Special Issue "Intelligent Forecasting and Optimization in Electrical Power Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Electrical Power System".

Deadline for manuscript submissions: 15 January 2022.

Special Issue Editors

Prof. Dr. Paweł Piotrowski
E-Mail Website
Guest Editor
Electrical Power Engineering Institute, Warsaw University of Technology (WUT), Koszykowa 75 Street, 00-661 Warszawa, Poland
Interests: artificial intelligence; machine learning; forecasting; optimization; power engineering
Prof. Dr. Grzegorz Dudek
E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Czestochowa University of Technology, 42-201 Częstochowa, Poland
Interests: machine learning; data mining; artificial intelligence; pattern recognition, evolutionary computation, and their application to classification, regression, forecasting and optimization problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dariusz Baczyński
E-Mail Website
Guest Editor
Electrical Power Engineering Institute, Warsaw University of Technology (WUT), Koszykowa 75 Street, 00-661 Warszawa, Poland
Interests: artificial neural networks; computational intelligence; optimization; forecasting; evolutionary algorithms; swarm intelligence

Special Issue Information

Dear Colleagues,

This Special Issue focuses on applications of artificial intelligence and machine learning models (including hybrid and ensembles methods) for forecasting and optimization in power engineering. ML and AI are one of the most exciting fields of computing today. These methods are effective and popular in regression problems, including forecasting and optimization. Effective operation of electrical power systems of various sizes (including microgrids) require precise short-term forecasts of both electricity generation in Renewable Energy Systems and electricity demand.

The ability to precise forecast electricity generation for example  by a wind farms and solar power plants is very important because RES often creates problems for networks managed by distribution system operators. Forecasts of generation in RES are also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage.

This Special Issue solicits original papers and review articles that present new research results in forecasting and optimization in electrical power systems. 

 Expected topics include, but are not limited to:

  • Artificial intelligence/machine learning/deep learning for forecasting of electricity generation in RES,
  • Artificial intelligence/machine learning/deep learning for forecasting of power demand in electrical power systems
  • Optimization of electrical power systems,
  • Forecasting of meteorological data (wind speed, solar radiation) important to forecast electricity generation in RES
  • Statistical analysis of data for forecasting models (including problems of big, missing, distorted and uncertain data),  
  • Reliability of electrical power systems.

Prof. Dr. Paweł Piotrowski
Prof. Dr. Grzegorz Dudek
Prof. Dr. Dariusz Baczyński
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 papers will be 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 2000 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 (1 paper)

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Research

Article
Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator
Energies 2021, 14(19), 6351; https://doi.org/10.3390/en14196351 - 05 Oct 2021
Viewed by 377
Abstract
The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is [...] Read more.
The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids. Full article
(This article belongs to the Special Issue Intelligent Forecasting and Optimization in Electrical Power Systems)
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