimized thermal design, and minimized stress on the circuit board. The use of next-generation power semiconductors and low-loss magnetic circuit elements has resulted in an optimized single-stage bidirectional converter design that showcases enhanced efficiency and competitiveness in the field. Furthermore, the converter’s design enables easy reconfiguration to meet the desired power output, vehicle type, and application needs, making it adaptable for future applications such as Vehicle-to-Grid (V2G) systems. The combination of these features—versatility in power output, efficient high-current transfer, innovative use of power semiconductors, and adaptability for future technologies—positions this DAB converter as a significant advancement in electric vehicle charging technology, offering a scalable solution to meet the evolving demands of electric mobility and renewable energy integration. Full article
(This article belongs to the Special Issue Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and Control)
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18 pages, 3668 KiB  
Article
Design of a High-Precision Self-Balancing Potential Transformer Calibrator
by Mengjia Li, Feng Zhou, Jiandong Jiang, Hao Liu, Bo Xiong, Xue Wang and Teng Yao
Energies 2024, 17(17), 4230; https://doi.org/10.3390/en17174230 - 24 Aug 2024
Viewed by 883
Abstract
Potential transformers are vital for measuring and protecting the power grid. Their accuracy and reliability directly impact the stability and security of the power system. To address the issues with traditional high-precision potential transformer calibrators, such as cumbersome operation and low efficiency, a [...] Read more.
Potential transformers are vital for measuring and protecting the power grid. Their accuracy and reliability directly impact the stability and security of the power system. To address the issues with traditional high-precision potential transformer calibrators, such as cumbersome operation and low efficiency, a high-precision potential transformer calibrator has been developed. The calibrator is based on an embedded system architecture of FPGA and ARM. It uses a high-precision current comparator along with feedback control technology. By monitoring and adjusting the error feedback voltage, it can perform the automated calibration of potential transformers with an accuracy class of 0.0001. The measurement ranges from 0.00001% to 200.0%. This study can be adapted to meet the development needs of modern digital measurement systems. Full article
(This article belongs to the Special Issue Intelligent Energy Management Systems for Smart Grids: Algorithms, Optimization, and Control)
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