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Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions

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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: 31 May 2024 | Viewed by 14924

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Special Issue Editors

Dr. Ahmed Fathy

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Guest Editor

Electrical Engineering Department, Jouf University, Sakakah 42421, 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

Dr. Hassan M. Hussein Farh

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Guest Editor

Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
Interests: smart grid technologies; renewable energy (wind and solar PV) applications; energy conservation measures; distributed power generation; power and energy infrastructure; power electronics applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, renewable energy resources (RESs) have found applications in many fields due to their effectiveness in reducing the harmful effects of fossil fuel sources. Renewable energy technologies are considered clean energy resources, and the optimal use of these resources mitigates the environmental impacts, and produces minimum secondary wastes; in addition, these are sustainable resources based on current and future economic and social societal needs. Moreover, RESs improve the power quality, achieve less power losses, and provide environmental benefits. Renewable technologies have an important role in confirming the sustainability of renewable energy. However, the operation of RESs still encounters many challenges related to climate changes and operation in abnormal conditions.

This Special Issue aims to publish a set of important research work and the latest advancements in renewable energy technologies to minimize the accompanied challenges of their operation. Specifically, authors are encouraged to submit their research investigating theoretical or simulation models, practical and experimental studies, optimization algorithms, and applications concerning microgrid technologies and renewable energy applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Renewable energy (solar PV, wind energy and hybrid systems);
Artificial intelligence and machine learning algorithms for renewable energy technologies and applications;
Optimal microgrid planning, operation and control;
Effect of electric vehicles (EVs) on the microgrid operation;
Microgrid energy management strategy;
The design and optimization of renewable energy technologies and hybrid energy systems;
Charge control and smart grid technologies for isolated grids;
Installation and operation challenges associated with hybrid energy systems;
Energy storage technologies adapted for hybrid energy systems.

We look forward to receiving your contributions.

Dr. Ahmed Fathy
Dr. Hassan M. Hussein Farh
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 system
microgrids
electric vehicles
hybrid energy system
artificial intelligence
energy storage

Published Papers (9 papers)

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Research

26 pages, 4069 KiB

Open AccessArticle

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

by Derya Betul Unsal, Ahmet Aksoz, Saadin Oyucu, Josep M. Guerrero and Merve Guler

Sustainability 2024, 16(7), 2894; https://doi.org/10.3390/su16072894 - 30 Mar 2024

Viewed by 793

Abstract

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey’s energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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25 pages, 5369 KiB

Open AccessArticle

Smart and Sustainable Wireless Electric Vehicle Charging Strategy with Renewable Energy and Internet of Things Integration

by Sheeraz Iqbal, Nahar F. Alshammari, Mokhtar Shouran and Jabir Massoud

Sustainability 2024, 16(6), 2487; https://doi.org/10.3390/su16062487 - 17 Mar 2024

Viewed by 1330

Abstract

This study addresses the challenges associated with electric vehicle (EV) charging in office environments. These challenges include (1) reliance on manual cable connections, (2) constrained charging options, (3) safety concerns with cable management, and (4) the lack of dynamic charging capabilities. This research focuses on an innovative wireless power transfer (WPT) system specifically designed for use in office parking areas. This system incorporates renewable energy resources (RERs) and uses the transformative power of the Internet of Things (IoT). It employs a mix of solar energy systems and battery storage solutions to facilitate a sustainable and efficient energy supply to EVs. The integration of IoT technology allows for the automatic initiation of charging as soon as an EV is parked. Additionally, the implementation of the Blynk application offers users real-time access to information regarding the operational status of the photovoltaic system and the battery levels of their EVs. The system is further enhanced with IoT and RFID technologies to provide dynamic updates on the availability of charging slots and to implement strict security protocols for user authentication and protection. The research also includes a case study focusing on the application of this charging system in office settings. The case study achieves a 95.9% IRR, lower NPC of USD 1.52 million, and 56.7% power contribution by RERs, and it reduces annual carbon emissions to 173,956 kg CO₂. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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26 pages, 1852 KiB

Open AccessArticle

Sustainable Integration of Solar Energy, Behavior Change, and Recycling Practices in Educational Institutions: A Holistic Framework for Environmental Conservation and Quality Education

by Abdulrahman Altassan

Sustainability 2023, 15(20), 15157; https://doi.org/10.3390/su152015157 - 23 Oct 2023

Cited by 1 | Viewed by 4570

Abstract

Environmental sustainability in educational institutions is a critical concern for addressing global challenges. This research presents a comprehensive framework for sustainable energy conservation, behavior change, and recycling practices in schools, with the aim of fostering environmental consciousness among students and enhancing overall educational quality. The framework integrates solar photovoltaic (PV) systems, encouraging students’ participation in their maintenance while repurposing collected water for plant irrigation and using organic waste as a natural fertilizer. By creating a micro-ecosystem within schools, the approach cultivates a generation of environmentally aware individuals who actively contribute to environmental stewardship. The framework aligns with Saudi Arabia’s 2030 vision of improving quality of life and increasing green surfaces. It promotes environmental awareness, facilitates clean energy adoption, and reduces operational costs. The role of municipalities and recycling bodies is crucial for its successful execution, involving waste management support, educational programs, and regulatory compliance. Through collaboration between schools, municipalities, and recycling bodies, the framework aims to create a culture of sustainability. It envisions students as advocates, gaining experiential knowledge in renewable energy technologies and waste management. This research offers a roadmap for schools to integrate solar energy, behavior change, and recycling practices, positioning them as leaders in environmental stewardship. The framework underscores the importance of collaborative efforts, financial support, and awareness campaigns. By embracing this comprehensive approach, schools can play a pivotal role in mitigating climate change, promoting sustainable living, and inspiring a brighter future for generations to come. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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23 pages, 2700 KiB

Open AccessArticle

Optimizing Hybrid Photovoltaic/Battery/Diesel Microgrids in Distribution Networks Considering Uncertainty and Reliability

by Zulfiqar Ali Memon and Mohammad Amin Akbari

Sustainability 2023, 15(18), 13499; https://doi.org/10.3390/su151813499 - 8 Sep 2023

Viewed by 1010

Abstract

Due to the importance of the allocation of energy microgrids in the power distribution networks, the effect of the uncertainties of their power generation sources and the inherent uncertainty of the network load on the problem of their optimization and the effect on the network performance should be evaluated. The optimal design and allocation of a hybrid microgrid system consisting of photovoltaic resources, battery storage, and a backup diesel generator are discussed in this paper. The objective of the problem is minimizing the costs of power losses, energy resources generation, diesel generation as backup resource, battery energy storage as well as load shedding with optimal determination of the components energy microgrid system include its installation location in the 33-bus distribution network and size of the PVs, batteries, and Diesel generators. Additionally, the effect of uncertainties in photovoltaic radiation and network demand are evaluated on the energy microgrid design and allocation. A Monte Carlo simulation is used to explore the full range of possibilities and determine the optimal decision based on the variability of the inputs. For an accurate assessment of the system’s reliability, a forced outage rate (FOR) analysis is performed to calculate potential photovoltaic losses that could affect the operational probability of the system. The cloud leopard optimization (CLO) algorithm is proposed to optimize this optimization problem. The effectiveness of the proposed algorithm in terms of accuracy and convergence speed is verified compared to other state-of-the-art optimization methods. To further improve the performance of the proposed algorithm, the reliability and uncertainties of photovoltaic resource production and load demand are investigated. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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16 pages, 9608 KiB

Open AccessArticle

A Non-Isolated High Voltage Gain DC–DC Converter Suitable for Sustainable Energy Systems

by Mamdouh L. Alghaythi

Sustainability 2023, 15(15), 12058; https://doi.org/10.3390/su151512058 - 7 Aug 2023

Viewed by 893

Abstract

A non-isolated high gain DC–DC converter with magnetic coupling and a VM circuit is proposed in this study. By the use of the appropriate coupled inductor turn ratio, the output voltage of the recommended topology can be raised. The VM circuit is used to boost the voltage gain even further as well as to clamp the voltage spike across the switch, which results in a lower voltage on the switch. As a result, a MOSFET switch with a lower ON-state resistance (R_DS-ON) is used which, in turn, causes the conduction losses to be reduced and the entire system efficiency to be raised. Another advantage of the proposed structure is the ZCS of the diodes, which reduces the voltage drop losses caused by the regenerative diodes. The function modes analysis and the theoretical equations are accomplished. A comparison survey with other prior works is being developed to investigate the competency of the proposed converter. Based on this, the higher voltage gain and efficiency as well as the lower voltage stress on the semiconductors can be achieved by the proposed converter compared to the other converters. The effectiveness of the proposed converter is confirmed by the experimental results at a laboratory-scale operating under 150 V output voltage with a 96% efficiency at the 150 W full load and a 25 kHz switching frequency. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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21 pages, 8358 KiB

Open AccessArticle

An Efficient White Shark Optimizer for Enhancing the Performance of Proton Exchange Membrane Fuel Cells

by Ahmed Fathy and Abdulmohsen Alanazi

Sustainability 2023, 15(15), 11741; https://doi.org/10.3390/su151511741 - 30 Jul 2023

Cited by 2 | Viewed by 1066

Abstract

This study investigates the substantial contribution of the recent numerical optimization technique known as the White Shark Optimizer (WSO) to evaluate the performance of proton exchange membrane fuel cell (PEMFC) design parameters that play a considerable role in boosting its effectiveness. A numerical code was developed and implemented via MATLAB software to achieve the research goal. The proposed WSO was employed to identify the unknown parameters of the PEMFC equivalent circuit, considering experimental data. The analyzed objective function was the root mean squared error (RMSE) between the measured and estimated fuel cell terminal voltages. Additionally, the proposed WSO was compared with other intelligent approaches such as the salp swarm algorithm (SSA), Harris hawks optimization (HHO), atom search optimization (ASO), dung beetle optimization algorithm (DBOA), stochastic paint optimizer (SPO), and comprehensive learning Archimedes optimization algorithm (HCLAOA). The numerical simulations revealed that the RMSE values varied between lower and higher values of 0.009095329 and 0.028663611, respectively. Additionally, the results indicated that the mean fitness value recorded in the considered PEMFC 250 W stack was 0.020057775. Moreover, the minimum fitness value was obtained using the proposed WSO, with an operating temperature of 353.15 K and working anode and cathode pressures are 3 bar and 5 bar, respectively. The proposed WSO offered the best results in terms of absolute errors compared to the other optimizers, confirming the robustness of the results in all considered cases. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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28 pages, 10574 KiB

Open AccessArticle

A Comprehensive Study on the Performance of Various Tracker Systems in Hybrid Renewable Energy Systems, Saudi Arabia

by Mohana Alanazi, Hani Attar, Ayman Amer, Ayesha Amjad, Mahmoud Mohamed, Mohammed Sh. Majid, Khalid Yahya and Mohamed Salem

Sustainability 2023, 15(13), 10626; https://doi.org/10.3390/su151310626 - 5 Jul 2023

Cited by 3 | Viewed by 1315

Abstract

To compensate for the lack of fossil fuel-based energy production systems, hybrid renewable energy systems (HRES) would be a useful solution. Investigating different design conditions and components would help industry professionals, engineers, and policymakers in producing and designing optimal systems. In this article, different tracker systems, including vertical, horizontal, and two-axis trackers in an off-grid HRES that includes photovoltaic (PV), wind turbine (WT), diesel generator (Gen), and battery (Bat) are considered. The goal is to find the optimum (OP) combination of an HRES in seven locations (Loc) in Saudi Arabia. The proposed load demand is 988.97 kWh/day, and the peak load is 212.34 kW. The results of the cost of energies (COEs) range between 0.108 to 0.143 USD/kWh. Secondly, the optimum size of the PV panels with different trackers is calculated. The HRES uses 100 kW PV in combination with other components. Additionally, the size of the PVs where 100% PV panels are used to reach the load demand in the selected locations is found. Finally, two sensitivity analyses (Sens) on the proposed PV and tracker costs and solar GHIs are conducted. The main goal of the article is to find the most cost-effective tracker system under different conditions while considering environmental aspects such as the CO₂ social penalty. The results show an increase of 35% in power production from PV (compared to not using a tracker) when using a two-axis tracker system. However, it is not always cost-effective. The increase in power production when using vertical and horizontal trackers (HT) is also significant. The findings show that introducing a specific tracker for all locations depends on renewable resources such as wind speed and solar GHI, as well as economic inputs. Overall, for GHIs higher than 5.5 kWh/m²/day, the vertical tracker (VT) is cost-effective. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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37 pages, 5352 KiB

Open AccessArticle

Multicriteria Decision-Making for Evaluating Solar Energy Source of Saudi Arabia

by Abdulaziz Alanazi and Mohana Alanazi

Sustainability 2023, 15(13), 10228; https://doi.org/10.3390/su151310228 - 28 Jun 2023

Cited by 4 | Viewed by 1613

Abstract

Saudi Arabia generates more than 98% of its electricity through hydrocarbon resources. To reduce the consumption of fossil fuel resources and protect the environment, the government of Saudi Arabia is planning to make renewable energy an essential part of its energy mix. In this study, due to the country’s abundant solar potential, solar energy has been selected as the energy source to generate renewable energy in Saudi Arabia. The two solar energy technologies, photovoltaic (PV) and solar thermal, have been analyzed in three different locations within the country. Multi-criteria decision-making (MCDM) techniques were used to rank the cities for each of the technologies. The SAW(Simple Additive Weighting)-AHP(Analytic Hierarchy Process) MCDM method based on climate, environmental, technical, economic, and social has been adopted to analyze the suitability of each technology for all locations. To assign weights to the criteria AHP method was used, while to rank the technologies, SAW was used. The results show that for the PV technology, Abha ranked 1st with a performance score of 91%, making it the most suitable location, followed by Jeddah with 83%. While for solar thermal technologies, Jeddah is the most suitable location, with a performance score of 96%, followed by Abha with 91%. The PV systems generated a maximum of 11,019 MWh in Abha, while the solar thermal produced maximum of 14,000 MWh in Jeddah. Overall, solar thermal technology outperformed PV technology in Saudi Arabia due to the country’s higher temperature. The analysis of photovoltaic and solar thermal technologies in this study provides valuable insight for the government of Saudi Arabia in identifying the best site for solar energy technologies in the country. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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25 pages, 2052 KiB

Open AccessArticle

Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

by Abdulaziz Alanazi and Tarek I. Alanazi

Sustainability 2023, 15(11), 9034; https://doi.org/10.3390/su15119034 - 2 Jun 2023

Cited by 5 | Viewed by 1223

Abstract

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Technologies, Opportunities, Challenges, and Promising Solutions)

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