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Processes
Special Issues
Advanced Technologies of Renewable Energy Sources (RESs)
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Advanced Technologies of Renewable Energy Sources (RESs)

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 1882

Special Issue Editors

Dr. Bonginkosi A. Thango

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Guest Editor
Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa
Interests: power systems; renewable energies; predictive maintenance of electrical equipment; artificial intelligence
Prof. Dr. Katleho Moloi

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Guest Editor
Electrical Power Engineering, Durban University of Technology, Durban 4001, South Africa
Interests: power system protection; renewable energy; machine learning applications
Dr. Agha F. Nnachi

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Guest Editor Assistant
Department of Electrical Engineering, Tshwane University of Technology, eMalahleni 1034, South Africa
Interests: HVDC; power conversion; power converters; power electronics; distributed generation; renewable energy technologies

Special Issue Information

Dear Colleagues,

The global pursuit of sustainable development has brought renewable energy sources (RESs) to the forefront as crucial solutions for reducing carbon emissions and mitigating climate change. The integration of advanced technologies into RESs has significantly enhanced their efficiency, reliability, and feasibility, supporting the global transition to cleaner energy systems. This interdisciplinary field involves the latest innovations in solar, wind, hydro, biomass, and geothermal energy, as well as energy storage solutions and grid integration strategies.

Advances in these technologies have led to significant progress in areas such as energy harvesting, conversion efficiency, cost reduction, and the minimization of environmental impacts. However, as energy demands rise and climate imperatives become more pressing, there is a need for continued innovation, optimization, and comprehensive analysis to maximize the potential of renewable technologies.

This Special Issue on “Advanced Technologies of Renewable Energy Sources (RESs)” seeks high-quality works focusing on cutting-edge advancements in renewable energy technologies. Topics include, but are not limited to, the following:

  • Innovations in solar photovoltaics and thermal systems;
  • Next-generation wind turbine design and efficiency enhancement;
  • Biomass and biofuel production technologies;
  • Hydroelectric power systems and improvements;
  • Geothermal energy utilization and technological advancements;
  • Energy storage solutions for intermittent RESs;
  • Smart grid integration and management of RESs;
  • Hybrid renewable energy systems;
  • Techno-economic analysis of renewable energy technologies;
  • Policy and strategic planning for promoting RES adoption.

We encourage authors to submit original research articles, comprehensive review articles, and insightful short communications. Planned papers may be announced with a title and short abstract (around 100 words) submitted to the Editorial Office.

Dr. Bonginkosi A. Thango
Prof. Dr. Katleho Moloi
Guest Editors

Dr. Agha F. Nnachi
Guest Editor Assistant

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. Processes 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

  • renewable energy technologies
  • solar power
  • wind energy
  • biomass conversion
  • energy storage solutions
  • geothermal energy
  • smart grid integration
  • sustainable energy systems
  • energy efficiency
  • hybrid renewable systems

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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

23 pages, 3856 KiB  
Article
Modeling and Optimization of an Integrated Energy Supply in the Oil and Gas Industry: A Case Study of Northeast China
by Yujie Zhu, Jinze Li, Pei Liu, Guosheng Zhang and He Liu
Processes 2025, 13(5), 1512; https://doi.org/10.3390/pr13051512 - 14 May 2025
Viewed by 366
Abstract
The oil and gas industry has large and constant power and heat loads and usually ownership of land resources near oil and gas production, providing opportunities for onsite integration of renewable energy. In the future, a possible decrease in reliable and affordable electricity [...] Read more.
The oil and gas industry has large and constant power and heat loads and usually ownership of land resources near oil and gas production, providing opportunities for onsite integration of renewable energy. In the future, a possible decrease in reliable and affordable electricity production capability by the power grid, mainly due to the penetration of intermittent renewable energy, makes seeking an alternative energy supply a wise choice for the oil and gas industry. Foreseeable carbon emission costs also drive the oil and gas industry to a less carbon-intensive way of production. However, it is not yet clear whether it is economically viable for the integration of renewable energy in the oil and gas industry. In this work, we propose a modeling and optimization framework for conceptual planning and the operation of an oilfield’s energy system, where energy demands—heat and power in particular, are supposed to be met by an integrated energy supply including both fossil fuels and renewable energy. Herein, an oilfield in Northeast China has been then taken as a case study. The results indicate that under current conditions with no power purchase limits, integrating renewable energy is not economically viable. As the power purchase limits the decrease by a certain threshold, renewable energy integration becomes essential for maintaining a stable power supply, with renewable energy capacity reaching 35% in a self-sufficient microgrid configuration. Similarly, as electricity prices rise, the deployment of renewable energy begins to exhibit economic viability at 1.5 times the current electricity price. Independent microgrids show better economic resilience compared to grid-dependent systems under rising electricity prices. When carbon prices increase, the heat electrification transformation for microgrids achieves a cost inflection point at USD 76.4 per tonne, resulting in overall cost reductions. These findings emphasize the importance of flexible, renewable energy-driven energy systems in cost-effective decarbonization and energy stability, providing insights for optimizing oilfield energy systems and supporting China’s carbon neutrality goals. Full article
(This article belongs to the Special Issue Advanced Technologies of Renewable Energy Sources (RESs))
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28 pages, 3803 KiB  
Article
Comparative Analysis of Five Numerical Methods and the Whale Optimization Algorithm for Wind Potential Assessment: A Case Study in Whittlesea, Eastern Cape, South Africa
by Ngwarai Shambira, Lwando Luvatsha and Patrick Mukumba
Processes 2025, 13(5), 1344; https://doi.org/10.3390/pr13051344 - 27 Apr 2025
Viewed by 289
Abstract
This study explores the potential of wind energy to address electricity shortages in South Africa, focusing on the Ekuphumleni community in Whittlesea. Given the challenges of expanding the national grid to these areas, wind energy is considered to be a feasible alternative to [...] Read more.
This study explores the potential of wind energy to address electricity shortages in South Africa, focusing on the Ekuphumleni community in Whittlesea. Given the challenges of expanding the national grid to these areas, wind energy is considered to be a feasible alternative to provide clean, renewable energy and reduce fossil fuel dependence in this community. This research evaluates wind potential utilizing the two-parameter Weibull distribution, with scale and shape parameters estimated by five traditional numerical methods and one metaheuristic optimization technique: whale optimization algorithm (WOA). Goodness-of-fit tests, such as the coefficient of determination (R2) and wind power density error (WPDE), were utilized to determine the best method for accurately estimating Weibull scale and shape parameters. Furthermore, net fitness, which combines R2 and WPDE, was employed to provide a holistic assessment of overall performance. Whittlesea showed moderate wind speeds, averaging 3.88 m/s at 10 m above ground level (AGL), with the highest speeds in winter (4.87 m/s) and optimum in July. The WOA method outperformed all five numerical methods in this study in accurately estimating Weibull distribution parameters. Interestingly, the openwind method (OWM), a numerical technique based on iterative methods, and the Brent method showed comparable performance to WOA. The wind power density was 67.29 W/m2, categorizing Whittlesea’s potential as poor and suitable for small-scale wind turbines. The east wind patterns favor efficient turbine placement. The study recommends using augmented wind turbines for the site to maximize energy capture at moderate speeds. Full article
(This article belongs to the Special Issue Advanced Technologies of Renewable Energy Sources (RESs))
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28 pages, 8870 KiB  
Article
Performance Analysis of Advanced Metaheuristics for Optimal Design of Multi-Objective Model Predictive Control of Doubly Fed Induction Generator
by Kumeshan Reddy, Rudiren Sarma and Dipayan Guha
Processes 2025, 13(1), 221; https://doi.org/10.3390/pr13010221 - 14 Jan 2025
Viewed by 729
Abstract
Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus [...] Read more.
Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus far exhibited promising results when compared to the conventional Proportional Integral control strategy. Recently, there has been research conducted regarding the reduction in switching frequency of FCS-MPC. Preliminary studies indicate that a reduction in switching frequency will result in larger current ripples and a greater total harmonic distortion (THD). However, research in this area is limited. The aim of this study is two-fold. Firstly, an indication into the effect of weighting factor magnitude on current ripple is provided. Thereafter, the research work provides insight into the effect of such weighting factor on the overall current ripple of FCS-MPC applied to the DFIG and attempts to determine an optimal weighting factor which will simultaneously reduce the switching frequency and keep the current ripple within acceptable limits. To tune the relevant weighting factor, the utilization of swam intelligence is deployed. Three swarm intelligence techniques, particle swarm optimization, the African Vulture Optimization Algorithm, and the Gorilla Troops Optimizer (GTO), are applied to achieve the optimal weighting factor. When applied to a 2 MW DFIG, the results indicated that owing to their strong exploitation capability, these algorithms were able to successfully reduce the switching frequency. The GTO exhibited the overall best results, boasting steady-state errors of 0.03% and 0.02% for the rotor direct and quadrature currents whilst reducing the switching frequency by up to 0.7%. However, as expected, there was a minor increase in the current ripple. A robustness test indicated that the use of metaheuristics still produces superior results in the face of changing operating conditions. The results instill confidence in FCS-MPC as the control strategy of choice, as wind energy conversion systems continue to penetrate the energy sector. Full article
(This article belongs to the Special Issue Advanced Technologies of Renewable Energy Sources (RESs))
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