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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: closed (28 November 2025) | Viewed by 7191

Special Issue Editors

Dr. Gulshan Sharma

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Dr. Pitshou N. Bokoro

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rajesh Kumar

E-Mail Website
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 Issues, Collections and Topics in MDPI journals

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 250 words) can be sent to the Editorial Office for assessment.

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

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

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Related Special Issue

Published Papers (7 papers)

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Research

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29 pages, 1699 KB  
Article
Multi-Agent-Based Coordinated Voltage Regulation Technique in an Unbalanced Distribution System
by Swathi Tangi, Dattatraya N. Gaonkar, Ramakrishna S S Nuvvula, Ahmed Ali and Syed Riyaz Ahammed
Energies 2025, 18(21), 5829; https://doi.org/10.3390/en18215829 - 5 Nov 2025
Viewed by 381
Abstract
Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve [...] Read more.
Unbalanced active distribution networks must be carefully analyzed to minimize undesirable implications from internal unbalances and the incorporation of intermittent sources, such as DG (Distributed Generation). A coordinated voltage regulation mechanism is being created employing a MAS (Multi-Agent System) control structure to solve the difficulties mentioned earlier. The proposed technique increases coordination between DGs and Shunt capacitors (SCs) to optimize the voltage profile and reduce overall power losses, along with voltage and current unbalanced factors in the proposed unbalanced 3-phase radial distribution network. To ensure improved real-time monitoring, PMUs (Phasor Measurement Units) measure the state parameters of the above-regulated distribution network in realtime. Because it does not necessitate the placement of PMUs at all nodes for total network observability, it is a cost-effective technique for estimating network state. The IEEE standard, a 25-bus unbalanced 3-phase distribution network feeder, is used to assess the viability of the recommended technique. MATLAB R2024a programming is used to simulate the case studies. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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25 pages, 1868 KB  
Article
AI-Powered Digital Twin Co-Simulation Framework for Climate-Adaptive Renewable Energy Grids
by Kwabena Addo, Musasa Kabeya and Evans Eshiemogie Ojo
Energies 2025, 18(21), 5593; https://doi.org/10.3390/en18215593 - 24 Oct 2025
Viewed by 1264
Abstract
Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive [...] Read more.
Climate change is accelerating the frequency and intensity of extreme weather events, posing a critical threat to the stability, efficiency, and resilience of modern renewable energy grids. In this study, we propose a modular, AI-integrated digital twin co-simulation framework that enables climate adaptive control of distributed energy resources (DERs) and storage assets in distribution networks. The framework leverages deep reinforcement learning (DDPG) agents trained within a high-fidelity co-simulation environment that couples physical grid dynamics, weather disturbances, and cyber-physical control loops using HELICS middleware. Through real-time coordination of photovoltaic systems, wind turbines, battery storage, and demand side flexibility, the trained agent autonomously learns to minimize power losses, voltage violations, and load shedding under stochastic climate perturbations. Simulation results on the IEEE 33-bus radial test system augmented with ERA5 climate reanalysis data demonstrate improvements in voltage regulation, energy efficiency, and resilience metrics. The framework also exhibits strong generalization across unseen weather scenarios and outperforms baseline rule based controls by reducing energy loss by 14.6% and improving recovery time by 19.5%. These findings position AI-integrated digital twins as a promising paradigm for future-proof, climate-resilient smart grids. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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31 pages, 1516 KB  
Article
Federated Quantum Machine Learning for Distributed Cybersecurity in Multi-Agent Energy Systems
by Kwabena Addo, Musasa Kabeya and Evans Eshiemogie Ojo
Energies 2025, 18(20), 5418; https://doi.org/10.3390/en18205418 - 14 Oct 2025
Cited by 2 | Viewed by 804
Abstract
The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. [...] Read more.
The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy infrastructures. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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34 pages, 509 KB  
Article
Energy Transformation of Road Transport Infrastructure—Concept and Assessment of the Electric Vehicle Recharging Systems
by Norbert Chamier-Gliszczynski, Joanna Alicja Dyczkowska, Wojciech Musiał, Aleksandra Panek and Piotr Kotylak
Energies 2025, 18(16), 4241; https://doi.org/10.3390/en18164241 - 9 Aug 2025
Cited by 3 | Viewed by 1021
Abstract
The energy transformation of transport infrastructure represents a significant challenge, being implemented along the TEN-T network under the introduced AFIR regulation (Regulation for the Deployment of Alternative Fuels Infrastructure). The goal of this transformation is the development of alternative fuels infrastructure deployed along [...] Read more.
The energy transformation of transport infrastructure represents a significant challenge, being implemented along the TEN-T network under the introduced AFIR regulation (Regulation for the Deployment of Alternative Fuels Infrastructure). The goal of this transformation is the development of alternative fuels infrastructure deployed along the Trans-European Transport Network (TEN-T), dedicated to light-duty electric vehicles (eLDVs) and heavy-duty electric vehicles (eHDVs). The measures undertaken must be preceded by an analytical process assessing the assumptions outlined in the AFIR regulation, defining targeted actions for achieving the regulation’s objectives, and evaluating the baseline status as well as projected conditions for the years 2025, 2027, 2030, and 2035. This assessment is essential during the planning and management stages of the energy transformation process of transport infrastructure being undertaken by individual EU Member States. Meeting the targets set by AFIR for transport infrastructure necessitates the development of appropriate research tools. The approach proposed in this article offers an innovative framework for addressing the challenges of energy transformation. The initial step involves designing a model for the energy transformation of transport infrastructure, followed by the definition of indicators to assess the implementation of AFIR objectives. This paper presents a model for the energy transformation of road transport infrastructure, defines the individual elements of the model, specifies indicators for evaluating the transformation process, and conducts a research study incorporating these components. This article aims to elucidate the core aspects of the energy transformation of transport infrastructure, identify actions aligned with achieving the objectives of the AFIR regulation, and perform an evaluation of its implementation. Additionally, the research addresses the question of how the energy transformation of road transport infrastructure is unfolding in Poland. The study is based on the structure of electric vehicles (EVs) and transport infrastructure along the TEN-T network in the territory of Poland. The current level of AFIR compliance for eLDVs for the years 2025, 2027, 2030, and 2035 is approximately 175%, 96%, 37%, and 13%, respectively. In contrast, for eHDVs, the compliance level is around 20%, 0%, and 0% for the TEN-T core network, and approximately 10%, 3%, and 0% for the TEN-T comprehensive network. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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21 pages, 3348 KB  
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
Cited by 2 | Viewed by 804
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
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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Review

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33 pages, 3063 KB  
Review
Multi-Objective Optimization of Load Flow in Power Systems: An Overview
by Bansendeka Theo Nyingu, Lebogang Masike and Mwana Wa Kalaga Mbukani
Energies 2025, 18(22), 6056; https://doi.org/10.3390/en18226056 - 20 Nov 2025
Viewed by 769
Abstract
The expanding complexity of power systems—driven by the motivation to reduce their carbon footprint by integrating renewable energy sources (RESs) in the grid, the increasing energy demand, grid scalability, and the necessity for reliable and sustainable operation—has made the optimal power flow (OPF) [...] Read more.
The expanding complexity of power systems—driven by the motivation to reduce their carbon footprint by integrating renewable energy sources (RESs) in the grid, the increasing energy demand, grid scalability, and the necessity for reliable and sustainable operation—has made the optimal power flow (OPF) problem the main issue in power systems. Hence, the concept of muti-objective optimal power flow (MOOPF) in power systems has become a crucial tool for power system management and planning. This article provides an overview of recent optimization techniques in power systems that have MOOPF as their central problem, as well as their applications in power systems, with the purpose of identifying significant approaches, challenges and trends when it comes to large-scale probabilistic MOOPF. This overview was developed based on an in-depth analysis of MOOPF techniques, the classification of their applications, and the formulation of the problem in power systems. This overview contributes to the existing literature by highlighting the evolution of optimization techniques, and the need for robust, probabilistic hybrid optimization techniques that can address variability, uncertainty, reliability, and sustainability in power systems. These findings are significant because they emphasize the current transition towards more adaptive and intelligent optimization strategies, which are essential to developing sustainable, dependable, and effective power systems, especially as we move towards smart grids and low-carbon energy systems. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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54 pages, 5812 KB  
Review
Advancing Renewable-Dominant Power Systems Through Internet of Things and Artificial Intelligence: A Comprehensive Review
by Temitope Adefarati, Gulshan Sharma, Pitshou N. Bokoro and Rajesh Kumar
Energies 2025, 18(19), 5243; https://doi.org/10.3390/en18195243 - 2 Oct 2025
Viewed by 1283
Abstract
The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution [...] Read more.
The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article
(This article belongs to the Special Issue AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems)
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