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Electronics
Special Issues
Intelligent Control and Optimization Technologies in Power Generation Systems
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Intelligent Control and Optimization Technologies in Power Generation Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 1871

Special Issue Editors

Prof. Dr. Xi Chen

E-Mail Website
Guest Editor
School of Mechanical Engineering/New Energy Research Institute, Hunan Institute of Science and Technology, Yueyang 414006, China
Interests: fuel cell & hydrogen energy; renewable energy power system; energy system intelligent control and optimization
Dr. Jia Liu

E-Mail Website
Guest Editor
School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
Interests: net-zero energy buildings; renewable energy; energy storage
Dr. Yuxuan Ding

E-Mail Website
Guest Editor
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: electromagnetic transient analysis of power grid; digital twin technique; power system risk assessment; fault diagnosis of smart grids

Special Issue Information

Dear Colleagues,

With the rapid development of the global economy, the demand for energy is growing rapidly. Currently, renewable energy and traditional fossil energy constitute the primary bases of power provision. High energy conversion efficiency plays a key role in power generation systems and can both improve the power generation and decrease the pollutant emissions. More importantly, intelligent control and optimization technologies are efficient ways of improving the performance of power generation systems with low cost and high efficiency.

In this regard, this Special Issue aims to collect review and working articles from around the world which cover and illuminate the state of the art of development of energy power systems, renewable energy systems, intelligent control and optimization technologies, and their recent technological spread. We aim to present discussions of different types of applications for energy power systems based on intelligent control and optimization technologies, with areas such as artificial intelligent control, neural network, intelligent optimization, multi-objective optimization, machine learning, intelligent building, etc., featured heavily

The SI topics outlined above will have a large impact among colleagues from universities and academia in general, as well as scientists, policy makers, practitioners, and students in the fields of power system engineering, energy engineering, automotive engineering, etc.

This SI aims to cover recent developments in energy system control and optimization and deal with the continuous technology advances in the domain of intelligent modeling, control and evaluation for power generation system and electrical grids. The scope of this SI includes the following:

  1. power generation system
  2. renewable energy system
  3. fuel cell
  4. hydrogen power system
  5. solar energy power system
  6. energy storage devices
  7. batteries
  8. distributed energy resources
  9. intelligent modeling on energy system
  10. multi-objective evaluation and optimization
  11. machine learning and deep learning
  12. big data technology
  13. power transmission technologies
  14. building energy system
  15. new energy vehicles
  16. water and heat management in fuel cell
  17. dynamic control in new energy system
  18. robust control in new energy system
  19. digital twin technique

Prof. Dr. Xi Chen
Dr. Jia Liu
Dr. Yuxuan Ding
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. Electronics 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 power generation
  • fuel cell
  • intelligent control and optimization
  • smart grid
  • system optimization

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Published Papers (2 papers)

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Research

16 pages, 1341 KiB  
Article
Design of a PID Controller for Microbial Fuel Cells Using Improved Particle Swarm Optimization
by Chenlong Wang, Baolong Zhu, Fengying Ma and Jiahao Sun
Electronics 2024, 13(17), 3381; https://doi.org/10.3390/electronics13173381 - 26 Aug 2024
Viewed by 182
Abstract
The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable [...] Read more.
The microbial fuel cell (MFC) is a renewable energy technology that utilizes the oxidative decomposition processes of anaerobic microorganisms to convert the chemical energy in organic matter, such as wastewater, sediments, or other biomass, into electrical power. This technology is not only applicable to wastewater treatment but can also be used for resource recovery from various organic wastes. The MFC usually requires an external controller that allows it to operate under controlled conditions to obtain a stable output voltage. Therefore, the application of a PID controller to the MFC is proposed in this paper. The design phase for this controller involves the identification of three parameters. Although the particle swarm optimization (PSO) algorithm is an advanced optimization algorithm based on swarm intelligence, it suffers from issues such as unreasonable population initialization and slow convergence speed. Therefore, this paper proposes an improved particle swarm algorithm based on the Golden Sine Strategy (GSCPSO). Using Circle chaotic mapping to make the distribution of the initial population more uniform, and then using the Golden Sine Strategy to improve the position update formula, not only improves the convergence speed of the population but also enhances convergence precision. The GSCPSO algorithm is applied to execute the described design process. The results of the simulation show that the designed control method exhibits smaller steady-state error, overshoot, and chattering compared with sliding-mode control (SMC), backstepping control, fuzzy SMC (FSMC), PSO-PID, and CPSO-PID. Full article
(This article belongs to the Special Issue Intelligent Control and Optimization Technologies in Power Generation Systems)
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26 pages, 5835 KiB  
Article
Chaos Moth Flame Algorithm for Multi-Objective Dynamic Economic Dispatch Integrating with Plug-In Electric Vehicles
by Wenqiang Yang, Xinxin Zhu, Fuquan Nie, Hongwei Jiao, Qinge Xiao and Zhile Yang
Electronics 2023, 12(12), 2742; https://doi.org/10.3390/electronics12122742 - 20 Jun 2023
Cited by 1 | Viewed by 1105
Abstract
Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the [...] Read more.
Dynamic economic dispatch (DED) plays an important role in the operation and control of power systems. The integration of DED with space and time makes it a complex and challenging problem in optimal decision making. By connecting plug-in electric vehicles (PEVs) to the grid (V2G), the fluctuations in the grid can be mitigated, and the benefits of balancing peaks and filling valleys can be realized. However, the complexity of DED has increased with the emergence of the penetration of plug-in electric vehicles. This paper proposes a model that takes into account the day-ahead, hourly-based scheduling of power systems and the impact of PEVs. To solve the model, an improved chaos moth flame optimization algorithm (CMFO) is introduced. This algorithm has a faster convergence rate and better global optimization capabilities due to the incorporation of chaotic mapping. The feasibility of the proposed CMFO is validated through numerical experiments on benchmark functions and various generation units of different sizes. The results demonstrate the superiority of CMFO compared with other commonly used swarm intelligence algorithms. Full article
(This article belongs to the Special Issue Intelligent Control and Optimization Technologies in Power Generation Systems)
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