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AI-Driven Sustainable Power Grids: Enhancing Cybersecurity, Operation, and Control of Conventional, Modern, and Renewable-Based Energy Systems—2nd Edition

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

Deadline for manuscript submissions: 25 May 2026 | Viewed by 888

Special Issue Editors

Dr. Gulshan Sharma

E-Mail Website
Guest Editor
Department of Electrical & Electronic 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 & Electronic 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—2nd Edition”, 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
  • smart grids

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Related Special Issue

Published Papers (2 papers)

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Research

35 pages, 3223 KB  
Article
Blockchain-Enhanced Cybersecurity Framework for Industry 4.0 Smart Grids: A Machine Learning-Based Intrusion Detection Approach
by Asrar Mahboob, Muhammad Rashad, Ahmed Bilal Awan and Ghulam Abbas
Energies 2026, 19(9), 2202; https://doi.org/10.3390/en19092202 - 2 May 2026
Viewed by 113
Abstract
Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more [...] Read more.
Recent years have witnessed the rapid proliferation of Industry 4.0 technologies in smart grids, leading to a revolution in energy generation and management, which provides improved operational efficiency and intelligent automation for smart grids. Nevertheless, this highly integrated infrastructure, while making energy more secure and reliable, simultaneously creates greater vulnerability to sophisticated cyber threats such as Distributed Denial of Service (DDoS) attacks, data manipulation and unauthorized access. The task of addressing these challenges requires innovative approaches that maintain the resilience as well as security of critical energy infrastructures. A novel Blockchain-Enhanced Cybersecurity Framework (BCF) specific to Industry 4.0-enabled smart grid systems is presented in this paper. The proposed framework integrates advanced security protocols with real-time threat detection capabilities through the decentralized, transparent and tamper-resistant nature of blockchain technology. Authentication, data validation and secure communication are accomplished through smart contracts to automate it, eliminating human intervention and single points of failures. The framework is able to allow for high transaction volumes, typical of modern smart grid networks, whilst maintaining integrity via a hybrid consensus mechanism that ensures scalability. In addition, the framework is further augmented with a Machine Learning-Based Intrusion Detection System (ML-IDS) to detect and mitigate cyber-attacks in real time. The proposed system achieves excellent performance in identifying malicious activities with high accuracy, precision and recall on the UNSW-NB15 dataset. Analysis with traditional methods indicates that the Blockchain Enhanced Cybersecurity Framework significantly lowers false positive rates and increases detection reliability. The framework is justified in terms of its strength to secure the systems in Industry 4.0-enabled smart grids against emerging cyber threats through extensive simulations and case studies. The value of this work is that it shows that blockchain and machine learning can be used to improve cybersecurity in renewable energy systems, and concrete insights and recommendations on implementing secure and cost-effective systems of energy infrastructure are provided. The proposed framework creates an enabling environment on which the creation of resilient and future-ready smart grids to facilitate the global goal of sustainable and secure energy can be developed. 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—2nd Edition)
28 pages, 1600 KB  
Article
A Data-Driven Deep Reinforcement Learning Framework for Real-Time Economic Dispatch of Microgrids Under Renewable Uncertainty
by Biao Dong, Shijie Cui and Xiaohui Wang
Energies 2026, 19(6), 1481; https://doi.org/10.3390/en19061481 - 16 Mar 2026
Viewed by 439
Abstract
The real-time economic dispatch of microgrids (MGs) is challenged by the high penetration of renewable energy and the resulting source–load uncertainties. Conventional optimization-based scheduling methods rely heavily on accurate probabilistic models and often suffer from high computational burdens, which limits their real-time applicability. [...] Read more.
The real-time economic dispatch of microgrids (MGs) is challenged by the high penetration of renewable energy and the resulting source–load uncertainties. Conventional optimization-based scheduling methods rely heavily on accurate probabilistic models and often suffer from high computational burdens, which limits their real-time applicability. To address these challenges, a data-driven deep reinforcement learning (DRL) framework is proposed for real-time microgrid energy management. The MG dispatch problem is formulated as a Markov decision process (MDP), and a Deep Deterministic Policy Gradient (DDPG) algorithm is adopted to efficiently handle the high-dimensional continuous action space of distributed generators and energy storage systems (ESS). The system state incorporates renewable generation, load demand, electricity price, and ESS operational conditions, while the reward function is designed as the negative of the operational cost with penalty terms for constraint violations. A continuous-action policy network is developed to directly generate control commands without action discretization, enabling smooth and flexible scheduling. Simulation studies are conducted on an extended European low-voltage microgrid test system under both deterministic and stochastic operating scenarios. The proposed approach is compared with model-based methods (MPC and MINLP) and representative DRL algorithms (SAC and PPO). The results show that the proposed DDPG-based strategy achieves competitive economic performance, fast convergence, and good adaptability to different initial ESS conditions. In stochastic environments, the proposed method maintains operating costs close to the optimal MINLP reference while significantly reducing the online computational time. These findings demonstrate that the proposed framework provides an efficient and practical solution for the real-time economic dispatch of microgrids with high renewable penetration. 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—2nd Edition)
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