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Energies
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Clean and Sustainable Energy with Artificial Intelligence
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Clean and Sustainable Energy with Artificial Intelligence

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B2: Clean Energy".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1405

Special Issue Editors

Prof. Dr. Hwai Chyuan Ong

E-Mail Website
Guest Editor
Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan
Interests: energy and fuel; renewable energy; environmental sustainability; biomass energy; waste management; green technology
Special Issues, Collections and Topics in MDPI journals
Dr. Kai Ling Yu

E-Mail Website
Guest Editor
Tan Sri Leo Moggie Distinguished Chair in Energy Informatics, Institute of Informatics and Computing in Energy (IICE), Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia
Interests: green technology; microalgae; bioenergy; environmental management
Dr. Hoang Chinh Nguyen

E-Mail Website
Guest Editor
School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia
Interests: biofuel production; biomass conversion; process optimization; biochemical engineering; biorefinery; green technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the current energy and climate change issues, there is a need for attention to look for alternative energy where renewable and clean energy sources serve an important role in the energy transition toward net zero emission. However, the innovation-as-usual is no longer enough for clean energy technologies to become available. Moving towards the era of digitalization, advanced energy technology integrated with Artificial Intelligence (AI), such as machine learning, deep learning algorithms, big data handling, etc, can ease the operation and management of the energy industry. Currently, most of the energy research focuses on renewable energy such as biofuel, hydrogen, wind, solar, biomass energy/bioenergy, bioelectricity, etc, towards the development of new energy materials, energy conversion and management, sustainable energy system design, optimization, distribution, and policy. Thus, saving time and cost in clean energy investment, supporting better energy management and helping clean energy innovations to get into the market faster and more efficiently.

This Special Issue aims to highlight the recent developments in clean and sustainable energy applications by utilizing Artificial Intelligence (AI) technology. Research topics for this Special Issue focus on clean energies such as biodiesel, biogas, bioethanol, hydrogen, wind, solar, energy-related materials, and other sustainable energy and the application of AI in the energy system. The original research and review articles are welcome. We look forward to receiving your contributions.

Prof. Dr. Hwai Chyuan Ong
Dr. Kai Ling Yu
Dr. Hoang Chinh Nguyen
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. Energies 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 2600 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

  • clean energy and sustainable energy
  • bioenergy and biofuel
  • hydrogen energy
  • renewable energy
  • waste to energy
  • energy informatics
  • energy simulation, modelling and optimization
  • application of Artificial Intelligence and machine learning in energy system
  • energy efficiency and energy management
  • energy and environment sustainability
  • novel energy materials
  • smart city

Published Papers (1 paper)

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Research

22 pages, 6116 KiB  
Article
Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems
by Ali M. Eltamaly, Zeyad A. Almutairi and Mohamed A. Abdelhamid
Energies 2023, 16(13), 5228; https://0-doi-org.brum.beds.ac.uk/10.3390/en16135228 - 07 Jul 2023
Cited by 3 | Viewed by 926
Abstract
Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship [...] Read more.
Due to the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade conditions (PSCs). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPTs) may become locked in one of the LPs, significantly reducing the PV system’s generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms’ key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA’s convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA’s superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time was reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, the best initialization of search agents, and the optimal design of the dc–dc converter are introduced for optimal MPPT performance. Full article
(This article belongs to the Special Issue Clean and Sustainable Energy with Artificial Intelligence)
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