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Renewable Energy, Electric Power Systems and Sustainability

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

Deadline for manuscript submissions: closed (5 October 2024) | Viewed by 6515

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Guest Editor
Department of Electrical Engineering, Federal University of São João del-Rei, São João del-Rei 36307-352, Brazil
Interests: DC–DC converters; AC–DC converters; photovoltaic systems; power converter topologies
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Special Issue Information

Dear Colleagues,

As the world grapples with the need to reduce greenhouse gas emissions, the transition from traditional power systems to modern smart grids represents a pivotal leap into the future. This paradigm shift toward cleaner and more sustainable energy sources enhances efficiency, reliability, and sustainability. In this context, the seamless integration of advanced technologies for real-time communication, automation, and data analytics is crucial to empower consumers with real-time insights, enable the better integration of renewable energy sources, and enhance grid resilience, fostering a more responsive, adaptive, and sustainable power infrastructure.

This Special Issue aims to create a forum for experts, professionals, and readers interested in topics related to electric power systems, renewable energies, and sustainability.

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

  • Power electronic converters applied in power systems and other related applications;
  • Electric vehicles and charging stations;
  • Control and energy management strategies;
  • Distributed generation;
  • Renewable energy conversion systems;
  • Hybrid energy systems;
  • dc and ac microgrids;
  • Assessment of energy resources in the context of smart grids.

We look forward to receiving your contributions.

Dr. Fernando Lessa Tofoli
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. 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

  • power systems
  • renewable energy
  • smart grids
  • solar energy
  • sustainability
  • wind energy

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Published Papers (4 papers)

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Research

27 pages, 17648 KiB  
Article
Switched-Capacitor-Based Hybrid Resonant Bidirectional Buck–Boost Converter for Improving Energy Harvesting in Photovoltaic Systems
by Caio Meira Amaral da Luz, Kenji Fabiano Ávila Okada, Aniel Silva Morais, Fernando Lessa Tofoli and Enio Roberto Ribeiro
Sustainability 2024, 16(22), 10142; https://doi.org/10.3390/su162210142 - 20 Nov 2024
Viewed by 636
Abstract
Photovoltaic (PV) modules are often connected in series to achieve the desired voltage level in practical applications. A common issue with this setup is module mismatch, which can be either permanent or temporary and is caused by various factors. The differential power processing [...] Read more.
Photovoltaic (PV) modules are often connected in series to achieve the desired voltage level in practical applications. A common issue with this setup is module mismatch, which can be either permanent or temporary and is caused by various factors. The differential power processing (DPP) concept has emerged as a prominent solution to address this problem. However, a significant drawback of current DPP topologies is their reduced performance under certain conditions, particularly in cases of permanent mismatch. As a result, applications involving the DPP concept for permanent mismatches remain underexplored. In this context, the goal of this work is to develop and implement a novel DPP topology capable of increasing energy harvesting in PV systems under permanent mismatch. The proposed hybrid architecture combines features from both bidirectional buck–boost (BBB) and resonant switched capacitor (ReSC) converters. The ReSC converter operates under soft-switching conditions, minimizing undesirable losses. Key advantages of the proposed converter include fewer switches, lower voltage stress, and soft-switching operation, making it suitable for PV systems with mismatched modules. Experimental tests showed an energy harvesting improvement under the assessed conditions. Full article
(This article belongs to the Special Issue Renewable Energy, Electric Power Systems and Sustainability)
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21 pages, 4017 KiB  
Article
A Machine Learning-Based Sustainable Energy Management of Wind Farms Using Bayesian Recurrent Neural Network
by Aisha Blfgeh and Hanadi Alkhudhayr
Sustainability 2024, 16(19), 8426; https://doi.org/10.3390/su16198426 - 27 Sep 2024
Cited by 2 | Viewed by 1456
Abstract
The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an [...] Read more.
The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an appropriate distribution function significantly affects the actual wind data, directly influencing the estimated energy output. While the Weibull function is commonly used to describe wind speed at various locations worldwide, the variability of weather information across wind sites varies significantly. Probabilistic forecasting offers comprehensive probability information for renewable generation and load, assisting decision-making in power systems under uncertainty. Traditional probabilistic forecasting techniques based on machine learning (ML) rely on prediction uncertainty derived from previous distributional assumptions. This study utilized a Bayesian Recurrent Neural Network (BNN-RNN), incorporating prior distributions for weight variables in the RNN network layer and extending the Bayesian networks. Initially, a periodic RNN processes data for wind energy prediction, capturing trends and correlation characteristics in time-series data to enable more accurate and reliable energy production forecasts. Subsequently, the wind power meteorological dataset was analyzed using the reciprocal entropy approach to reduce dimensionality and eliminate variables with weak connections, thereby simplifying the structure of the prediction model. The BNN-RNN prediction model integrates inputs from RNN-transformed time-series data, dimensionality-reduced weather information, and time categorization feature data. The Winkler index is lower by 3.4%, 32.6%, and 7.2%, respectively, and the overall index of probability forecasting pinball loss is reduced by 51.2%, 22.3%, and 10.7%, respectively, compared with all three approaches. The implications of this study are significant, as they demonstrate the potential for more accurate wind energy forecasting through Bayesian optimization. These findings contribute to more precise decision-making and bring sustainability to the effective management of energy systems by proposing a Bayesian Recurrent Neural Network (BNN-RNN) to improve wind energy forecasts. The model further enhances future estimates of wind energy generation, considering the stochastic nature of meteorological data. The study is crucial in increasing the understanding and application of machine learning by establishing how Bayesian optimization significantly improves probabilistic forecasting models that would revolutionize sustainable energy management. Full article
(This article belongs to the Special Issue Renewable Energy, Electric Power Systems and Sustainability)
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25 pages, 8614 KiB  
Article
Techno-Economic Analysis of Combined Production of Wind Energy and Green Hydrogen on the Northern Coast of Mauritania
by Varha Maaloum, El Moustapha Bououbeid, Mohamed Mahmoud Ali, Kaan Yetilmezsoy, Shafiqur Rehman, Christophe Ménézo, Abdel Kader Mahmoud, Shahab Makoui, Mamadou Lamine Samb and Ahmed Mohamed Yahya
Sustainability 2024, 16(18), 8063; https://doi.org/10.3390/su16188063 - 14 Sep 2024
Viewed by 2259
Abstract
Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy [...] Read more.
Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H2. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania’s north coast has potential for wind energy, aiding green hydrogen production for energy goals. Full article
(This article belongs to the Special Issue Renewable Energy, Electric Power Systems and Sustainability)
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16 pages, 13353 KiB  
Article
The Impact of Desert Regions on Solar Energy Production with the Evaluation of Groundwater for Maintenance: A Case Study in Morocco
by Ali Ait Ali, Youssef Ouhassan, Mohcine Abouyaakoub, Mbarek Chahboun and Hicham Hihi
Sustainability 2024, 16(13), 5476; https://doi.org/10.3390/su16135476 - 27 Jun 2024
Cited by 1 | Viewed by 1260
Abstract
The aim of this research work is to investigate the influence of temperature and wind-blown dust on solar energy production in a desert region of Morocco. Moreover, it aims to assess the quality of water, in particular the groundwater used for the maintenance [...] Read more.
The aim of this research work is to investigate the influence of temperature and wind-blown dust on solar energy production in a desert region of Morocco. Moreover, it aims to assess the quality of water, in particular the groundwater used for the maintenance of photovoltaic panels (quality analysis). This region is characterized by very high temperatures and wind-blown dust in the summer, which has a major impact on the production of the photovoltaic panels. Before installing this maintenance system (cooling and cleaning using water), we decided to assess the quality of this water, whose temperature generally varies between 10 and 16 °C at a depth of 4 m, whatever the season. This is an important, stable, and sustainable source of water that can be entirely used to protect the photovoltaic modules from wind-blown dust and temperature in order to improve their efficiency. However, this water can also have a major impact on the quality of the energy. It can be contaminated with limestone and salts, which can cause the photovoltaic panels to block. All the research and studies carried out in the context of maintenance using water do not take into account the nature of this water (whether it is good or bad). After simulating our model on the Matlab-Simulink environment, we can see that the temperature has a significant influence on solar energy production (a reduction of power by 20% at 45 °C) in this region. Moreover, after the assessment of the water quality in our school laboratory, we found that the water, and especially the groundwater in this desert region of Morocco, are suitable for the maintenance of photovoltaic panels. Full article
(This article belongs to the Special Issue Renewable Energy, Electric Power Systems and Sustainability)
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