Sustainable Intelligent Design, Control and Optimization for Renewable-Integrated Power Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electromechanical Energy Conversion Systems".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 659

Editor


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Guest Editor
Department of Electrical Power Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt
Interests: optimization; power systems analysis; power transmission
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Special Issue Information

Dear Colleagues,

The global shift toward sustainable and low-carbon energy systems has significantly transformed the structure and operation of modern power networks. The large-scale integration of renewable energy sources, such as solar and wind, introduces inherent variability, uncertainty, and complexity into power system planning and operation. These challenges necessitate the development of advanced methodologies for intelligent design, real-time control, and optimal operation. In recent years, the emergence of artificial intelligence, machine learning, and metaheuristic optimization techniques has provided powerful tools to enhance system flexibility, reliability, and efficiency. Furthermore, the evolution of smart grids, microgrids, and cyber-physical energy systems has reinforced the need for interdisciplinary approaches that combine sustainability principles with intelligent engineering solutions. This Special Issue seeks to address these pressing challenges and highlight innovative contributions that support the transition toward resilient and low-carbon renewable-integrated power systems.

We are pleased to invite you to contribute to this Special Issue, titled “Sustainable Intelligent Design, Control and Optimization for Renewable-Integrated Power Systems.” This Special Issue aims to present recent advances in sustainable intelligent design, control, and optimization techniques for renewable-integrated power systems, with a strong emphasis on practical applicability and engineering innovation. It is intended to provide a focused platform for disseminating cutting-edge research that aligns with the scope of Machines, particularly in areas related to intelligent systems, advanced control, energy-efficient technologies, and system optimization. The scope is designed to be sufficiently broad to attract diverse contributions while maintaining a clear focus on intelligent and sustainable energy system applications.

This Special Issue aims to include topics on the following:

  • Promoting innovative AI-driven and optimization-based solutions for power systems;
  • Addressing challenges of renewable energy integration and system uncertainty;
  • Enhancing operational efficiency, stability, and resilience of modern grids;
  • Bridging theoretical developments with real-world engineering applications;
  • Encouraging interdisciplinary research across energy, control, and computational intelligence.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Intelligent optimization algorithms for power system planning and operation;
  • Renewable energy integration, hybrid systems, and energy storage technologies;
  • Smart grids, microgrids, and distributed energy resource management;
  • AI-based control strategies for stability, frequency, and voltage regulation;
  • Energy management systems and demand-side response optimization;
  • Low-carbon technologies and sustainable energy system design;
  • Metaheuristic, evolutionary, and data-driven approaches in power engineering;
  • Digital twin, IoT, and cyber-physical systems in energy applications;
  • Uncertainty modeling, stochastic optimization, and robust control techniques;
  • Electric vehicle integration and its impact on power system operation;
  • Power system resilience, reliability, and security enhancement;
  • Multi-objective optimization and decision-making in energy systems.

We look forward to receiving your contributions. 

Dr. Abdullah Shaheen
Guest Editor

Manuscript Submission Information

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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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • sustainable power systems
  • renewable energy integration
  • intelligent optimization
  • artificial intelligence
  • smart grids
  • microgrids
  • energy management systems
  • power system control
  • metaheuristic algorithms
  • energy storage systems

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Published Papers (1 paper)

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Research

43 pages, 9331 KB  
Article
Sustainable Multi-Energy Microgrid Operation: Birds of Prey-Based Day-Ahead Scheduling Under Seasonal Renewable Uncertainty
by Hany S. E. Mansour, Hassan M. Hussein Farh, Abdullrahman A. Al-Shamma’a, AL-Wesabi Ibrahim, Abdullah M. Al-Shaalan, Amira S. Mohamed and Honey A. Zedan
Machines 2026, 14(5), 559; https://doi.org/10.3390/machines14050559 - 16 May 2026
Viewed by 324
Abstract
The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind [...] Read more.
The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. Full article
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