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Sustainable Energy Systems and Renewable Generation

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6555

Special Issue Editor

Dr. Sean Loughney

E-Mail Website
Guest Editor
Senior Lecturer in Maritime Management, School of Engineering, Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK
Interests: design, operation, and safety of maritime engineering systems such as: ships, oil and gas installations, and offshore renewable energy structures; marine asset integrity monitoring; management of said energy structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled " Sustainable Energy Systems and Renewable Generation ", will present the results of completed and conducted research works, both in technical and social and economic terms, based on the future directions for the development of sustainable and renewable energy technology and operations.

Energy is sustainable if it meets the needs of the present without compromising the ability of future generations to meet their own needs. Definitions of sustainable energy consider environmental aspects, such as greenhouse gas emissions, and social and economic aspects, such as energy poverty. Renewable energy sources, such as wind, hydroelectric power, solar, and geothermal energy, are generally far more sustainable than fossil fuel sources, yet some renewable energy sources, such as the clearing of forests to produce biofuels, can cause severe environmental damage. The role of non-renewable energy sources in sustainable energy is also controversial. Nuclear power is a low-carbon source whose historic mortality rates are comparable to those of wind and solar, but its sustainability has been debated over concerns about radioactive waste, nuclear proliferation, and accidents. Similarly, transitioning from coal to natural gas does carry some environmental benefits, such as lower climate impact, but may lead to a delay in switching to more sustainable options. Furthermore, carbon capture and storage can be built into power plants to remove their CO2 emissions but is highly expensive.

This Special Issue will showcase multi- and cross-disciplinary studies that address the systemic and strategic implementation and application of renewable and sustainable energy generation. To achieve the long-term goals of a sustainable future and carbon neutrality, core systems and technologies must change rapidly and dramatically. Energy sustainability has many dimensions, including both production and utilization as well as the way in which they are connected to sustainable development. Sustainability also consists of three distinct aspects: economic, environmental, and social sustainability. Renewable energy generation and sustainable energy system development are highly researched and debated topics. Renewable energies can be harnessed economically and in an environmentally friendly manner to produce heat and electricity. Along with the declining costs of renewable energy technologies, the economic opportunities for the employment of renewable technologies are increasing globally. Therefore, reducing GHG emissions and fostering sustainability should both be considered as opportunities and societal priorities.

  • Renewable energy technologies.
  • Renewable energy applications and implementation.
  • Renewable energy policy, management, and governance.
  • Technical innovations and economic feasibility.
  • Marine and offshore energy systems.
  • Onshore energy systems.
  • Case studies encompassing renewable and sustainable energy development.
  • Sustainable energy systems in buildings.
  • Smart grids and microgrids for green electricity.
  • CO2 capture and storage.
  • Future high-capacity energy storages.

Dr. Sean Loughney
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

  • renewable energy generation
  • sustainable energy
  • technology implementation
  • renewable application areas
  • cross- and multi-discipline sustainability

Related Special Issue

Published Papers (6 papers)

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Research

15 pages, 394 KiB  
Article
Advancing the Sustainability of Risk Assessments within the Renewable Energy Sector—Review of Published Risk Assessments
by Mark Jenkins, Sean Loughney, Dante Benjamin Matellini and Jin Wang
Sustainability 2024, 16(6), 2446; https://doi.org/10.3390/su16062446 - 15 Mar 2024
Viewed by 532
Abstract
Repeated regulatory incident investigations demonstrate the insufficiency of company risk assessments and the vulnerabilities that this exposes to the business and its duty holders who are, ultimately, culpable for the subsequent legislative breaches. While the epistemology and taxonomy of the traditional risk assessment [...] Read more.
Repeated regulatory incident investigations demonstrate the insufficiency of company risk assessments and the vulnerabilities that this exposes to the business and its duty holders who are, ultimately, culpable for the subsequent legislative breaches. While the epistemology and taxonomy of the traditional risk assessment are well established, there is a paucity of information that allows the verification and validation of the risk assessment content. Using evidence-based methodologies such as Content Analysis, Thematic Analysis, and validating the outputs using a survey, it became possible to “reverse engineer” the risk assessment content. This analysis of the published risk assessments, kindly supplied by six different Renewable Energy businesses, established that deterministic and behavioristic risk management methodologies had been adopted. These methodologies permitted and guided the use of vague and imprecise terminology and phraseology, numerical inconsistencies resulting in data ossification, and flawed assumptions. This analysis enables the duty holders to make informed and rational judgements about the adequacy of the risk assessment documents, and the process that permitted and guided their creation. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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14 pages, 2522 KiB  
Article
Monthly Global Solar Radiation Model Based on Artificial Neural Network, Temperature Data and Geographical and Topographical Parameters: A Case Study in Spain
by Enrique González-Plaza, David García and Jesús-Ignacio Prieto
Sustainability 2024, 16(3), 1293; https://doi.org/10.3390/su16031293 - 02 Feb 2024
Cited by 1 | Viewed by 539
Abstract
Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific [...] Read more.
Solar energy plays an essential role in the current energy context to achieve sustainable development while supplying energy needs, creating jobs, and protecting the environment. Many solar radiation models have provided valid estimates at many different locations, using appropriate input variables for specific climatic conditions, but predictions are less accurate on a regional scale. Since radiometric weather stations are relatively dispersed, even in the most developed countries, it is interesting to develop indirect models based on measurements that are common in secondary network stations. This paper develops a monthly global solar radiation model based on a simple neural network structure, using temperature, geographical, and topographical data from 105 meteorological stations, representative of the whole of peninsular Spain. A hierarchical clustering procedure was employed to select the data used to train and validate the model. To avoid functional dependencies between parameters and variables, which hinder the generality of the model, all input and output variables are dimensionless. The estimates fit the 1260 monthly data with RRMSE values of about 6%, which improves results obtained previously, using regression models, and proves that simplicity is compatible with the generality and accuracy of a model, even in large regions with very varied characteristics. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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31 pages, 9504 KiB  
Article
On the Usage of Artificial Neural Networks for the Determination of Optimal Wind Farms Allocation
by Kleanthis Xenitidis, Konstantinos Ioannou, Georgios Tsantopoulos and Dimitrios Myronidis
Sustainability 2023, 15(24), 16938; https://doi.org/10.3390/su152416938 - 18 Dec 2023
Viewed by 1191
Abstract
Worldwide energy demand is constantly increasing. This fact, in combination with the ever growing need to reduce the energy production footprint on the environment, has led to the adoption of cleaner and more sustainable forms of energy production. Renewable Energy Sources (RES) are [...] Read more.
Worldwide energy demand is constantly increasing. This fact, in combination with the ever growing need to reduce the energy production footprint on the environment, has led to the adoption of cleaner and more sustainable forms of energy production. Renewable Energy Sources (RES) are constantly developing in an effort to increase their conversion efficiency and improve their life cycle. However, not all types of RES are accepted by the general public. Wind Turbines (WTs) are considered by many researchers as the least acceptable type of RES. This is mostly because of how their installation alters the surrounding landscape, produces noise and puts birds in danger when they happen to fly over the installation area. This paper aims to apply a methodology which, by using Rational Basis Function Neural Networks (RBFNN), is capable of investigating the criteria used for the installation locations of WTs in a transparent way. The results from the Neural Network (NN) will be combined with protected areas and the Land Fragmentation Index (LFI), in order to determine possible new installation locations with increased social acceptance and, at the same time, increased energy production. A case study of the proposed methodology has been implemented for the entire Greek territory, which is considered one of the most suitable areas for the installation of wind farms due to its particular geomorphology. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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18 pages, 6790 KiB  
Article
Large Eddy Simulation Inflow Generation Using Reduced Length Scales for Flows Past Low-Rise Buildings
by Ava Shahrokhi, Julien Berthaut-Gerentes, Lin Ma, Derek Ingham and Mohamed Pourkashanian
Sustainability 2023, 15(17), 12786; https://doi.org/10.3390/su151712786 - 24 Aug 2023
Viewed by 697
Abstract
When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in [...] Read more.
When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in the domain, their effect on the downstream flow field has not, as yet, been investigated. In this paper, we validate the effectiveness and accuracy of implementing a reduced turbulence integral length scale for inflow generation in LES results at the rooftop of low-rise buildings and develop a technique to estimate the real local length scales using simulation results. We measure the wind locally and calculate the turbulence length scales from the energy spectrum of the wind data and simulation data. According to these results, there is an excellent agreement between the length scale from simulation and measurement when they are scaled with their corresponding freestream/inlet value. These results indicate that a reduced integral length scale can be safely used for LES to provide a reliable prediction of the energy spectrum as well as the length scales around complex geometries. The simulation results were confidently employed to obtain the best location for a wind turbine installation on low-rise buildings. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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19 pages, 7791 KiB  
Article
An AC-DC LED Integrated Streetlight Driver with Power Factor Correction and Soft-Switching Functions
by Chun-An Cheng, Hung-Liang Cheng, Chien-Hsuan Chang, En-Chih Chang, Zheng-You Kuo, Cheng-Kuan Lin and Sheng-Hong Hou
Sustainability 2023, 15(13), 10579; https://doi.org/10.3390/su151310579 - 05 Jul 2023
Cited by 2 | Viewed by 929
Abstract
The use of light-emitting diodes (LEDs) in street lighting applications has been greatly welcomed with the current trends of energy saving, environmental protection, carbon reduction, and sustainable development. This paper presents a novel AC-DC LED integrated streetlight driver that combines an interleaved buck [...] Read more.
The use of light-emitting diodes (LEDs) in street lighting applications has been greatly welcomed with the current trends of energy saving, environmental protection, carbon reduction, and sustainable development. This paper presents a novel AC-DC LED integrated streetlight driver that combines an interleaved buck converter with a coupled inductor and a half-bridge series resonant converter with a full-bridge rectifier into a single-stage power conversion topology with power factor correction (PFC) and soft switching capabilities. The PFC is achieved by designing the coupling inductor in the interleaved buck converter sub-circuit in discontinuous conduction mode. In addition, the resonant tank in the half-bridge series resonant converter sub-circuit is designed to be similar to an inductive load, thus giving the power switch a zero-voltage switching (ZVS) function, decreasing switching losses and increasing the overall efficiency of the proposed circuit. A prototype circuit of the proposed LED integrated streetlight driver with a power rating of 165 W (235 V/0.7 A) and 110 V input utility voltage has been developed and tested. According to the measurement results, a power factor greater than 0.98, a total harmonic distortion coefficient of the input current less than 3%, and an efficiency greater than 89% were obtained in the AC-DC LED integrated streetlight driver. Therefore, the experimental results are satisfactory and demonstrate the functionality of the proposed AC-DC LED integrated streetlight driver. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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19 pages, 8911 KiB  
Article
Genetic Algorithms-Based Optimum PV Site Selection Minimizing Visual Disturbance
by Nikolaos Nagkoulis, Eva Loukogeorgaki and Michela Ghislanzoni
Sustainability 2022, 14(19), 12602; https://doi.org/10.3390/su141912602 - 04 Oct 2022
Cited by 2 | Viewed by 1732
Abstract
In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to [...] Read more.
In this paper, an integrated methodology is developed to determine optimum areas for Photovoltaic (PV) installations that minimize the relevant visual disturbance and satisfy spatial constraints associated with land use, as well as environmental and techno-economic siting factors. The visual disturbance due to PV installations is quantified by introducing and calculating the “Social Disturbance” (SDIS) indicator, whereas optimum locations are determined for predefined values of two siting preferences (maximum allowable PV locations—grid station distance and minimum allowable total coverage area of PV installations). Thematic maps of appropriate selected exclusion criteria are produced, followed by a cumulative weighted viewshed analysis, where the SDIS indicator is calculated. Optimum solutions are then determined by developing and employing a Genetic Algorithms (GAs) optimization process. The methodology is applied for the municipality of La Palma Del Condado in Spain for 100 different combinations of the two siting preferences. The optimization results are also employed to create a flexible and easy-to-use web-GIS application, facilitating policy-makers to choose the set of solutions that better fulfils their preferences. The GAs algorithm offers the ability to determine distinguishable, but compact, regions of optimum locations in the region, whereas the results indicate the strong dependence of the optimum areas upon the two siting preferences. Full article
(This article belongs to the Special Issue Sustainable Energy Systems and Renewable Generation)
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