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Energies
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Soft Computing Applications in Electric Power Networks
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Soft Computing Applications in Electric Power Networks

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5883

Special Issue Editors

Dr. Mohamed A. Tolba

E-Mail Website
Guest Editor
1. Reactors Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo 11787, Egypt
2. Electrical Power Systems Department, Moscow Power Engineering Institute, 111250 Moscow, Russia
Interests: power quality management; distributed generators; control systems; power system operation; smart grids; renewable energy systems; heuristic optimization techniques
Prof. Dr. Ali M. Eltamaly

E-Mail Website
Guest Editor
Electrical Engineering Department, Mansura University, Mansura 35516, Egypt
Interests: renewable energy; smart grids; power electronics; motor drives; power quality; artificial intelligence; evolutionary and heuristic optimization techniques; distributed generation
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed A. Zaki Diab

E-Mail Website
Guest Editor
Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia 61517, Egypt
Interests: renewable energy systems; power electronics; machines drives; smart grids; evolutionary; heuristic optimization techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Vladimir N. Tulsky

E-Mail Website
Guest Editor
Electrical Power Systems Department, Moscow Power Engineering Institute, 111250 Moscow, Russia
Interests: power quality management; renewable energy systems, power electronics; smart grids; evolutionary; control systems; power system operation; optimization techniques

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of the Energies journal on “Soft Computing Applications in Electric Power Networks”. Recently, soft computing applications, as a part of artificial intelligence, have attracted many researchers and engineers to overcome the difficult problems that result from an enormous number of different applications in the electrical power networks field. Soft computing methodologies are applied to electrical power-related issues with the tasks of data analysis such as optimization, prediction, classification, etc. These issues can be connected with new approaches for the operation and planning of distributed power generation, renewable energy systems’ management, enormous power system applications related to smart grids, etc. In this Special Issue, we welcome articles that exchange views about the novel methodologies-based applications related to electrical power networks, providing sufficient analysis and solutions to the difficult problems in such applications. Moreover, additional applications related to soft computing methodologies can be considered based on the problems related to renewable energy systems (solar, thermoelectric, wind, fuel cell, etc.), power operation management, microgrid design (with renewable and non-renewable generation), etc.

Topics of interest include, but are not limited to, the following:

  • Distributed power generation modeling;
  • Optimization of operation of power systems;
  • Integration of distributed generation in distribution systems and smart grids;
  • Impact of distributed energy resources and storage devices on power system operation;
  • Distributed power generation expansion planning;
  • Application of AI methodologies for an improved power system operation;
  • Optimal scheduling of distributed power generation;
  • Integration and impact of electric vehicles;
  • Distributed generation in a transactive energy framework;
  • Control topologies of power electronics.

Dr. Mohamed A. Tolba
Prof. Dr. Ali M. Eltamaly
Dr. Ahmed A. Zaki Diab
Dr. Vladimir N. Tulsky
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

  • distributed energy resources
  • renewable energy systems
  • power quality
  • storage systems
  • electric vehicles
  • power electronics
  • protection schemes and devices
  • operation, planning, and economics
  • smart grid
  • microgrid

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Published Papers (2 papers)

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Research

29 pages, 6904 KiB  
Article
Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm
by Mohammed Hamouda Ali, Ali M. El-Rifaie, Ahmed A. F. Youssef, Vladimir N. Tulsky and Mohamed A. Tolba
Energies 2023, 16(2), 846; https://doi.org/10.3390/en16020846 - 11 Jan 2023
Cited by 31 | Viewed by 2194
Abstract
The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become [...] Read more.
The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimization algorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimization algorithm (COOT), golden jackal optimization algorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimization algorithm (SCSO), and the skills optimization algorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition, the results indicate that POA is capable of identifying the most appropriate worldwide solutions. It is also successfully investigating preferred search locations, ensuring a fast convergence speed and enhancing the search engine’s capabilities. Full article
(This article belongs to the Special Issue Soft Computing Applications in Electric Power Networks)
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19 pages, 3074 KiB  
Article
Robust Fractional MPPT-Based Moth-Flame Optimization Algorithm for Thermoelectric Generation Applications
by Hegazy Rezk, Magdy M. Zaky, Mohemmed Alhaider and Mohamed A. Tolba
Energies 2022, 15(23), 8836; https://doi.org/10.3390/en15238836 - 23 Nov 2022
Cited by 6 | Viewed by 1602
Abstract
Depending on the temperature difference between the hot and cold sides of the thermoelectric generator (TEG), the output performance of the TEG can be produced. This means that it is necessary to force a TEG based on robust maximum power point tracking (MPPT) [...] Read more.
Depending on the temperature difference between the hot and cold sides of the thermoelectric generator (TEG), the output performance of the TEG can be produced. This means that it is necessary to force a TEG based on robust maximum power point tracking (MPPT) to operate close to its MPP at any given temperature or load. In this paper, an improved fractional MPPT (IFMPPT) is proposed in order to increase the amount of energy that can be harvested from TEGs. According to the suggested method, fractional order control is used. A moth-flame optimizer (MFO) was used to determine IFMPPT’s optimal parameters. A comparison of the results obtained by the MFO is made with those obtained by a particle swarm optimizer, genetic algorithm, gray wolf optimizer, seagull optimization algorithm, and tunicate swarm algorithm in order to demonstrate MFO’s superiority. IFMPPT’s primary objective is to enhance dynamic responses and exclude steady-state oscillations. Consequently, incremental resistance and perturb and observe are compared with the proposed strategy’s performance. It was revealed that IFMPPT provides superior tracking results both in dynamic and steady-state conditions when compared with traditional methods. Full article
(This article belongs to the Special Issue Soft Computing Applications in Electric Power Networks)
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