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Machine Learning and Artificial Intelligence Techniques Applications in Renewable Energy Systems

A Special Issue of Energies (ISSN 1996-1073) belonging to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1801

Editor

Prof. Dr. Ramiro Barbosa

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Guest Editor
Electrical Engineering, Instituto Superior de Engenharia do Porto (ISEP), Polytechnic of Porto, 4249-015 Porto, Portugal
Interests: control; modeling; simulation; artificial intelligence; fractional calculus; fractional-order control; evolutionary algorithms; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing complexity of renewable energy systems demands advanced strategies to address challenges related to variability, stability, energy management, interoperability, and security. Recent advances in machine learning, deep learning, reinforcement learning, and hybrid models have demonstrated high potential in improving the efficiency, reliability, and sustainability of smart grids and renewable energy systems. These approaches are expected to accelerate the transition toward cleaner and more sustainable energy solutions.

This Special Issue aims to collect and promote the most recent advances in the theory and applications of machine learning and artificial intelligence for renewable energy systems. Contributions focusing on renewable generation forecasting and optimization, fault detection, energy storage management, the control of distributed energy resources, microgrid optimization, cybersecurity and data management, and smart grid operation are particularly encouraged.

Prof. Dr. Ramiro Barbosa
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 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-anonymized 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

  • renewable and smart energy systems
  • smart grids
  • building energy management systems
  • demand response
  • energy forecasting
  • artificial intelligence and machine learning
  • evolutionary algorithms and metaheuristics
  • multi-agent systems
  • optimization
  • cybersecurity
  • predictive maintenance

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Published Papers (4 papers)

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Research

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23 pages, 2818 KB  
Article
A Hybrid Analytical Approach for Voltage Stability Assessment in Microgrids Using Machine Learning
by Muhammad Jamshed Abbass and Robert Lis
Energies 2026, 19(17), 3983; https://doi.org/10.3390/en19173983 - 25 Aug 2026
Viewed by 122
Abstract
The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. [...] Read more.
The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Techniques Applications in Renewable Energy Systems)
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20 pages, 3654 KB  
Article
Distribution Network Optimization with Aggregation and Reinforcement Learning Under Massive Distributed Resources Integration
by Peng Yu, Jiawei Xing, Xinbin Zuo, Yan Cheng, Yu Yi, Shunmin Sun, Xiao Wei, Zhigang Zhang, Jianxiu Li and Yunpeng Zhang
Energies 2026, 19(15), 3664; https://doi.org/10.3390/en19153664 - 4 Aug 2026
Viewed by 312
Abstract
The integration of large-scale distributed energy resources (DERs) into distribution networks (DNs) brings challenges to the effective control of DNs. In traditional approaches, mathematical or reinforcement learning (RL)-based solution algorithms are commonly used. However, the exponential increase in the number of DERs reduces [...] Read more.
The integration of large-scale distributed energy resources (DERs) into distribution networks (DNs) brings challenges to the effective control of DNs. In traditional approaches, mathematical or reinforcement learning (RL)-based solution algorithms are commonly used. However, the exponential increase in the number of DERs reduces the effectiveness of these strategies. Mathematical methods struggle to cope with the dynamic uncertainty caused by the high penetration of renewable energy, while RL algorithms relying on global data training may violate multi-agent privacy protocols. This paper proposes a DNs cooperative optimization method based on resource aggregation and RL. To reduce optimization dimensionality and ensure the privacy of resource data, a dynamic aggregation strategy is employed to aggregate a large number of distributed energy resources into aggregated entities, and the adjustable active–reactive power boundaries of each aggregated entity are derived. To fully exploit the regulation capability of DNs, data centers (DCs), as novel devices, are considered as flexible loads. To improve the convergence speed of model training and decision-making accuracy, evolution strategies (ES) and prioritized experience replay (PER) are integrated into the Soft Actor-Critic (SAC) algorithm, respectively. The proposed method is validated on the IEEE 33-bus and IEEE 123-bus systems. The results demonstrate the effectiveness and superiority of the proposed method in ensuring the secure operation of DNs. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Techniques Applications in Renewable Energy Systems)
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Review

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27 pages, 1293 KB  
Review
Integration of Alternative Energy at Airports: A Safety-Oriented Review
by Daniela Marasová, Karolína Hrešková, Peter Koščák and Martina Koščáková
Energies 2026, 19(12), 2759; https://doi.org/10.3390/en19122759 - 8 Jun 2026
Viewed by 369
Abstract
This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational [...] Read more.
This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Techniques Applications in Renewable Energy Systems)
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Other

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57 pages, 2815 KB  
Systematic Review
Reinforcement Learning for Integrated MPPT and Battery Management in Photovoltaic Systems: A Systematic Review
by Francisco Fidalgo and Ramiro Barbosa
Energies 2026, 19(16), 3720; https://doi.org/10.3390/en19163720 - 7 Aug 2026
Viewed by 436
Abstract
This work provides a systematic literature review on reinforcement learning (RL) for integrated maximum power point tracking (MPPT) and battery management in photovoltaic (PV) systems. As PV installations increasingly incorporate battery energy storage, the traditional objective of maximizing instantaneous power extraction is no [...] Read more.
This work provides a systematic literature review on reinforcement learning (RL) for integrated maximum power point tracking (MPPT) and battery management in photovoltaic (PV) systems. As PV installations increasingly incorporate battery energy storage, the traditional objective of maximizing instantaneous power extraction is no longer sufficient on its own, since control decisions also affect battery state of charge, efficiency, degradation, and load support. Although RL has shown strong potential for sequential decision making in energy systems, most existing studies still treat MPPT and battery management as separate or only loosely coordinated problems. This review examines this issue by systematically examining how RL, particularly continuous-action methods, has been applied to coupled PV–battery control. The analysis highlights the shortcomings of discrete-action formulations in power-electronic systems and emphasizes the advantages and limitations of actor–critic approaches such as DDPG, TD3, PPO, and SAC for directly optimizing continuous-control variables. Across the reviewed literature, RL is found to be used predominantly at the supervisory energy management level, with PV generation often treated as exogenous rather than as an explicit control decision. This review therefore identifies a persistent structural separation between converter-level PV control and storage-aware energy management within a common learning and evaluation framework. It identifies continuous-action RL as a candidate formulation for unified PV–battery optimization while highlighting important challenges in constraint handling, state representation, sample efficiency, stability, and hardware validation. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence Techniques Applications in Renewable Energy Systems)
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