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Sustainable Development Goals (SDGs): The Challenges in Achieving Clean Energy Worldwide

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

Deadline for manuscript submissions: closed (8 August 2023) | Viewed by 4847

Special Issue Editors

Dr. Abdollah Kavousi-Fard

E-Mail Website
Guest Editor
Electrical and Electronics Engineering Department, Shiraz University of Technology, Shiraz 71946-84334, Fars, Iran
Interests: strategic management; renewable energy sources; artificial intelligence; AI; data mining
Dr. Morteza Dabbaghjamanesh

E-Mail Website
Guest Editor
1. Smart Power Tech LLC, Dallas, TX 77056, USA
2. Design and Optimization of Energy Systems (DOES) Laboratory, The University of Texas at Dallas, Richardson, TX 75080, USA
Interests: power system operation; renewable energy sources; cybersecurity analysis; machine learning; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable development is a key concept for the enhancement of human life quality in the present without compromising the capability of future generations to meet their own needs. Considering the challenges facing sustainable energy management, it is more than before perceived that without a clear and strategic plan, there is no way to reach those goals. In this way, not only efficient modeling and management methods, but also data managing and mining approaches can help to pave the way toward sustainable development based on renewable resources. In this regard, strategic management can greatly help in finding more reliable, secure and efficient decision-making in the future grids including the smart grid, microgrids and smart cities. Therefore, this Special Issue focuses on sustainable development goals (SDGs) and the upcoming challenges in achieving clean energy worldwide. To this end, new concepts, methods, modifications and hybrid solutions that can support the idea of sustainable development toward a clean energy system are welcome. This Special Issue invites authors from both industry and academia to submit original research works to this Special Issue with a special focus on sustainable development with applications on:

  • Strategic decision-making toward a cleaner environment.
  • Strategic operation of the renewable-based energy systems.
  • Modeling, operation and management of the smart grids and smart buildings.
  • Planning and design of the distribution systems.
  • Operation, modeling and management of the smart city.
  • Optimal modeling and scheduling of the microgrids.
  • Smart citizens in the smart cities.
  • Human health in the clean energy systems.

We look forward to receiving your contributions.

Dr. Abdollah Kavousi-Fard
Dr. Morteza Dabbaghjamanesh
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

  • sustainable development
  • smart city
  • clean energy
  • smart citizen
  • data mining

Published Papers (3 papers)

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Research

16 pages, 2818 KiB  
Article
Analyzing the Progress of China and the World in Achieving Sustainable Development Goals 7 and 13
by Md Altab Hossin, Shuwen Xiong, David Alemzero and Hermas Abudu
Sustainability 2023, 15(19), 14115; https://doi.org/10.3390/su151914115 - 23 Sep 2023
Cited by 1 | Viewed by 1933
Abstract
Achieving Sustainable Development Goal 7 (SDG 7) and SDG 13 together requires a holistic and integrated approach to simultaneously address the challenges of clean energy and climate action. In order to find integrated policy strategies, this study offers a comparative analysis using the [...] Read more.
Achieving Sustainable Development Goal 7 (SDG 7) and SDG 13 together requires a holistic and integrated approach to simultaneously address the challenges of clean energy and climate action. In order to find integrated policy strategies, this study offers a comparative analysis using the case of China and the world regarding energy access, energy intensity, clean cooking, renewable energy, global warming gases, and investment in energy by the private sector to advance SDGs 7 and 13, applying a principal component regression (PCR) and forecasting models for the period 1990 to 2021. Overall, these findings indicate that China is making significant progress towards meeting the goals of the Paris Agreement. This progress is evident in the notable variations observed in key variables such as access to clean cooking solutions, private sector investments in energy, renewable energy generation, and enhanced energy efficiency. In contrast, the global landscape exhibits only minimal fluctuations in these aspects within its framework. The PCR proves that all the components are significant regarding China, whereas, for the world, seven components are significant out of eight. Furthermore, the global temperature projection indicates that the world is nearing the 1-degree Celsius threshold, with the current temperature standing at 0.558 degrees Celsius. This suggests that the goal of limiting global warming to 1.5 degrees Celsius by 2030 remains attainable. Notably, China’s projected average temperature for 2030 is 7.2 degrees Celsius, marking a 12% decrease from the 2021 temperature level. This trajectory aligns with China’s commitment to achieving the 1.5-degree Celsius target by 2030. This study makes a valuable contribution to the field of energy transition, offering insights into the path to maintaining global warming at 1.5 degrees Celsius as stipulated by the Paris Agreement by 2030. Full article
(This article belongs to the Special Issue Sustainable Development Goals (SDGs): The Challenges in Achieving Clean Energy Worldwide)
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20 pages, 1906 KiB  
Article
Hybrid Machine Learning and Modified Teaching Learning-Based English Optimization Algorithm for Smart City Communication
by Xing Liu, Xiaojing Zhang and Aliasghar Baziar
Sustainability 2023, 15(15), 11535; https://doi.org/10.3390/su151511535 - 26 Jul 2023
Viewed by 891
Abstract
This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The [...] Read more.
This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems, which face challenges due to their high population density. The proposed algorithm integrates the strengths of machine learning techniques, more specifically, the long short-term memory (LSTM) technique, with teaching learning-based optimization algorithms. To achieve optimization, the algorithm learns from historical data on energy consumption and communication patterns specific to the modeled system. By leveraging these insights, it can predict future energy consumption and communication patterns accurately. Additionally, the algorithm incorporates a modified teaching learning-based optimization approach inspired by the teaching and learning process in classrooms. It adjusts the system parameters based on feedback received from the system, thereby optimizing both energy consumption and communication systems. The effectiveness of the proposed algorithm is evaluated through a case study conducted on the test system, where historical data on energy consumption and communication patterns are analyzed. The results demonstrate that the algorithm efficiently optimizes the communication and energy management systems, leading to substantial energy savings and improved communication efficiency within the test system. In conclusion, this study presents a hybrid machine learning and modified teaching learning-based optimization algorithm that effectively addresses the communication and energy management challenges in the test system. Moreover, this algorithm holds the potential for application in various smart city domains beyond the test system. The findings of this research contribute to the advancement of smart city technologies and offer valuable insights into reducing energy consumption in densely populated urban areas. Full article
(This article belongs to the Special Issue Sustainable Development Goals (SDGs): The Challenges in Achieving Clean Energy Worldwide)
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16 pages, 2690 KiB  
Article
Towards Sustainable Energy Systems Considering Unexpected Sports Event Management: Integrating Machine Learning and Optimization Algorithms
by Lei Zhang and Ying Yang
Sustainability 2023, 15(9), 7186; https://doi.org/10.3390/su15097186 - 26 Apr 2023
Cited by 1 | Viewed by 1649
Abstract
This paper proposes a novel approach for achieving sustainable energy systems in unexpected sports event management by integrating machine learning and optimization algorithms. Specifically, we used reinforcement learning for peak load forecasting and bat evolutionary algorithm for optimization, since the energy management problem [...] Read more.
This paper proposes a novel approach for achieving sustainable energy systems in unexpected sports event management by integrating machine learning and optimization algorithms. Specifically, we used reinforcement learning for peak load forecasting and bat evolutionary algorithm for optimization, since the energy management problem in sports events is typically non-linear. Machine learning algorithms, specifically reinforcement learning, are used to analyze historical data and provide accurate peak load forecasts. This information can then be used to optimize energy consumption during the event through the use of algorithms such as the bat evolutionary algorithm, which can effectively solve non-linear optimization problems. The integration of these algorithms in unexpected sports event management can lead to significant improvements in sustainability and cost-effectiveness. This paper presents a case study of the implementation of reinforcement learning and bat evolutionary algorithms in an unexpected sports event management scenario, demonstrating the effectiveness of the proposed approach in achieving sustainable energy systems and reducing overall energy consumption. Overall, this paper provides a roadmap for integrating machine learning and optimization algorithms, such as reinforcement learning and bat evolutionary algorithm, in unexpected sports event management to achieve sustainable energy systems, promoting a more sustainable future for the sports event industry and the planet as a whole. Full article
(This article belongs to the Special Issue Sustainable Development Goals (SDGs): The Challenges in Achieving Clean Energy Worldwide)
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