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Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World
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Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 9010

Special Issue Editors

Dr. Muhammad Shahzad Nazir

E-Mail Website
Guest Editor
Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China
Interests: renewable energy technologies; modelling; energy storage; optimization; sustainable energy
Dr. Sleiman Farah

E-Mail Website
Guest Editor
Department of Mechanical and Production Engineering, Aarhus University, 8000 Aarhus, Denmark
Interests: renewable energy technologies; energy storage; renewable systems modelling; optimization; forecasting

Special Issue Information

Dear Colleagues,

Electric power systems can be severely damaged by natural catastrophes such as typhoons, floods, earthquakes, and tornadoes. Some of these catastrophes are likely to increase in intensity and frequency because of global warming, which is accelerated by CO2 emissions. Electric power systems are also susceptible to cyber-attacks as the operation of these systems is increasingly depending on Internet communications. Natural and cyber damages to electric power systems can cause long unplanned power outages that can last for several weeks. Consequently, electric power systems must be resilient to offer continuous and consistent electric power supply to end customers and to ensure smart grid systems’ long-term economic viability. Sustainable smart energy systems through emerging electronics and automation technologies can achieve resilience through the innovation and deployment of smart sensors, as well as enhanced information and communication infrastructure. These new novel breakthroughs have prompted substantial research efforts to address the growing demand for resilient sustainable smart grid systems.

This Special Issue aspires to be an open platform for sharing knowledge regarding advances and difficulties in resilient sustainable smart energy grid systems against natural and cyber disasters in this environment. We invite novel contributions that address both theoretical and experimental elements of ideas, new developments, or mature investigations.

Topics of interest include, but are not limited to, the following aspects of sustainable energy technologies:

  • Renewable energy technologies.
  • Automation technology.
  • Environmental analysis and impact mitigation.
  • Carbon emission (CO2).
  • Multiple energy carriers, DC and hybrid microgrid, etc.
  • Sustainable energy design, planning, and operation.
  • Power quality, reliability, and resilience.
  • Topologies of converters for smart electric grids.
  • Smart metering and smart sensors.
  • Smart electric transportation in smart grid resilience.
  • Microgrids, community energy systems, remote power systems, or related entities.
  • Smart grid monitoring and management during extreme events.
  • Cyber-physical systems, IoT, and artificial intelligence (AI).
  • Natural disasters and component failure prediction.

Dr. Muhammad Shahzad Nazir
Dr. Sleiman Farah
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
  • energy storage
  • policy making
  • social acceptance and equity
  • energy island/communities
  • multiple energy carriers
  • power quality, reliability, and resilience
  • environmental analysis and impact mitigation
  • sustainability

Published Papers (5 papers)

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Research

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25 pages, 5170 KiB  
Article
Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM
by Yuanhua Chen, Muhammad Shoaib Bhutta, Muhammad Abubakar, Dingtian Xiao, Fahad M. Almasoudi, Hamad Naeem and Muhammad Faheem
Sustainability 2023, 15(11), 8555; https://doi.org/10.3390/su15118555 - 25 May 2023
Cited by 10 | Viewed by 1955
Abstract
The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy [...] Read more.
The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems. Full article
(This article belongs to the Special Issue Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World)
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13 pages, 4523 KiB  
Article
Fluorination Treatment and Nano-Alumina Concentration on the Direct Current Breakdown Performance & Trap Levels of Epoxy/Alumina Nanocomposite for a Sustainable Power System
by Muhammad Zeeshan Khan, Muhammad Shahzad Nazir, Muhammad Shoaib Bhutta and Feipeng Wang
Sustainability 2023, 15(7), 5826; https://doi.org/10.3390/su15075826 - 27 Mar 2023
Cited by 1 | Viewed by 1147
Abstract
Epoxy resin is extensively used in gas insulated switches as a renewable energy coating due to its exceptional insulation, mechanical characteristics, and environmental friendliness. The higher resistivity of the epoxy resin causes numerous surface charges to accumulate on the surface of the epoxy [...] Read more.
Epoxy resin is extensively used in gas insulated switches as a renewable energy coating due to its exceptional insulation, mechanical characteristics, and environmental friendliness. The higher resistivity of the epoxy resin causes numerous surface charges to accumulate on the surface of the epoxy resin as a result of carrier injection due to the high DC electric field, which may cause insulation failure of the power transmission system. In this study, various concentrations of epoxy resins blended with nano-alumina (nano-Al2O3) at 0 wt%, 1 wt%, 3 wt%, and 5 wt% were created. Afterwards, the epoxy resin and Al2O3 nanocomposites were fluorinated by utilizing a combination of F2 and N2 with a ratio of 20% F2 at 0.05 MPa while maintaining the temperature at 40 °C. In order to improve dispersion, nano-Al2O3 was treated with a silane coupling agent called γ-aminopropyltriethoxysilane (KH550). Additionally, infrared spectroscopy based on the Fourier transform was used to investigate the structure of chemical bonds. Furthermore, the changes in the molecular chains were verified by the FTIR spectra. The DC breakdown strength of epoxy resin\Al2O3 nano-composites showed that breakdown strength significantly improved after gas-phase fluorination. Moreover, 1 wt% nano-Al2O3 showed a higher breakdown strength. The fluorinated layer had a charge-suppressing effect, reducing the charge injected into the polymer matrix of the epoxy-resin matrix and increasing its DC breakdown capability. Thermally stimulated current (TSC) measurements indicate that epoxy resin’s trap energy and trap density are altered by nano-Al2O3 incorporation and fluorination treatment (gas-phase). It was also observed that introducing nano-Al2O3 at a lower concentration (e.g., 1 wt%) can hinder the growth of space charge in the polymer matrix of the epoxy resin, thus enhancing the deep traps’ energy. Furthermore, a fluorination layer containing a strong polarization of C-F bonding would seize the charge injection from electrodes, thus decreasing the conductivity and suppressing the charge injection. Full article
(This article belongs to the Special Issue Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World)
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29 pages, 7308 KiB  
Article
The Potential Role of Hybrid Renewable Energy System for Grid Intermittency Problem: A Techno-Economic Optimisation and Comparative Analysis
by Muhammad Paend Bakht, Zainal Salam, Mehr Gul, Waqas Anjum, Mohamad Anuar Kamaruddin, Nuzhat Khan and Abba Lawan Bukar
Sustainability 2022, 14(21), 14045; https://doi.org/10.3390/su142114045 - 28 Oct 2022
Cited by 9 | Viewed by 1600
Abstract
The renewed interest for power generation using renewables due to global trends provides an opportunity to rethink the approach to address the old yet existing load shedding problem. In the literature, limited studies are available that address the load shedding problem using a [...] Read more.
The renewed interest for power generation using renewables due to global trends provides an opportunity to rethink the approach to address the old yet existing load shedding problem. In the literature, limited studies are available that address the load shedding problem using a hybrid renewable energy system. This paper aims to fill this gap by proposing a techno-economic optimisation of a hybrid renewable energy system to mitigate the effect of load shedding at the distribution level. The proposed system in this work is configured using a photovoltaic array, wind turbines, an energy storage unit (of batteries), and a diesel generator system. The proposed system is equipped with a rule-based energy management scheme to ensure efficient utilisation and scheduling of the sources. The sizes of the photovoltaic array, wind turbine unit, and the batteries are optimised via the grasshopper optimisation algorithm based on the multi-criterion decision that includes loss of power supply probability, levelised cost of electricity, and payback period. The results for the actual case study in Quetta, Pakistan, show that the optimum sizes of the photovoltaic array, wind turbines, and the batteries are 35.75 kW, 10 kW, and 28.8 kWh, respectively. The sizes are based on the minimum values of levelised cost of electricity (6.64 cents/kWh), loss of power supply probability (0.0092), and payback period (7.4 years). These results are compared with conventional methods (generators, uninterruptible power supply, and a combined system of generator and uninterruptible power supply system) commonly used to deal with the load shedding problem. The results show that the renewable based hybrid system is a reliable and cost-effective option to address grid intermittency problem. Full article
(This article belongs to the Special Issue Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World)
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16 pages, 5398 KiB  
Article
A Robust Economic Framework for Integrated Energy Systems Based on Hybrid Shuffled Frog-Leaping and Local Search Algorithm
by Ahmed N. Abdalla, Yongfeng Ju, Muhammad Shahzad Nazir and Hai Tao
Sustainability 2022, 14(17), 10660; https://doi.org/10.3390/su141710660 - 26 Aug 2022
Cited by 9 | Viewed by 1320
Abstract
The safe and efficient operation of the integrated energy system is severely hampered by a number of unpredictable elements, such as the output of renewable energy sources, the cost of energy purchases, and full demand response (IES). The effectiveness and excellence of the [...] Read more.
The safe and efficient operation of the integrated energy system is severely hampered by a number of unpredictable elements, such as the output of renewable energy sources, the cost of energy purchases, and full demand response (IES). The effectiveness and excellence of the integrated energy system scheduling method can be increased with advanced modeling of unpredictable aspects. Thus, the IES robust stochastic optimisation model is constructed and solved with the hybrid shuffled frog-leaping and local search (HSFLA–LS) algorithm. Finally, a simulation analysis considering the uncertainty of energy purchase price based on the hybrid SFLA–LS algorithm is reduced by USD 1.63 (0.64%) and USD 3.34 (1.3%), compared to PSO and GA, respectively. In addition, the time taken to execute the SFLA–LS algorithm for the program is reduced by 1.886 s (1.59%), and 3.117 s (2.7%), compared to PSO and GA, respectively. The findings demonstrate that the suggested approach can lower system running expenses, and achieve the coordination and optimization of economy and robustness. Full article
(This article belongs to the Special Issue Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World)
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Review

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28 pages, 5040 KiB  
Review
Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions
by Muhammad Asif, Hang Shen, Chunlin Zhou, Yuandong Guo, Yibo Yuan, Pu Shao, Lan Xie and Muhammad Shoaib Bhutta
Sustainability 2023, 15(10), 8298; https://doi.org/10.3390/su15108298 - 19 May 2023
Cited by 3 | Viewed by 2067
Abstract
Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, [...] Read more.
Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted. Full article
(This article belongs to the Special Issue Energy Technologies, Challenges and Solutions for a Sustainable (Energy) World)
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