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Advances in Computational Intelligence for Control, Estimation, and Optimization in Power Systems, Electrical Machines, and Renewable Energy Systems

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2134

Special Issue Editors

Dr. Amirmehdi Yazdani

E-Mail Website
Guest Editor
School of Engineering and Energy, Murdoch University, Perth, WA 6150, Australia
Interests: robotics and autonomous systems; control systems; AI-based decision making and planning
Special Issues, Collections and Topics in MDPI journals
Dr. Amin Mahmoudi

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide 5042, Australia
Interests: electrical machines and energy conversion; power electronics and electrical drives; renewable energy systems and energy storage; electric vehicles; power system analysis distributed generation
Special Issues, Collections and Topics in MDPI journals
Dr. Solmaz Kahourzade

E-Mail Website
Guest Editor
SREM, University of South Australia, Adelaide 5042, Australia
Interests: electrical machines and power electronics for applications such as renewable energy, electric traction systems, and electrical drives for industrial applications
Special Issues, Collections and Topics in MDPI journals
Dr. Zhongwei Deng

E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: electric vehicles; energy storage systems; battery modeling; battery management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growing complexity of modern power systems, electrical machines, and renewable energy integration, there is increasing demand for intelligent, adaptive, and robust methods to ensure efficient operation, reliability, and sustainability. Computational intelligence (CI) techniques, including artificial neural networks, fuzzy systems, evolutionary algorithms, and hybrid methods, have emerged as powerful tools to tackle challenges in modeling, control, estimation, and optimization within these domains.

This Special Issue will bring together cutting-edge research and practical advancements in the application of computational intelligence to power and energy systems. Contributions that highlight novel approaches, real-world implementations, and interdisciplinary innovations are especially welcome.

Topics of interest include, but are not limited to, the following:

  1. Computational Intelligence in Power Systems and Smart Grids;
  2. Advanced Modelling of Electrical Machines and Drives;
  3. Control Strategies for Power Electronics and Energy Conversion;
  4. Fault Diagnosis and Condition Monitoring using AI;
  5. State Estimation and Forecasting in Renewable Energy Systems;
  6. Optimization Techniques for Energy Management and Grid Stability;
  7. Hybrid and Adaptive Intelligent Systems for Microgrids and Distributed Generation;
  8. Machine Learning for Load Forecasting and Demand Response;
  9. Digital Twin and Intelligent Automation in Electrical Engineering Applications;
  10. Real-time Applications and Hardware-in-the-Loop Implementation of CI Techniques.

Dr. Amirmehdi Yazdani
Dr. Amin Mahmoudi
Dr. Solmaz Kahourzade
Dr. Zhongwei Deng
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

  • computational intelligence
  • artificial neural networks
  • fuzzy logic systems
  • genetic algorithms
  • swarm intelligence
  • intelligent control
  • state estimation
  • system identification
  • electrical drives and motor control
  • power electronics and converters
  • renewable energy systems (wind, solar, hybrid)
  • smart grid technologies
  • energy storage optimization
  • electric vehicles and transportation electrification
  • microgrids and distributed generation
  • power system stability and forecasting
  • model predictive control
  • fault detection and diagnostics
  • data-driven energy management

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

Related Special Issue

Published Papers (3 papers)

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Research

30 pages, 3662 KB  
Article
Novel GBest–Lévy Adaptive Differential Ant Bee Colony Optimization for Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Smart Distribution Systems
by Aadel Mohammed Alatwi, Hani Albalawi, Abdul Wadood, Ibrahem E. Atawi and Khaled Saleem S. Alatawi
Energies 2025, 18(22), 6018; https://doi.org/10.3390/en18226018 - 17 Nov 2025
Viewed by 249
Abstract
The transition to electric vehicles (EVs) is pivotal for decarbonizing transport, yet the siting of EV charging stations (EVCSs) can load radial distribution networks with higher losses and more pronounced voltage drops. This study formulates the joint siting and sizing of EVCSs and [...] Read more.
The transition to electric vehicles (EVs) is pivotal for decarbonizing transport, yet the siting of EV charging stations (EVCSs) can load radial distribution networks with higher losses and more pronounced voltage drops. This study formulates the joint siting and sizing of EVCSs and distributed generators (DGs) as a constrained optimization that minimizes real and reactive losses and voltage deviation with integer bus location decisions. A novel version of the Artificial Bee Colony (ABC) algorithm known as GBest–Lévy Adaptive Differential ABC (GLAD-ABC) is introduced, combining global best guidance, differential perturbations, adaptive step sizes, Lévy-flight scouting, and periodic local refinement for finding the global optimum solution and avoiding local optima. The optimizer is coupled with a backward–forward sweep load flow and a EVCS power demand model. Validation on the IEEE-33 and IEEE-69 feeders across multiple scenarios shows that EVCS-only deployment degrades network performance, whereas optimizing EVCS and DG allocation via GLAD-ABC markedly improves voltage profiles and reduces both real and reactive losses. The proposed optimizer shows superior performance compared with other optimization algorithms reported in the literature, delivering consistently lower active losses alongside fast, stable convergence, indicating strong suitability for utility planning in EV-rich grids. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence for Control, Estimation, and Optimization in Power Systems, Electrical Machines, and Renewable Energy Systems)
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16 pages, 2907 KB  
Article
A New Model for Partial Discharge Inception Voltage Estimation in Insulation Systems at Low and High Pressure: Application to Electrical Asset Components
by Gian Carlo Montanari, Sukesh Babu Myneni, Muhammad Shafiq and Zhaowen Chen
Energies 2025, 18(21), 5782; https://doi.org/10.3390/en18215782 - 2 Nov 2025
Viewed by 645
Abstract
Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types [...] Read more.
Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types of power supply sources, from AC sinusoidal to DC and power electronics. This revolution, beneficial for asset efficiency and resilience, does come at the cost of increased risk of failure for electrical insulation systems. Intrinsic and extrinsic aging mechanisms are not completely known under DC and power electronics, and the risk of inception of partial discharges, PD, which is the most harmful extrinsic aging factor for electrical insulation, is as high, or even higher, compared with AC. To complicate the picture, electrical and electronic components can be used at different pressure levels, such as in aerospace, and it is known that partial discharge inception voltage, PDIV, drops down, and PD magnitude increases, lowering pressure. Models to predict PDIV for surface and internal discharges, as function of pressure, have been proposed recently, but they cannot be applied straightforwardly on practical asset components where type and locations of defects generating PD is unknown. This paper wants to close this application gap. Derivation and validation of an approximate, heuristic model able to predict PDIV at various pressure levels below and above the standard atmospheric pressure, SAP, are dealt with in this paper, referring to typical asset components such as cables, motors, printed circuit-boards, PCB, and under sinusoidal AC voltage. The good capability of the model to predict PDIV and any investigated pressure, from 3 to 0.05 bar, is validated by PD measurements performed using an innovative, automatic PD analytics software able to identify the typology of defect generating PD, i.e., whether surface or internal. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence for Control, Estimation, and Optimization in Power Systems, Electrical Machines, and Renewable Energy Systems)
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29 pages, 4258 KB  
Article
A Risk-Averse Data-Driven Distributionally Robust Optimization Method for Transmission Power Systems Under Uncertainty
by Mehrdad Ghahramani, Daryoush Habibi and Asma Aziz
Energies 2025, 18(19), 5245; https://doi.org/10.3390/en18195245 - 2 Oct 2025
Viewed by 910
Abstract
The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow [...] Read more.
The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence for Control, Estimation, and Optimization in Power Systems, Electrical Machines, and Renewable Energy Systems)
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