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Advanced Control Strategies for Renewable Energy Systems and Sustainable Development
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Advanced Control Strategies for Renewable Energy Systems and Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 8214

Special Issue Editors

Prof. Dr. Youssef Errami

E-Mail Website
Guest Editor
Faculty of Sciences, Chouaib Doukkali University, Eljadida, Morocco
Interests: renewable energy system control; power electronics systems; electric drives and electrical power engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdellatif Obbadi

E-Mail Website
Guest Editor
Faculty of Sciences, Chouaib Doukkali University, El Jadida, Morocco
Interests: renewable energy system control; power electronics systems; electric drives and electrical power engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sahnoun Smail

E-Mail Website
Guest Editor
Laboratory Electronics, Instrumentation and Energy (LEIE), Physics Department, Faculty of Sciences, Chouaïb Doukkali University, El Jadida 24000, Morocco
Interests: renewable energy systems; non-destructive thermal testing

Special Issue Information

Dear Colleagues,

In recent years, with the growth of population and industrialization, there has been increasing attention on renewable energy sources, which are unlimited in nature and eco-friendly. In addition, these sources are expected to play a significant role in the future energy industry, becoming the main alternative to, and reaching the level of, conventional fossil resources.

This Special Issue aims to present recent advances related to the theory and control of all categories of renewable energy sources.

Topics of interest for publication include, but are not limited to, the following:

  • Renewable energy systems;
  • Back-to-back power converters;
  • New efficient renewable energy technologies;
  • Multi-level power converters for renewable energy sources;
  • Modelling, control and nonlinear control of renewable energy sources;
  • Fuzzy logic research;
  • AI learning approaches;
  • New control strategies for maximum power extraction;
  • Adaptive power electronic control algorithms for the efficient operation of integrated renewable energy sources;
  • Artificial intelligence control;
  • Optimal power flow control;
  • Practical implementation of control techniques;
  • Grid-forming power electronics systems and future grid code requirements.
  • Sustainable energy

Prof. Dr. Youssef Errami
Prof. Dr. Abdellatif Obbadi
Prof. Dr. Sahnoun Smail
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 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. Sustainability 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 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 conversion systems 
  • maximum power point tracking 
  • power electronic converter 
  • controllers of renewable energy systems 
  • control and optimization 
  • nonlinear controllers 
  • fault ride through 
  • artificial intelligence control 
  • fuzzy logic research 
  • deep learning 
  • sustainable energy

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

Published Papers (6 papers)

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Research

30 pages, 9417 KiB  
Article
Proposal of a Hybrid Neuro-Fuzzy-Based Controller to Optimize the Energy Efficiency of a Wind Turbine
by Nathalia-Michelle Peralta-Vasconez, Leonardo Peña-Pupo, Pablo-Andrés Buestán-Andrade, José R. Nuñez-Alvarez and Herminio Martínez-García
Sustainability 2025, 17(8), 3742; https://doi.org/10.3390/su17083742 - 21 Apr 2025
Abstract
Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural [...] Read more.
Optimizing wind turbine control is a major challenge due to wind variability and nonlinearity. This research seeks to improve the performance of wind turbines by designing and developing hybrid intelligent controllers that combine advanced artificial intelligence techniques. A control system combining deep neural networks and fuzzy logic was implemented to optimize the efficiency and operational stability of a 3.5 MW wind turbine. This study analyzed several deep learning models (LSTM, GRU, CNN, ANN, and transformers) to predict the generated power, using data from the SCADA system. The structure of the hybrid controller includes a fuzzy inference system with 28 rules based on linguistic variables that consider power, wind speed, and wind direction. Experiments showed that the hybrid-GRU controller achieved the best balance between predictive performance and computational efficiency, with an R2 of 0.96 and 12,119.54 predictions per second. The GRU excels in overall optimization. This study confirms intelligent hybrid controllers’ effectiveness in improving wind turbines’ performance under various operating conditions, contributing significantly to the field of wind energy. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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31 pages, 5958 KiB  
Article
Biogas Production from a Solar-Heated Temperature-Controlled Biogas Digester
by Francis Makamure, Patrick Mukumba and Golden Makaka
Sustainability 2024, 16(22), 9894; https://doi.org/10.3390/su16229894 - 13 Nov 2024
Cited by 2 | Viewed by 2131
Abstract
This research paper explores biogas production in an underground temperature-controlled fixed dome digester and compares it with a similar uncontrolled digester. Two underground fixed-dome digesters, one fitted with a solar heating system and a stirrer and the other one with an identical stirrer [...] Read more.
This research paper explores biogas production in an underground temperature-controlled fixed dome digester and compares it with a similar uncontrolled digester. Two underground fixed-dome digesters, one fitted with a solar heating system and a stirrer and the other one with an identical stirrer only, were batch-fed with cow dung slurry collected from the University of Fort Hare farm and mixed with water in a ratio of 1:1. The solar heating system consisted of a solar geyser, pex-al-pex tubing, an electric ball valve, a water circulation pump, an Arduino aided temperature control system, and a heat exchanger located at the centre of the digester. Both the digesters were intermittently stirred for 10 min every 4 h. The digester without a heating system was used as a control. Biogas production in the two digesters was compared to assess the effect of solar heating on biogas production. The total solids, volatile solids, and the chemical oxygen demand of the cow dung used as substrate were determined before and after digestion. These were compared together with the cumulative biogas produced and the methane content for the controlled and uncontrolled digesters. It was observed that the temperature control system kept the slurry temperature in the controlled digester within the required range for 82.76% of the retention period, showing an efficiency of 82.76%. Some maximum temperature gradients of 7.0 °C were observed in both the controlled and uncontrolled digesters, showing that the stirrer speed of 30 rpm was not fast enough to create the needed vortex for a uniform mix in the slurry. It was further observed that the heat from the solar geyser and the ground insulation were sufficient to keep the digester temperature within the required temperature range without any additional heat source even at night. Biogas yield was observed to depend on the pH with a strong coefficient of determination of 0.788 and 0.755 for the controlled and uncontrolled digesters, respectively. The cumulative biogas was 26.77 m3 and 18.05 m3 for controlled and uncontrolled digesters, respectively, which was an increase of 33%. The methane content increased by 14% while carbon dioxide decreased by 10% from the uncontrolled to the controlled scenario. The percentage removal of the TS, VS, and COD was 66.26%, 76.81%, and 74.69%, respectively, compared to 47.01%, 60.37%, and 57.86% for the uncontrolled situation. Thus, the percentage removal of TS, VS, and COD increased by 19.25%, 16.44%, and 16.89%, respectively. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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17 pages, 4174 KiB  
Article
Nonlinear Enhanced Control for Wind Energy Generation System-Based Permanent Magnet Synchronous Generator
by Youssef Errami, Abdellatif Obbadi, Smail Sahnoun and Mohssin Aoutoul
Sustainability 2024, 16(17), 7374; https://doi.org/10.3390/su16177374 - 27 Aug 2024
Viewed by 1276
Abstract
This paper proposes a Nonlinear Backstepping Approach (NBA) to improve the control performance of a Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Generation System (WEGS) under parameter uncertainties and short circuits with fluctuations in the grid voltage. Both the rectifier and the three-phase [...] Read more.
This paper proposes a Nonlinear Backstepping Approach (NBA) to improve the control performance of a Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Generation System (WEGS) under parameter uncertainties and short circuits with fluctuations in the grid voltage. Both the rectifier and the three-phase inverter are controlled using the NBA scheme; this method ensures Maximum Power Point Tracking (MPPT), which is a very appealing control objective with unpredictable scenarios of wind speed, and regulates the active and reactive power flows to the electrical network under varying wind speeds. Also, an inverter was employed to control voltage of the DC bus and the powers. The regulator’s stability is achieving using the Lyapunov approach. Simulation results with Matlab/Simulink confirm the efficiency of the presented scheme. The comparative analysis of the NBA with conventional Vector Controllers (VCs), under parameter deviations and for low voltage drop conditions, demonstrates the efficiency of the studied method. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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20 pages, 6860 KiB  
Article
Integrated Control Design for Hybrid Grid-Photovoltaic Systems in Distillation Applications: A Reference Model and Fuzzy Logic Approach
by Hassan Abouobaida, Youssef Mchaouar, Safeer Ullah, Younes Abouelmahjoub, Hisham Alghamdi, Baheej Alghamdi and Habib Kraiem
Sustainability 2024, 16(17), 7304; https://doi.org/10.3390/su16177304 - 25 Aug 2024
Cited by 1 | Viewed by 1408
Abstract
This paper presents a novel hybrid structural control solution designed for distillation systems that utilize a solar source alongside an electrical grid. The power conversion architecture incorporates a reversible bridge rectifier and a quadratic boost converter. The hybrid photovoltaic grid configuration offers several [...] Read more.
This paper presents a novel hybrid structural control solution designed for distillation systems that utilize a solar source alongside an electrical grid. The power conversion architecture incorporates a reversible bridge rectifier and a quadratic boost converter. The hybrid photovoltaic grid configuration offers several benefits, including source complementarity, enhanced dependability, and energy availability aligned with power requirements. Leveraging a photovoltaic source operating at maximum power facilitates energy conservation. On the control front, an adaptive technique based on a reference model is proposed. Fuzzy logic governs the quadratic boost converter, simplifying the management of its complex nonlinear nature. The control strategy aims to maximize solar power utilization, minimize harmonic components in the grid current, synthesize an adaptive controller, and achieve a near-unit power factor on the grid. The simulation results for a steady distillation system demonstrate promising findings. Despite variations in irradiation, load power, and grid drops, the system maintains a minimal bus voltage ripple, remaining close to the intended value. Optimization of the panel-generated power leads to improved PV source utilization and enhanced system efficiency. Furthermore, the combination with an electrical grid achieves a low rate of grid current distortion and a unitary power factor. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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26 pages, 13435 KiB  
Article
Short-Term Prediction of Rural Photovoltaic Power Generation Based on Improved Dung Beetle Optimization Algorithm
by Jie Meng, Qing Yuan, Weiqi Zhang, Tianjiao Yan and Fanqiu Kong
Sustainability 2024, 16(13), 5467; https://doi.org/10.3390/su16135467 - 27 Jun 2024
Cited by 4 | Viewed by 1197
Abstract
Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) [...] Read more.
Addressing the challenges of randomness, volatility, and low prediction accuracy in rural low-carbon photovoltaic (PV) power generation, along with its unique characteristics, is crucial for the sustainable development of rural energy. This paper presents a forecasting model that combines variational mode decomposition (VMD) and an improved dung beetle optimization algorithm (IDBO) with the kernel extreme learning machine (KELM). Initially, a Gaussian mixture model (GMM) is used to categorize PV power data, separating analogous samples during different weather conditions. Afterwards, VMD is applied to stabilize the initial power sequence and extract numerous consistent subsequences. These subsequences are then employed to develop individual KELM prediction models, with their nuclear and regularization parameters optimized by IDBO. Finally, the predictions from the various subsequences are aggregated to produce the overall forecast. Empirical evidence via a case study indicates that the proposed VMD-IDBO-KELM model achieves commendable prediction accuracy across diverse weather conditions, surpassing existing models and affirming its efficacy and superiority. Compared with traditional VMD-DBO-KELM algorithms, the mean absolute percentage error of the VMD-IDBO-KELM model forecasting on sunny days, cloudy days and rainy days is reduced by 2.66%, 1.98% and 6.46%, respectively. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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15 pages, 4003 KiB  
Article
Design and Performance Analysis of a Small-Scale Prototype Water Condensing System for Biomass Combustion Flue Gas Abatement
by Valentina Coccia, Ramoon Barros Lovate Temporim, Leandro Lunghi, Oleksandra Tryboi, Franco Cotana, Anna Magrini, Daniele Dondi, Dhanalakshmi Vadivel, Marco Cartesegna and Andrea Nicolini
Sustainability 2024, 16(12), 5164; https://doi.org/10.3390/su16125164 - 18 Jun 2024
Cited by 1 | Viewed by 1331
Abstract
This article outlines the design and performance of a flue gas condensation system integrated with a biomass combustion plant. The system comprises a biomass plant fuelled by wood chips, generating flue gases. These gases are condensed via a double heat exchanger set-up, extracting [...] Read more.
This article outlines the design and performance of a flue gas condensation system integrated with a biomass combustion plant. The system comprises a biomass plant fuelled by wood chips, generating flue gases. These gases are condensed via a double heat exchanger set-up, extracting water and heat to reduce concentrations of CO, CO2, and NOx while releasing gases at a temperature close to ambient temperature. The 100 kW biomass plant operates steadily, consuming 50 kg of wood chips per hour with fuel energy of 18.98 MJ/kg. Post combustion, the gases exit at 430 °C and undergo two-stage cooling. In the first stage, gases are cooled in a high-temperature tube heat exchanger, transferring heat to air. They then enter the second stage, a flue gas/water heat exchanger, recovering sensible and latent thermal energy, which leads to water condensation. Flue gas is discharged at approximately 33 °C. Throughout, parameters like the flue gas temperatures, mass flow, fuel consumption, heat carrier temperatures, and water condensation rates were monitored. The test results show that the system can condense water from flue gas at 75 g/min at 22 °C while reducing pollutant emissions by approximately 20% for CO2, 19% for CO, 30% for NO, and 26% for NOx. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Renewable Energy Systems and Sustainable Development)
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