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Intelligent Systems and Renewable/Sustainable Energy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 5022

Special Issue Editors

Prof. Dr. Jerry Chun Wei Lin

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Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: AI/DL/ML; big data analytics; optimization; IoTs; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Chunwei Tian

E-Mail Website
Guest Editor
School of Software, Northwestern Polytechnical University, Xi’an, China
Interests: image restoration; image recognition and deep learning
Prof. Marcin Fojcik

E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: communication system; welfare technology; production systems; sustainable development
Prof. Dr. Rafał Cupek

E-Mail Website
Guest Editor
Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Gliwice, Poland
Interests: control system; communication; artificial intelligence; industry 4.0

Special Issue Information

Dear Colleagues,

According to the European Green Deal and the European Climate Law, the European Commission has proposed to reduce greenhouse gas emissions by 55 percent by 2030 and reach zero emissions by 2050. In addition, human development requires a significant amount of energy, especially in the era of Industry 4.0. Energy demand is increasing in response to the growing number of problems related to environmental degradation and the energy crisis. However, the energy on our planet is finite and limited. Therefore, the production and application of renewable/sustainable energy are particularly important.

Renewable/sustainable energy will be an essential component of energy production and consumption in coming years, as more and more emphasis is placed on carbon neutrality. The share of renewable/sustainable energy in the world's energy supply is increasing, which is good for both the environment and sustainable development. Figuring out how to use evolving technology to intelligent energy systems that reduce energy consumption and protect the environment is critical. In particular, big data and deep learning strategies are especially trusted options for optimizing and monitoring energy consumption. For this reason, this Special Issue focuses on applied science alongside smart systems and sustainable energy to bring together the research achievements of researchers from academia and industry. Topics of interest include, but are not limited to, the following:

  • Optimization and forecasting of renewable/sustainable energy resources;
  • Innovations in smart grids and microgrids;
  • Emerging renewable/sustainable energy techniques;
  • Impacts of the renewable/sustainable energy techniques in environments;
  • Automated guided vehicle techniques in renewable/sustainable power systems;
  • Smart and intelligent grids for renewable and sustainable energy;
  • Energy applicability in power quality, grid stability, and renewable and sustainable flexibility;
  • AI/DL/ML applications in renewable/sustainable energy management systems.

Prof. Dr. Jerry Chun Wei Lin|
Dr. Chunwei Tian
Prof. Marcin Fojcik
Prof. Dr. Rafał Cupek
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. Applied Sciences 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.

Published Papers (4 papers)

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Research

13 pages, 1668 KiB  
Article
Data Collecting and Monitoring for Photovoltaic System: A Deep-Q-Learning-Based Unmanned Aerial Vehicle-Assisted Scheme
by Hao Zhang, Yuanlong Liu, Jian Meng, Yushun Yao, Hao Zheng, Jiansong Miao and Rentao Gu
Appl. Sci. 2023, 13(21), 11613; https://doi.org/10.3390/app132111613 - 24 Oct 2023
Viewed by 677
Abstract
Nowadays, massive photovoltaic power stations are being integrated into grid networks. However, a large number of photovoltaic facilities are located in special areas, which presents difficulties in management. Unmanned Aerial Vehicle (UAV)-assisted data collection will be a prospective solution for photovoltaic systems. In [...] Read more.
Nowadays, massive photovoltaic power stations are being integrated into grid networks. However, a large number of photovoltaic facilities are located in special areas, which presents difficulties in management. Unmanned Aerial Vehicle (UAV)-assisted data collection will be a prospective solution for photovoltaic systems. In this paper, based on Deep Reinforcement Learning (DRL), we propose a UAV-assisted scheme, which could be used in scenarios without awareness of sensor nodes’ (SNs) precise locations and has better universality. The optimized data collection work was formulated as a Markov Decision Process (MDP), and the approximate optimal policy was found by Deep Q-Learning (DQN). The simulation results show efficiency and convergence and demonstrate the effectiveness of the proposed scheme compared with other benchmarks. Full article
(This article belongs to the Special Issue Intelligent Systems and Renewable/Sustainable Energy)
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14 pages, 638 KiB  
Article
Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
by Hao Zhang, Fuhao Liu, Baichao Ma, Shengfang Zhang, Yuxin Peng, Rentao Gu and Jiansong Miao
Appl. Sci. 2023, 13(21), 11611; https://doi.org/10.3390/app132111611 - 24 Oct 2023
Viewed by 902
Abstract
To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic [...] Read more.
To cope with climate change and other environmental problems, countries and regions around the world have begun to pay attention to the development of renewable energy under the drive of achieving the global carbon emission peak and carbon neutrality goal. The distributed photovoltaic (PV) power grid is an effective solution that can utilize solar energy resources to provide clean a energy supply. However, with the continuous grid connection of distributed energy, it poses great challenges to the power supply stability and security of the grid. Therefore, it is particularly important to promote the local consumption of distributed energy and the construction of the energy internet. This paper aims to study the cooperative operation and energy optimization scheduling problem among distributed PV power grids, and proposes a new scheme to reduce the electricity cost under the constraint of power supply and demand balance. The optimization problem is modeled as a Markov decision process (MDP), and a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used to solve the MDP problem. Simulation results show that the proposed algorithm outperforms other benchmark algorithms in terms of reducing electricity cost, convergence and stability, and verifies its effectiveness. Full article
(This article belongs to the Special Issue Intelligent Systems and Renewable/Sustainable Energy)
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19 pages, 3602 KiB  
Article
Genetic-Algorithm-Driven Parameter Optimization of Three Representative DAB Controllers for Voltage Stability
by Wenjie Du and Wenjie Chen
Appl. Sci. 2023, 13(18), 10374; https://doi.org/10.3390/app131810374 - 16 Sep 2023
Viewed by 1137
Abstract
In the process of integrating renewable energy sources into DC microgrids, the isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in the microgrid can affect system stability. This study focuses on using a Genetic Algorithm to optimize the parameters [...] Read more.
In the process of integrating renewable energy sources into DC microgrids, the isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in the microgrid can affect system stability. This study focuses on using a Genetic Algorithm to optimize the parameters of three typical DAB controllers (PI controller based on pole placement, sliding mode controller, and model predictive controller) with the aim of improving voltage stability, especially during sudden load drops. The results demonstrate that controllers optimized using Genetic Algorithm outperform the methods of pole placement and traditional manual tuning significantly. For the PI controller, the maximum drop rate reduced from 8.00% to 4.00%. The phase margin increased from 123° to 126°. In the case of the sliding mode controller, the maximum drop rate decreased from 7.50% to 5.00%. The phase margin increased from 127° to 155°. As for the model predictive controller, the maximum drop rate reduced from 1.00% to 0.70%. The gain margin increased from 25.8 dB to 26.2 dB. These results highlight the potential of using the Genetic Algorithm in optimizing control parameters, offering the prospect of improving the performance and stability of DC–DC converters. Full article
(This article belongs to the Special Issue Intelligent Systems and Renewable/Sustainable Energy)
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16 pages, 10922 KiB  
Article
Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications
by Shaochen Ma, Kaixun He and Xin Peng
Appl. Sci. 2023, 13(17), 9867; https://doi.org/10.3390/app13179867 - 31 Aug 2023
Cited by 1 | Viewed by 956
Abstract
In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal [...] Read more.
In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations may lead to low combustion efficiency of boilers, cause disturbances to the normal operation of thermal power units, increased energy waste and carbon emissions, and even lead to serious explosion accidents. Therefore, the online identification of coal types for thermal power units is of great significance. To obtain the precise type of coal online, in the present work, a data-driven coal identification method is proposed based on convolutional networks that do not necessitate additional hardware detection equipment and are easy to implement. Experimental results indicate that the proposed method exhibits superior performance in comparison to traditional methods, thus ultimately improving the performance of thermal power plant. Full article
(This article belongs to the Special Issue Intelligent Systems and Renewable/Sustainable Energy)
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