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Renewable Energy and Power Electronics Technology

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 4601

Special Issue Editors

Dr. Minh-Chau Dinh

E-Mail Website
Guest Editor
Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea
Interests: power system analysis; FACTS; power electronic applications; wind energy; applications of digital twin
Special Issues, Collections and Topics in MDPI journals
Dr. Jae-In Lee

E-Mail Website
Guest Editor
Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea
Interests: power system analysis; FACTS; power system protection

Special Issue Information

Dear Colleagues,

The global transition to renewable energy sources is now imperative in order to address the twin challenges of climate change and the rising energy demands of a rapidly growing population. This Special Issue, entitled “Renewable Energy and Power Electronics Technology”, delves into the interdisciplinary nexus of renewable energy systems and power electronics, emphasizing advancements, challenges, and innovations that shape this dynamic field.

This Special Issue provides a platform for the exploration of emerging technologies that enable the efficient generation, conversion, storage, and utilization of renewable energy. In addition, we welcome articles that  focus on the role of power electronics in enhancing the penetration of renewable energy, particularly in distributed energy resources and microgrids. Furthermore, this Special Issue discusses the integration of artificial intelligence and machine learning in predictive maintenance, energy forecasting, and system optimization.

The scope of this Special Issue includes, but is not limited to, the following topics:

-Renewable energy (Photovoltaic, wind energy systems, etc.)

-Energy conversion technologies

-Energy storage technologies

-Smart grid integration

-Advanced power electronics converters

-Control strategies and optimization methods for improving system reliability, scalability, and cost-effectiveness

-Novel converter topologies

-Grid-interfacing techniques (grid-forming inverter, etc.)

-Energy management strategies

By collecting contributions from researchers, engineers, and industry professionals, this Special Issue aims to provide a comprehensive overview of the current state and future directions of renewable energy and power electronics technology. It serves as a valuable resource for academics, practitioners, and policymakers seeking to promote sustainable energy solutions and create a resilient and sustainable energy infrastructure for the future.

Dr. Minh-Chau Dinh
Dr. Jae-In Lee
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

  • renewable energy
  • energy conversion
  • energy storage
  • grid forming
  • virtual power plant (VPP)
  • advanced power electronics converters
  • design
  • modelling
  • control

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

Published Papers (3 papers)

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Research

20 pages, 1584 KB  
Article
Determinants of Consumer Decisions in the Electric Vehicle Market
by Stanisław Bielski, Renata Marks-Bielska, Paweł Wiśniewski, Krystyna Kurowska and Przemysław Sobieraj
Energies 2026, 19(3), 667; https://doi.org/10.3390/en19030667 - 27 Jan 2026
Cited by 1 | Viewed by 839
Abstract
Determinants of consumers’ decisions in the electric vehicle market are dictated by many factors, starting from ecology to the profitability of owning an electric vehicle. Currently, the electric vehicle market in Poland grows every year. When addressing the issues related to the determinants [...] Read more.
Determinants of consumers’ decisions in the electric vehicle market are dictated by many factors, starting from ecology to the profitability of owning an electric vehicle. Currently, the electric vehicle market in Poland grows every year. When addressing the issues related to the determinants of consumer decisions on the electric vehicle market, statistical data and an online questionnaire were used, in which 103 people, who were interested in electric vehicles, participated. The main purpose of this research was to determine what factors influence consumers’ attitude to the purchase of electric vehicles the most. The study focuses primarily on Battery Electric Vehicles (BEVs), as reflected in the survey design and respondents’ interpretations of electric vehicles. The study showed that over half of the respondents are considering the purchase of an electric vehicle, and to purchase this type of car they would be more encouraged by financial support, such as subsidies from the state and tax relief, as well as free parking spaces in cities. It was also established that consumers are discouraged from buying electric vehicles by the lack of adequate infrastructure in cities needed to freely own an electric vehicle, as well as too high prices of these cars and the long time it takes to charge the battery. Full article
(This article belongs to the Special Issue Renewable Energy and Power Electronics Technology)
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37 pages, 15911 KB  
Article
Geometry-Resolved Electro-Thermal Modeling of Cylindrical Lithium-Ion Cells Using 3D Simulation and Thermal Network Reduction
by Martin Baťa, Milan Plzák, Michal Miloslav Uličný, Gabriel Gálik, Markus Schörgenhumer, Šimon Berta, Andrej Ürge and Danica Rosinová
Energies 2026, 19(2), 375; https://doi.org/10.3390/en19020375 - 12 Jan 2026
Viewed by 855
Abstract
Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive [...] Read more.
Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article
(This article belongs to the Special Issue Renewable Energy and Power Electronics Technology)
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19 pages, 4071 KB  
Article
Design of an Efficient Deep Learning-Based Diagnostic Model for Wind Turbine Gearboxes Using SCADA Data
by Xuan-Kien Mai, Jun-Yeop Lee, Jae-In Lee, Byeong-Soo Go, Seok-Ju Lee and Minh-Chau Dinh
Energies 2025, 18(11), 2814; https://doi.org/10.3390/en18112814 - 28 May 2025
Cited by 4 | Viewed by 2018
Abstract
Global efforts to address climate change have intensified the transition from fossil fuels to renewable energy sources, positioning wind power as a critical player due to its advanced technology, scalability, and environmental benefits. Despite their potential, the reliability of wind turbines, particularly their [...] Read more.
Global efforts to address climate change have intensified the transition from fossil fuels to renewable energy sources, positioning wind power as a critical player due to its advanced technology, scalability, and environmental benefits. Despite their potential, the reliability of wind turbines, particularly their gearboxes, remains a persistent challenge. Gearbox failures lead to significant downtime, high maintenance costs, and reduced operational efficiency, threatening the economic competitiveness of wind energy. This study proposes an innovative condition monitoring model for wind turbine gearboxes, utilizing Supervisory Control and Data Acquisition systems and Deep Learning techniques. The model analyzes historical operating data from wind turbine to classify gearbox conditions into normal and abnormal states. Optimizing the dataset for deep neural networks through advanced data processing methods achieves an impressive fault detection accuracy of 98.8%. Designed for seamless integration into real-time monitoring systems, this approach enables early fault prediction and supports proactive maintenance strategies. By enhancing gearbox reliability, reducing unplanned downtime, and lowering maintenance expenses, the model improves the overall economic viability of wind farms. This advancement reinforces wind energy’s pivotal role in driving a sustainable, low-carbon future, aligning with global climate goals and renewable energy adoption. Full article
(This article belongs to the Special Issue Renewable Energy and Power Electronics Technology)
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