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Artificial Intelligence Technologies for Electric Power Systems
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Artificial Intelligence Technologies for Electric Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (15 January 2021) | Viewed by 17063

Special Issue Editor

Prof. Dr. Mihai Gavrilas

E-Mail Website
Guest Editor
Department of Power Engineering, “Gheorghe Asachi” Technical University of Iasi, 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 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

  • 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 (6 papers)

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Research

20 pages, 1910 KiB  
Article
Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm
by Andrei M. Tudose, Irina I. Picioroaga, Dorian O. Sidea and Constantin Bulac
Energies 2021, 14(5), 1222; https://doi.org/10.3390/en14051222 - 24 Feb 2021
Cited by 46 | Viewed by 2583
Abstract
The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational [...] Read more.
The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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22 pages, 564 KiB  
Article
Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms
by Samer Chaaraoui, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo and Harald Kunstmann
Energies 2021, 14(2), 409; https://doi.org/10.3390/en14020409 - 13 Jan 2021
Cited by 8 | Viewed by 2629
Abstract
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of [...] Read more.
Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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15 pages, 457 KiB  
Article
Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design
by Maher Selim, Ryan Zhou, Wenying Feng and Peter Quinsey
Energies 2021, 14(1), 247; https://doi.org/10.3390/en14010247 - 5 Jan 2021
Cited by 17 | Viewed by 3060
Abstract
Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, [...] Read more.
Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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15 pages, 1698 KiB  
Article
An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine
by Yiyi Zhang, Yuxuan Wang, Xianhao Fan, Wei Zhang, Ran Zhuo, Jian Hao and Zhen Shi
Energies 2020, 13(24), 6678; https://doi.org/10.3390/en13246678 - 17 Dec 2020
Cited by 9 | Viewed by 2085
Abstract
Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision [...] Read more.
Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA features. The diagnostic accuracy of the proposed GAPSVM integrated with the FTR fault diagnosis method reached 86.80% after 10 repeated calculations using 118 groups of IEC technical committee (TC) 10 samples. Moreover, the robustness is also proven through 30 groups of DGA samples from the State Grid Co. of China and 15 practical cases with missing values. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Electric Power Systems)
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35 pages, 3920 KiB  
Article
Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy
by Khaled Nusair and Lina Alhmoud
Energies 2020, 13(22), 6066; https://doi.org/10.3390/en13226066 - 19 Nov 2020
Cited by 23 | Viewed by 2545
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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38 pages, 5110 KiB  
Article
Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’
by Gianfranco Chicco and Andrea Mazza
Energies 2020, 13(19), 5097; https://doi.org/10.3390/en13195097 - 30 Sep 2020
Cited by 37 | Viewed by 3301
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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