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AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems

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

Deadline for manuscript submissions: 28 November 2025 | Viewed by 201

Special Issue Editors


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Guest Editor
Department of Electrical Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa
Interests: power system operation and control; renewable energy integration; microgrids; deregulation power system; optimization techniques; application of artificial intelligence; electric vehicles & blockchain technology

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Guest Editor
Department of Electrical Engineering Technology, University of Johannesburg, Johannesburg 2006, South Africa
Interests: microgrid systems; cyber-physical power system; the discrete element method (DEM); granular materials; photovoltaics and electrification in agriculture

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Guest Editor
Department of Human Anatomy and Physiology, Faculty of Health Sciences, University of Johannesburg, Johannesburg 2094, South Africa
Interests: power system; artificial intelligence; machine intelligence; energy

Special Issue Information

Dear Colleagues,

This proposed Special Issue, titled “AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems”, will focus on the latest advancements and innovations in power grid technologies, emphasizing the integration of renewable energy systems with cutting-edge AI applications. The next-generation smart grids envisions the seamless integration of a wide array of renewable energy sources (RESs)—such as solar photovoltaics (PV), wind turbines, geothermal, tidal, and wave energy—each with its own intermittency and distinct generational characteristics.

The power grid is increasingly shaped by fluctuating energy demands and the incorporation of energy storage technologies, including batteries, supercapacitors, electrolyzers, and electric vehicles (EVs). These additions are transforming traditional grids into dynamic, active distribution networks. Effectively managing such complex systems requires intelligent monitoring, real-time data exchange, and advanced control mechanisms—key components for both existing and future power systems.

This transformation highlights the urgent need to address cybersecurity and sustainability challenges. Through the use of AI-enabled forecasting, adaptive energy storage strategies, and secure communication protocols, artificial intelligence plays a crucial role in developing resilient, intelligent, and environmentally sustainable power infrastructures. This Special Issue will spotlight the central role of AI in securing power grids, enhancing operational efficiency, and advancing the goals of sustainable computing.

Dr. Gulshan Sharma
Dr. Pitshou N. Bokoro
Prof. Dr. Rajesh Kumar
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.

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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

  • load frequency control
  • solar PV
  • wind power
  • distribution systems
  • microgrids
  • electric vehicles
  • congestion managment
  • smartgrids

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Published Papers (1 paper)

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Research

21 pages, 3348 KiB  
Article
An Intelligent Technique for Coordination and Control of PV Energy and Voltage-Regulating Devices in Distribution Networks Under Uncertainties
by Tolulope David Makanju, Ali N. Hasan, Oluwole John Famoriji and Thokozani Shongwe
Energies 2025, 18(13), 3481; https://doi.org/10.3390/en18133481 - 1 Jul 2025
Abstract
The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of [...] Read more.
The proactive involvement of photovoltaic (PV) smart inverters (PVSIs) in grid management facilitates voltage regulation and enhances the integration of distributed energy resources (DERs) within distribution networks. However, to fully exploit the capabilities of PVSIs, it is essential to achieve optimal control of their operations and effective coordination with voltage-regulating devices in the distribution network. This study developed a dual strategy approach to forecast the optimal setpoints of onload tap changers (OLTCs), PVSIs, and distribution static synchronous compensators (DSTATCOMs) to improve the voltage profiles in power distribution systems. The study began by running a centralized AC optimal power flow (CACOPF) and using the hourly PV output power and the load demand to determine the optimal active and reactive power of the PVSIs, the setpoint of the DSTATCOM, and the optimal tap setting of the OLTC. Furthermore, Machine Learning (ML) models were trained as controllers to determine the reactive-power setpoints for the PVSIs and DSTATCOMs as well as the optimal OLTC tap position required for voltage stability in the network. To assess the effectiveness of the method, comprehensive evaluations were carried out on a modified IEEE 33 bus with a high penetration of PV energy. The results showed that deep neural networks (DNNs) outperformed other ML models used to mimic the coordination method based on CACOPF. Furthermore, when the DNN-based controller was tested and compared with the optimizer approach under different loading and PV conditions, the DNN-based controller was found to outperform the optimizer in terms of computational time. This approach allows predictive control in power systems, helping system operators determine the action to be initiated under uncertain PV energy and loading conditions. The approach also addresses the computational inefficiency arising from contingencies in the power system that may occur when optimal power flow (OPF) is run multiple times. Full article
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