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Renewable and Sustainable Energy System Techniques Development
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Renewable and Sustainable Energy System Techniques Development

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

Deadline for manuscript submissions: closed (9 June 2023) | Viewed by 10958

Special Issue Editor

Prof. Dr. Ashraf Mohamed Hemeida

E-Mail Website
Guest Editor
Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt
Interests: renewable energy systems; optimization techniques; power quality; power system stability; IoT technology in power systems; FACTS applications; micro-grid systems

Special Issue Information

Dear Colleagues,

It is well known that renewable and sustainable energy systems have become a major area of interest due to their significant benefits over other sources of energies. The growing use of wind farms, photovoltaic systems, concentrated solar power systems, geothermal energy systems, and tidal energy systems provides many points of research to improve the efficiency and quality of these energy systems. Implementations of advanced control techniques, and hybrid optimization techniques based on FACT devices to improve the power output quality of renewable energy systems is an urgent need. Governments around the world are encouraging investments in the field of renewable energy by facilitating the procedures of installing renewable energy systems, especially in remote areas for sustainable development. Developments in techniques enhancing renewable energy output support the sustainability development plan. In addition to the implementation of optimization techniques in other subjects related to energy consumption such as electric furnaces, etc., topics of interest for publication include but are not limited to:

  • Photovoltaic energy system cooling techniques in dry weather;
  • Impact of weather uncertainty on PV output power;
  • Wind energy systems;
  • Hybrid PV/wind energy systems;
  • Tidal energy systems;
  • Geothermal energy systems;
  • Impact of renewable energy systems on sustainability;
  • Optimal design of wind energy and PV systems;
  • Machine learning algorithms in energy systems;
  • Nuclear energy system stability;
  • Impact of reactive energy sources on system stability;
  • Application of advanced control techniques on energy systems;
  • Micro-grid systems impacts on network stability;
  • Applications of optimized FACT devices on renewable energy systems;
  • Application of advanced single and muti-objective optimization techniques.

Prof. Dr. Ashraf Mohamed Hemeida
Guest Editor

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. 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

  • wind energy systems
  • photovoltaic technologies
  • hybrid renewable energy systems
  • renewable and sustainable energy applications
  • optimal renewable energy sources
  • optimization techniques in renewable energy systems
  • tidal energy systems
  • geothermal energy systems
  • nuclear energy
  • machine learning in energy systems

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Published Papers (4 papers)

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Research

30 pages, 7285 KiB  
Article
Optimizing Artificial Neural Networks for the Accurate Prediction of Global Solar Radiation: A Performance Comparison with Conventional Methods
by Mohamed A. Ali, Ashraf Elsayed, Islam Elkabani, Mohammad Akrami, M. Elsayed Youssef and Gasser E. Hassan
Energies 2023, 16(17), 6165; https://doi.org/10.3390/en16176165 - 24 Aug 2023
Cited by 14 | Viewed by 2430
Abstract
Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many [...] Read more.
Obtaining precise solar radiation data is the first stage in determining the availability of solar energy. It is also regarded as one of the major inputs for a variety of solar applications. Due to the scarcity of solar radiation measurement data for many locations throughout the world, many solar radiation models are utilized to predict global solar radiation. Indeed, the most widely used AI technique is artificial neural networks (ANNs). Hitherto, while ANNs have been utilized in various studies to estimate global solar radiation (GSR), limited attention has been given to the architecture of ANN. Thus, this study aimed to: first, optimize the design of one of the faster and most used machine-learning (ML) algorithms, the ANN, to forecast GSR more accurately while saving computation power; second, optimize the number of neurons in the hidden layer to obtain the most significant ANN model for accurate GSR estimation, since it is still lacking; in addition to investigating the impact of varying the number of neurons in the hidden layer on the proficiency of the ANN-based model to predict GSR with high accuracy; and, finally, conduct a comparative study between the ANN and empirical techniques for estimating GSR. The results showed that the best ANN model and the empirical model provided an excellent estimation for the GSR, with a Coefficient of Determination R2 greater than 0.98%. Additionally, ANN architectures with a smaller number of neurons in the single hidden layer (1–3 neurons) provided the best performance, with R2 > 0.98%. Furthermore, the performance of the developed ANN models remained approximately stable and excellent when the number of hidden layer’s neurons was less than ten neurons (R2 > 0.97%), as their performance was very close to each other. However, the ANN models experienced performance instability when the number of hidden layer’s neurons exceeded nine neurons. Furthermore, the performance comparison between the best ANN-based model and the empirical one revealed that both models performed well (R2 > 0.98%). Moreover, while the relative error for the best ANN model slightly exceeded the range, ±10% in November and December, it remained within the range for the empirical model even in the winter months. Additionally, the obtained results of the best ANN model in this work were compared with the recent related work. While it had a good RMSE value of 0.8361 MJ/m2 day−1 within the ranges of previous work, its correlation coefficient (r) was the best one. Therefore, the developed models in this study can be utilized for accurate GSR forecasting. The accurate and efficient estimation of global solar radiation using both models can be valuable in designing and performance evaluation for different solar applications. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy System Techniques Development)
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18 pages, 1045 KiB  
Article
A Computational Case Study on Sustainable Energy Transition in the Kingdom of Saudi Arabia
by Mohammed Alghassab
Energies 2023, 16(13), 5133; https://doi.org/10.3390/en16135133 - 3 Jul 2023
Cited by 10 | Viewed by 2860
Abstract
With the increasing urgency for sustainable development and energy transition, decision-makers face complex challenges in evaluating and prioritizing viable alternatives. Traditional decision-making techniques often struggle to capture the inherent uncertainty and imprecision associated with the latest sustainable energy transition issues. This paper presents [...] Read more.
With the increasing urgency for sustainable development and energy transition, decision-makers face complex challenges in evaluating and prioritizing viable alternatives. Traditional decision-making techniques often struggle to capture the inherent uncertainty and imprecision associated with the latest sustainable energy transition issues. This paper presents a research framework based on fuzzy set theory and the technique for order of preference by similarity to ideal solution (TOPSIS) method to address these complexities and uncertainties. Our proposed approach offers a comprehensive evaluation and ranking of alternatives for sustainable energy transition. To demonstrate the effectiveness and applicability of this system, we employ a case study in the Kingdom of Saudi Arabia (KSA). As a global leader in fossil fuel production and export, particularly oil, the KSA has recognized the need to address climate change and diversify its energy sector. By leveraging the fuzzy TOPSIS-based framework, we provide decision-makers with a powerful tool to navigate the challenges and uncertainties involved in the energy transition process. This research yields promising results, demonstrating the superior capabilities of the proposed fuzzy TOPSIS-based framework compared to traditional decision-making techniques. The case study in the KSA highlights how our approach effectively captures and addresses the uncertainties and complexities involved in sustainable energy transition decision making. Through comprehensive evaluations and rankings, decision-makers gain valuable insights into alternative solutions, facilitating informed and strategic decision-making processes. Our research contributes to sustainable energy transitions by introducing a robust decision-making framework that integrates fuzzy set theory and the TOPSIS method. Based on the fuzzy TOPSIS-based evaluation, the research findings indicate that solar energy (EA1) ranked as the most favourable alternative among the evaluated options for the sustainable energy transition in the KSA. Using our framework, stakeholders in the KSA and similar contexts can make informed decisions to accelerate their energy transition efforts and achieve sustainable development goals. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy System Techniques Development)
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27 pages, 8394 KiB  
Article
An Online Archimedes Optimization Algorithm Identifier-Controlled Adaptive Modified Virtual Inertia Control for Microgrids
by Asmaa Fawzy, Youssef Mobarak, Dina S. Osheba, Mahmoud G. Hemeida, Tomonobu Senjyu and Mohamed Roshdy
Energies 2022, 15(23), 8884; https://doi.org/10.3390/en15238884 - 24 Nov 2022
Cited by 4 | Viewed by 1635
Abstract
Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual [...] Read more.
Single widespread employment of renewable energy sources (RESs) contributes to the shortage in the inertia of the microgrid (MG). After this, frequency stability may regress as a result of power imbalance or minor load fluctuations. This paper proposes an explicit adaptive modified virtual inertia control (VIC) based on an online Archimedes optimization algorithm (AOA) identifier for MG containing thermal, wind, and solar photovoltaic power plants. The Rung Kutta approach is used to construct the proposed online identifier, which acts as a model of the MG. AOA predicts the coefficients of the online identifier based on the input and output of MG to mimic the frequency deviation of the MG online. AOA estimates online the inertia and damping coefficients of the VIC system with an energy storage device based on online AOA identifier coefficients. The frequency deviation of the MG based on the proposed explicit adaptive modified VIC is compared with the conventional VIC based on fixed parameters and the VIC system based on optimal parameters using AOA offline under mutation in loads, weather-dependent input, and MG parameters using MATLAB/Simulink software. Furthermore, the proposed explicit adaptive modified VIC based on an online AOA identifier is evaluated with the adaptive VIC system based on fuzzy logic control, which adjusts only the inertial gain online. The simulation results demonstrate the capabilities of the proposed explicit adaptive modified VIC to improve the frequency stability and enhance low-inertia islanded MGs with RESs. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy System Techniques Development)
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12 pages, 4358 KiB  
Article
Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique
by Mahmoud M. Hussein, Salem Alkhalaf, Tarek Hassan Mohamed, Dina S. Osheba, Mahrous Ahmed, Ashraf Hemeida and Ammar M. Hassan
Energies 2022, 15(22), 8474; https://doi.org/10.3390/en15228474 - 13 Nov 2022
Cited by 6 | Viewed by 3145
Abstract
In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is [...] Read more.
In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application. Full article
(This article belongs to the Special Issue Renewable and Sustainable Energy System Techniques Development)
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