Symmetry and Renewable Energy Power Systems
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 2
Special Issue Editor
Interests: maximum power point tracking (MPPT) algorithms; photovoltaic (PV) systems; DC-DC converters; artificial intelligence and machine learning in renewable energy; energy storage systems and integration
Special Issue Information
Dear Colleagues,
We warmly invite you to contribute to the upcoming Special Issue “Symmetry and Renewable Energy Power Systems.”
With the rapid growth of renewable energy technologies, the optimization of system performance, the integration of advanced control methods, and the development of efficient power conversion devices have become crucial to meeting today’s global energy and environmental challenges. This Special Issue offers a unique platform to present and exchange the latest scientific and technological advancements in this dynamic and high-impact research area.
The aim of this Special Issue is to gather high-quality contributions focusing on the modeling, optimization, control, and integration of renewable energy systems. The topics are fully aligned with the scope of Symmetry, with particular emphasis on symmetry, asymmetry, and their applications in energy conversion and management. The Special Issue seeks to compile at least ten high-quality articles. If this number is achieved, the collection may also be published in book form, thereby providing enhanced visibility to the accepted works.
In this Issue, we welcome both original research articles and comprehensive review papers. Research areas may include (but are not limited to) the following:
- Maximum Power Point Tracking (MPPT) algorithms for renewable energy harvesting;
- DC–DC converters and advanced power conversion topologies;
- Wind Energy Conversion Systems (WECS);
- PV system characterization and optimization;
- Advanced and nonlinear control of power converters;
- Grid-connected and standalone PV systems;
- Optimization techniques and artificial intelligence in renewable energy;
- Energy storage systems and integration.
We look forward to receiving your contributions.
Prof. Dr. Abdelkhalek Chellakhi
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Symmetry 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
- maximum power point tracking (MPPT) algorithms
- grid-connected and standalone photovoltaic systems
- DC–DC converters
- wind energy systems
- optimization
- advanced control of power converters
- electric vehicles
- energy storage systems
- nonlinear control and optimization techniques
- artificial intelligence and machine learning approaches
- metaheuristic techniques
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