Special Issue "Artificial Intelligence Technologies for Electric Power Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Smart Grids and Microgrids".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editor

Prof. Dr. Mihai Gavrilas
Website
Guest Editor
Department of Power Engineering, “Gheorghe Asachi” Technical University of Iasi, Romania
Interests: electricity generation and consumption forecasting; load profiling, electricity market analysis and simulation; smart grid design and optimization; power system stability

Special Issue Information

Dear Colleagues,

Climate change mitigation and environmental protection are two of the most important priorities of the moment. To achieve these goals, transforming the energy sector into a less polluting and more efficient industry is one of the most efficient tools. Indeed, today’s energy industry is becoming smarter. Thus, what is known today as Smart Grid means power generation from renewable sources, mainly wind and solar, the integration of distributed generation systems and energy storage systems, the improvement of control devices and the development of active electrical networks based on flexibility and intelligent control. 

Artificial intelligence technologies, whose bases were laid in the 1940s, are today experiencing an impressive evolution, and their integration into the power industry is an unquestionable must. Predicting electricity consumption and generation using artificial neural networks, identifying consumer categories based on clustering and self-organizing techniques, optimizing the design and operation of transmission and distribution networks using metaheuristics or the intelligent control of automation and protection systems based on fuzzy logic and fuzzy techniques are just a few examples of applications of artificial intelligence techniques in power systems. 

This Special Issue welcomes original contributions in the application of artificial intelligence in power systems or other related fields.

Prof. Dr. Mihai Gavrilas
Guest Editor

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 1800 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

  • smart grids
  • distributed generation
  • renewable energy sources
  • energy storage
  • intelligent control
  • artificial intelligence
  • evolutionary computation
  • nature inspired algorithms
  • artificial neural networks
  • fuzzy systems.

Published Papers (2 papers)

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Research

Open AccessArticle
Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy
Energies 2020, 13(22), 6066; https://doi.org/10.3390/en13226066 - 19 Nov 2020
Abstract
In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the [...] Read more.
In recent decades, the energy market around the world has been reshaped to accommodate the high penetration of renewable energy resources. Although renewable energy sources have brought various benefits, including low operation cost of wind and solar PV power plants, and reducing the environmental risks associated with the conventional power resources, they have imposed a wide range of difficulties in power system planning and operation. Naturally, classical optimal power flow (OPF) is a nonlinear problem. Integrating renewable energy resources with conventional thermal power generators escalates the difficulty of the OPF problem due to the uncertain and intermittent nature of these resources. To address the complexity associated with the process of the integration of renewable energy resources into the classical electric power systems, two probability distribution functions (Weibull and lognormal) are used to forecast the voltaic power output of wind and solar photovoltaic, respectively. Optimal power flow, including renewable energy, is formulated as a single-objective and multi-objective problem in which many objective functions are considered, such as minimizing the fuel cost, emission, real power loss, and voltage deviation. Real power generation, bus voltage, load tap changers ratios, and shunt compensators values are optimized under various power systems’ constraints. This paper aims to solve the OPF problem and examines the effect of renewable energy resources on the above-mentioned objective functions. A combined model of wind integrated IEEE 30-bus system, solar PV integrated IEEE 30-bus system, and hybrid wind and solar PV integrated IEEE 30-bus system is performed using the equilibrium optimizer technique (EO) and other five heuristic search methods. A comparison of simulation and statistical results of EO with other optimization techniques showed that EO is more effective and superior and provides the lowest optimization value in term of electric power generation, real power loss, emission index and voltage deviation. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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Open AccessArticle
Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’
Energies 2020, 13(19), 5097; https://doi.org/10.3390/en13195097 - 30 Sep 2020
Abstract
In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming [...] Read more.
In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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