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Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making

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

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 27514

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Special Issue Editors

Dr. Hegazy Rezk

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Electrical Engineering, College of Engineering - Wadi Aldwaser, Prince Sattam bin Abdulaziz University, Wadi Aldwaser, Saudi Arabia
Interests: renewable energy; energy storage devices; energy management; advanced control; optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Mokhtar Aly

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Electronics Engineering Department, Universidad Tecnica Federico Santa Maria, Valparaiso 2390123, Chile
Interests: renewable energy applications; reliability of power electronics systems; multilevel inverters; model predictive control; resilient microgrids; electric vehicles (EV)

Prof. Dr. Mohammad Ali Abdelkareem

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Sustainable and Renewable Energy Engineering Department, University of Sharjah, Sharjah, United Arab Emirates
Interests: renewable energy; fuel cells; microbial fuel cells; energy storage; water desalination
Special Issues, Collections and Topics in MDPI journals

Dr. Ahmed Fathy

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Guest Editor

Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia
Interests: renewable energy (solar energy, wind energy and hybrid systems); artificial intelligence applications; system security and system stability; operational planning and scheduling; optimal operation and control of power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial advancement and rapid population growth have resulted in increasing fossil fuel usage that is limited in resources and has a severe environmental impact. The strength of this environmental impact has increased with global warming, and the health issues associated with are quite clear today. The consensus among scientists is that sustainable renewable energy sources with no or very low environmental impact are the best solution to this problem. Modeling and optimization are effectively used to solve complicated processes in a short time with minimum effort. Among the different modeling and optimization processes, Artificial Intelligence (AI) and modern optimization have exhibited excellent results in dealing with various applications in various research areas. Modeling based on AI and modern optimization methods is playing a key part in the industrial revolution, being extensively used by practicing engineers to solve complicated problems. Moreover, applying modern control systems can lead to enhancing energy efficiency, reliability, stability, and energy security of renewable and sustainable energy systems. Model predictive control (MPC) methods can achieve fast, precise, and multiobjective control tasks for renewable energy systems. By contrast, multicriteria decision making (MCDM) problems are basically fundamental issues in various fields, including renewable and sustainable energy. MCDM models provide a useful way to model several real-world problems, and they are extensively used in many engineering applications, such as energy efficiency, sustainable development, and so forth. The Special Issue provides a platform for researchers and practitioners from both academia and industry in addition to experts in the area of modern optimizations, control systems, artificial intelligence, and decision making applied to renewable and sustainable energy. Papers published in this Special Issue describe original works in different topics in both science and engineering, such as: soft computing, neural networks, fuzzy logic, multicriteria decision making, etc.

We cordially invite you to submit your original contributions to this Special Issue, entitled: “Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multicriteria Decision Making”. This is a Special Issue of Sustainability MDPI, an international peer-reviewed open access journal covered by various databases, such as WOS and SCOPUS. The present Special Issue aims to collect innovative solutions and experimental research supported by appropriate modeling and design, but also state-of-the-art studies, in the following topics:

Modeling based on artificial intelligence
Decision-making methods for sustainable development
Modern optimization
Renewable energy systems
Hydrogen and fuel cell
Energy storage systems
Advanced control systems
Model predictive control
Energy management strategies
Neural networks
Energy efficiency

Dr. Hegazy Rezk
Dr. Mokhtar Aly
Dr. Mohammad Ali Abdelkareem
Dr. Ahmed Fathy
Guest Editors

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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
energy efficiency
model predictive control
artificial intelligence
decision making
modern optimization
energy management
solar energy
wind energy
biomass
fuel cell

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Published Papers (11 papers)

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Research

25 pages, 4848 KiB

Open AccessArticle

A New Nonlinear Controller for the Maximum Power Point Tracking of Photovoltaic Systems in Micro Grid Applications Based on Modified Anti-Disturbance Compensation

by Ahmad Taher Azar, Azher M. Abed, Farah Ayad Abdulmajeed, Ibrahim A. Hameed, Nashwa Ahmad Kamal, Anwar Jaafar Mohamad Jawad, Ali Hashim Abbas, Zainab Abdulateef Rashed, Zahraa Sabah Hashim, Mouayad A. Sahib, Ibraheem Kasim Ibraheem and Rasha Thabit

Sustainability 2022, 14(17), 10511; https://doi.org/10.3390/su141710511 - 23 Aug 2022

Cited by 8 | Viewed by 2083

Abstract

In the photovoltaic system, the performance, efficiency, and generated power of the PV system are affected by changes in the environment, disturbances, and parameter variations, and this leads to a deviation from the operating maximum power point (MPP) of the PV system. Therefore, the main aim of this paper is to ensure the PV system operates at the maximum power point under the influence of exogenous disturbances and uncertainties, i.e., no matter how the irradiation, temperature, and load of the PV system change, by proposing a maximum power point tracking for the photovoltaic system (PV) based on the active disturbance rejection control (ADRC) paradigm. The proposed method provides better performance with excellent tracking for the MPP by controlling the duty cycle of the DC–DC buck converter. Moreover, comparison simulations have been performed between the proposed method and the linear ADRC (LADRC), conventional ADRC, and the improved ADRC (IADRC) to investigate the effectiveness of the proposed method. Finally, the simulation results validated the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with excellent response and minimum output performance index (OPI). Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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18 pages, 6644 KiB

Open AccessArticle

Experimental Investigation on Waste Heat Recovery from a Cement Factory to Enhance Thermoelectric Generation

by Mohamed R. Gomaa, Talib K. Murtadha, Ahmad Abu-jrai, Hegazy Rezk, Moath A. Altarawneh and Abdullah Marashli

Sustainability 2022, 14(16), 10146; https://doi.org/10.3390/su141610146 - 16 Aug 2022

Cited by 15 | Viewed by 2447

Abstract

This work investigated the potential for waste heat recovery from a cement factory using thermoelectric generation (TEG) technology. Several TEGs were placed on a secondary coaxial shell separated from the kiln shell by an air gap. The performance of the system was tested and evaluated experimentally. Two cooling methods, active water and forced air, were considered. A forced closed-loop water cooling system with a heat exchanger was considered for the active-water cooling method. A heat exchanger was inserted before the water tank to improve cooling efficiency by reducing the inlet temperature of the cooling water tank, in contrast to forced-air cooling, in which a heatsink was used. The obtained results indicated that the closed-loop water-cooled system equipped with a radiator, i.e., active water, has the highest conversion efficiency. The maximum absorbed heat for the forced-air and active-water cooling systems were 265.03 and 262.95 W, respectively. The active-water cooling method improves the power of TEG by 4.4% in comparison with forced-air cooling, while the payback periods for the proposed active-water and forced-air cooling systems are approximately 16 and 9 months, respectively. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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24 pages, 5369 KiB

Open AccessArticle

Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines

by Muhammad Nabeel Hussain, Nadeem Shaukat, Ammar Ahmad, Muhammad Abid, Abrar Hashmi, Zohreh Rajabi and Muhammad Atiq Ur Rehman Tariq

Sustainability 2022, 14(14), 8458; https://doi.org/10.3390/su14148458 - 11 Jul 2022

Cited by 6 | Viewed by 1691

Abstract

In this paper, the meta-heuristic multi-objective elitist teaching–learning based optimization technique is implemented for wind farm layout discrete optimization problem. The optimization of wind farm layout addresses the optimum siting among the wind turbines within the wind farm to accomplish economical, profitable, and technical features. The presented methodology is implemented with multi-objective optimization problem through different targets such as minimizing cost, power output maximization, and the saving of the number of turbines. These targets are investigated with some case studies of multi-objective optimization problems in three scenarios of wind (Scenario-I: fixed wind direction and constant speed, Scenario-II: variable wind direction and constant speed, and Scenario-III: variable wind direction and variable speed) for the optimal micro-siting of wind turbines in a given land area that maximizes the power production while minimizing the total cost. To check the effectiveness of the algorithm, firstly, the results obtained for the three different scenarios have been compared with past studies available in the literature. Secondly, the numbers of turbines have also been optimized by using teaching–learning based optimization. It has been observed that the proposed algorithm shows the optimal layouts along with the optimal number of turbines with minimum fitness evaluation. Finally, the concept of elitism has been introduced in the teaching–learning based optimization algorithm. It is proposed that if elitist-teaching–learning based optimization with elite size of 15% is used, computational expense can be significantly reduced. It can be concluded that that the results obtained by the proposed algorithm are more accurate and advantageous than others. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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16 pages, 4675 KiB

Open AccessArticle

A Novel Approach to Generate Hourly Photovoltaic Power Scenarios

by Stephan Schlüter, Fabian Menz, Milena Kojić, Petar Mitić and Aida Hanić

Sustainability 2022, 14(8), 4617; https://doi.org/10.3390/su14084617 - 12 Apr 2022

Viewed by 2064

Abstract

Photovoltaic power is playing an ever-increasing role in the energy mix of countries worldwide. It is a stochastic energy source, and simulation models are needed to establish reliable risk management. This paper presents a novel approach for simulating hourly solar irradiation and—as a consequence—photovoltaic power based on easily accessible data such as wind, temperature, and cloudiness. Solar simulations are generated via a multiplication factor that scales the maximum possible solar irradiation. Photovoltaic simulations are then derived using formulas that approximate the physical interdependencies. The resulting simulations are unbiased on an annual level and reasonably reflect historic irradiation movements. Interpreting our approach as a descriptive model, we find that error values vary over the year and with granularity. Errors are highest when considering hourly values in wintertime, especially in the morning or late afternoon. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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12 pages, 2709 KiB

Open AccessArticle

Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production

by Ahmed M. Nassef and Ahmed Handam

Sustainability 2022, 14(5), 2970; https://doi.org/10.3390/su14052970 - 3 Mar 2022

Cited by 3 | Viewed by 1816

Abstract

The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling parameters were considered to increase the hydrogen: irradiation time (min), metal loading, methane concentration, and steam concentration. In the first phase, an ANFIS model was created with the help of the experimental data samples. The subtractive clustering (SC) technique was used to generate the fuzzy rules. In addition, the Gaussian-type and weighed average were used for the fuzzification and defuzzification methods, respectively. The reliability of the resulting model was assessed statistically by RMSE and the correlation (R²) measures. The small RMSE value and high R² value of testing samples assured the correctness of the modelling phase, as they reached 0.0668 and 0.981, respectively. Based on the robust model, the optimization phase was applied. The slime mold algorithm (SMA), as a recent as well as simple optimizer, was applied to look for the best set of parameters that maximizes hydrogen production. The resulting values were compared by the findings of three competitive optimizers, namely particle swarm optimization (PSO), Harris hawks optimization (HHO), and evolutionary strategy HHO (EESHHO). By running the optimizers 30 times, the statistical results showed that the SMA obtained the maximum value with high mean, standard deviation, and median. Furthermore, the proposed strategy of combining the ANFIS modelling and the SMA optimizer produced an increase in the hydrogen production by 15.7% in comparison to both the experimental and traditional RSM techniques. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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32 pages, 5611 KiB

Open AccessArticle

Environmental Assessment of a Diesel Engine Fueled with Various Biodiesel Blends: Polynomial Regression and Grey Wolf Optimization

by Ali Alahmer, Hussein Alahmer, Ahmed Handam and Hegazy Rezk

Sustainability 2022, 14(3), 1367; https://doi.org/10.3390/su14031367 - 25 Jan 2022

Cited by 31 | Viewed by 3310

Abstract

A series of tests were carried out to assess the environmental effects of biodiesel blends made of different vegetable oil, such as corn, sunflower, and palm, on exhaust and noise diesel engine emissions. Biodiesel blends with 20% vegetable oil biodiesel and 80% diesel fuel by volume were developed. The tests were conducted in a stationary diesel engine test bed consisting of a single-cylinder, four-stroke, and direct injection engine at variable engine speed. A prediction framework in terms of polynomial regression (PR) was first adopted to determine the correlation between the independent variables (engine speed, fuel type) and the dependent variables (exhaust emissions, noise level, and brake thermal efficiency). After that, a regression model was optimized by the grey wolf optimization (GWO) algorithm to update the current positions of the population in the discrete searching space, resulting in the optimal engine speed and fuel type for lower exhaust and noise emissions and maximizing engine performance. The following conclusions were drawn from the experimental and optimization results: in general, the emissions of unburned hydrocarbon (UHC), carbon dioxide (CO₂), and carbon monoxide (CO) from all the different types of biodiesel blends were lower than those of diesel fuel. In contrast, the concentration of nitrogen oxides (NOx) emitted by all the types of biodiesel blends increased. The noise level produced by all the forms of biodiesel, especially palm biodiesel fuel, was lowered when compared to pure diesel. All the tested fuels had a high noise level in the middle frequency band, at 75% engine load, and high engine speeds. On average, the proposed PR-GWO model exhibited remarkable predictive reliability, with a high square of correlation coefficient (R²) of 0.9823 and a low root mean square error (RMSE) of 0.0177. Finally, the proposed model achieved superior outcomes, which may be utilized to predict and maximize engine performance and minimize exhaust and noise emissions. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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21 pages, 3364 KiB

Open AccessArticle

Investigation of the Effect of Solar Ventilation on the Cabin Temperature of Vehicles Parked under the Sun

by Hani Al-Rawashdeh, Ahmad O. Hasan, Hazem A. Al-Shakhanbeh, Mujahed Al-Dhaifallah, Mohamed R. Gomaa and Hegazy Rezk

Sustainability 2021, 13(24), 13963; https://doi.org/10.3390/su132413963 - 17 Dec 2021

Cited by 12 | Viewed by 3473

Abstract

During hot days, the temperature inside vehicles parked under the sun is very high; according to previous studies, the vehicle cabin temperature can be more than 20 °C higher than the ambient temperature. Due to the greenhouse effect, the heating that occurs inside a vehicle while it is parked under the sun has an impact on energy crises and environmental pollution. In addition, the increase in the temperature inside the cabin will have an effect on the dashboard and plastic accessories and the leather on the seats will age rapidly. The ventilation of solar energy from the cabin of a vehicle parked under the blazing sun has received a great deal of attention. The present study was conducted to utilize a renewable energy system to operate the ventilation system through a novel portable ventilation system powered by solar energy. Experimental results were obtained for a vehicle with and without the solar ventilation system. The results indicate that the maximum daily average difference in temperature during the experimental tests between the cabin of the car and the atmospheric temperature with and without the solar ventilation system was 7.2 °C and 20.6 °C, respectively. With and without the usage of the system, the minimum average difference in temperature between the automobile’s cabin and the atmospheric temperature was 6.2 °C and 17.6 °C, respectively. The results indicate that the proposed system is effective and that the thermal comfort inside the vehicle’s cabin improved when the vehicle was parked under the hot sun. Therefore, this system helps to protect human bodies, conserve energy, protect the environment, protect the vehicle’s cabin, and provide a comfortable environment. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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22 pages, 11259 KiB

Open AccessArticle

A Modified Triple-Diode Model Parameters Identification for Perovskite Solar Cells via Nature-Inspired Search Optimization Algorithms

by Alaa A. Zaky, Ahmed Fathy, Hegazy Rezk, Konstantina Gkini, Polycarpos Falaras and Amlak Abaza

Sustainability 2021, 13(23), 12969; https://doi.org/10.3390/su132312969 - 23 Nov 2021

Cited by 8 | Viewed by 2188

Abstract

Recently, perovskite solar cells (PSCs) have been widely investigated as an efficient alternative for silicon solar cells. In this work, a proposed modified triple-diode model (MTDM) for PSCs modeling and simulation was used. The Bald Eagle Search (BES) algorithm, which is a novel nature-inspired search optimizer, was suggested for solving the model and estimating the PSCs device parameters because of the complex nature of determining the model parameters. Two PSC architectures, namely control and modified devices, were experimentally fabricated, characterized and tested in the lab. The I–V datasets of the fabricated devices were recorded at standard conditions. The decision variables in the proposed optimization process are the nine and ten unknown parameters of triple-diode model (TDM) and MTDM, respectively. The direct comparison with a number of modern optimization techniques including grey wolf (GWO), particle swarm (PSO) and moth flame (MFO) optimizers, as well as sine cosine (SCA) and slap swarm (SSA) algorithms, confirmed the superiority of the proposed BES approach, where the Root Mean Square Error (RMSE) objective function between the experimental data and estimated characteristics achieves the least value. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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17 pages, 4903 KiB

Open AccessArticle

A Sine Cosine Algorithm-Based Fractional MPPT for Thermoelectric Generation System

by Hegazy Rezk, Mohammed Mazen Alhato, Mujahed Al-Dhaifallah and Soufiene Bouallègue

Sustainability 2021, 13(21), 11650; https://doi.org/10.3390/su132111650 - 21 Oct 2021

Cited by 5 | Viewed by 1681

Abstract

Thermoelectric generators (TEGs) are equipment for transforming thermal power into electricity via the Seebeck effect. These modules have gained increasing interest in research fields related to sustainable energy. The harvested energy is mostly reliant on the differential temperature between the hot and cold areas of the TEGs. Hence, a reliable maximum power point tracker is necessary to operate TEGs too close to their maximum power point (MPP) under an operational and climate variation. In this paper, an optimized fractional incremental resistance tracker (OF-INRT) is suggested to enhance the output performance of a TEG. The introduced tracker is based on the fractional-order PI^λD^μ control concepts. The optimal parameters of the OF-INRT are determined using a population-based sine cosine algorithm (SCA). To confirm the optimality of the introduced SCA, experiments were conducted and the results compared with those of particle swarm optimization (PSO) and whale optimization algorithm (WOA) based techniques. The key goal of the suggested OF-INRT is to overcome the two main issues in conventional trackers, i.e., the slow dynamics of traditional incremental resistance trackers (INRT) and the high steady-state fluctuation around the MPP in the prevalent perturb and observe trackers (POTs). The main findings prove the superiority of the OF-INRT in comparison with the INRT and POT, for both dynamic and steady-state responses. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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19 pages, 1061 KiB

Open AccessArticle

Multicriteria Decision-Making to Determine the Optimal Energy Management Strategy of Hybrid PV–Diesel Battery-Based Desalination System

by Hegazy Rezk, Basem Alamri, Mokhtar Aly, Ahmed Fathy, Abdul G. Olabi, Mohammad Ali Abdelkareem and Hamdy A. Ziedan

Sustainability 2021, 13(8), 4202; https://doi.org/10.3390/su13084202 - 9 Apr 2021

Cited by 18 | Viewed by 2383

Abstract

This paper identifies the best energy management strategy of hybrid photovoltaic–diesel battery-based water desalination systems in isolated regions using technical, economic and techno–economic criteria. The employed procedures include Criteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as tools for the solution. Twelve alternatives, containing three–four energy management strategies; four energy management strategies, load following (LF), cycle charging (CC), combined LF–CC, and predictive strategy; and three different sizes of brackish water reverse osmosis (BWRO) water desalination units, BWRO-150, BWRO-250, and BWRO-500, are investigated with capacity of 150, 250, and 500 m³/day, respectively. Eight attributes comprising different technical and economic metrics are considered during the evaluation procedure. HOMER Pro^® software is utilized to perform the simulation and optimization. The main findings confirmed that the best energy management strategies are predictive strategies and the reverse osmosis (RO) unit’s optimal size is RO-250. For such an option, the annual operating cost and initial costs are $4590 and $78,435, respectively, whereas the cost of energy is $0.156/kWh. The excess energy and unmet loads are 27,532 kWh and 20.3 kWh, respectively. The breakeven grid extension distance and the amount of CO₂ are 6.02 km and 14,289 kg per year, respectively. Compared with CC–RO-150, the amount of CO₂ has been sharply decreased by 61.2%. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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31 pages, 7273 KiB

Open AccessArticle

Dynamic Voltage Restorer Integrated with Photovoltaic-Thermoelectric Generator for Voltage Disturbances Compensation and Energy Saving in Three-Phase System

by N. Kanagaraj and Hegazy Rezk

Sustainability 2021, 13(6), 3511; https://doi.org/10.3390/su13063511 - 22 Mar 2021

Cited by 18 | Viewed by 2658

Abstract

The dynamic voltage restorer (DVR) combined with a photovoltaic–thermoelectric generator (PV-TEG) system is proposed for voltage disturbance compensation in the three-phase four-wire distribution system. The PV-TEG hybrid energy source is used in the DVR system to improve the system ability for deep and long-period power quality disturbance compensation. In addition, the DVR will save grid energy consumption when the hybrid PV-TEG module generates sufficient power to meet the load demand. An enhanced variable factor adaptive fuzzy logic controller (VFAFLC)-based maximum power point tracking (MPPT) control scheme is proposed to extract the maximum possible power from the PV module. Since the PV and TEG combine a hybrid energy source for generating power, the DVR can work efficiently for the voltage sag/swell, outage compensation, and energy conservation mode with minimum energy storage facilities. The in-phase compensation method and the three-leg voltage source inverter (VSI) circuit are chosen in the present system for better voltage and/or power compensation. To confirm the effectiveness of the proposed hybrid PV-TEG integrated DVR system, a simulation-based investigation is carried out with four different operational modes with MATLAB software. The study results confirm that the proposed DVR system can compensate power quality disturbances of the three-phase load with less total harmonics distortion (THD) and will also work efficiently under energy conservation mode to save grid energy consumption. Moreover, the proposed VFAFLC-based control technique performs better to achieve the maximum power point (MPP) quickly and accurately, thereby improving the efficiency of the hybrid energy module. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy: Modeling, Control, Modern Optimization and Multi Criteria Decision Making)

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