Artificial Intelligence for More Efficient Renewable Energy Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 110
Special Issue Editors
Interests: energy conversion systems; power electronics; renewable energy; artificial intelligence
Interests: sustainable aviation electrification systems & technologies; energy management strategies and integrated control systems in aviation/transport electrification; smart grid planning and operation with transport integration, such as in airports, vehicle charging, etc.
Special Issue Information
Dear Colleagues,
Overview: The steady integration of renewable energy systems into the utility grid presents novel challenges for future power grids. The intermittent nature of power injection from renewable sources, combined with unpredictable load-side demand and the rising prevalence of electric vehicles, poses significant issues for grid stability and performance. Power electronics-based power conversion systems are pivotal in maintaining grid stability and ensuring seamless operation. They manage power flow between generation and demand, control active and reactive powers injected into the grid, regulate power flow between the grid and EVs through V2G and G2V technologies, and oversee EV motor drives.
To enable these systems to meet their evolving responsibilities, such as supporting the grid during low-voltage faults and integrating V2G and G2V technologies for peak shaving and valley filling, the development of more efficient and high-performance systems based on artificial intelligence (AI) techniques is imperative. These controllers must ensure robust and high-quality power flow control, provide precise tracking performance of the reference current, and be capable of mitigating internal/external disturbances, handling model parametric mismatches, and addressing grid voltage distortion.
Machine learning-based energy management for microgrids enhances efficiency, reliability, and sustainability. It can predict and respond to energy demand fluctuations, ensuring a stable and reliable energy supply. Additionally, machine learning enables real-time decision-making, which processes vast amounts of data in real time, allowing for quick adjustments and proactive management of the energy system to maintain balance and prevent outages.
In this Special Issue, we aim to gather contributions on advanced artificial intelligence techniques for power conversion systems in the grid integration of renewable energy systems and transportation electrification applications. This Special Issue provides a comprehensive platform for the presentation and discussion of emerging research in this field. Topics of interest include but are not limited to the following:
- Advanced AI-assisted nonlinear, predictive, model-free, and data-driven control methods for power converters in renewable energy systems and electric vehicle motor drive applications.
- Grid-feeding control methods for the grid integration of renewable energy systems with energy storage systems.
- Optimizing energy management strategies for renewable energy systems using AI and soft-computing techniques.
- Grid-forming control methods for isolated microgrid applications.
- AI-based prediction of renewable generation.
- Microgrid topology identification, fault diagnosis, and system reconfiguration.
- Machine learning-based microgrid system modeling, energy management, stability analysis, and reliability assessment.
Dr. Mostefa Kermadi
Dr. **ning Zhang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net 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. Algorithms 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 1600 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
- artificial intelligence
- metaheuristic algorithm
- power conversion
- renewable energy systems
