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Artificial Intelligence (AI) in the Power Grid and Renewable Energy
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Artificial Intelligence (AI) in the Power Grid and Renewable Energy

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 14847

Special Issue Editor

Prof. Dr. Chao-Rong Chen

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Interests: swarm intelligence; power systems; renew energy system; neural networks; smart grid; artificial intelligence

Special Issue Information

Dear Colleagues,

A power grid is a large-scale, dynamic and nonlinear system, which is important in stablility and reliablility. Thus, development of advanced technologies and computational methods applied to the modern power grid is interesting.

Artificial Intelligence (AI) in the Power Grid and Renewable Energy is a special issue in Energies for those who want to publish the original papers about the transmission, distribution, utilization, and renewable energy. This special issue aims at presenting important results of work in the power systems. The works can be applied research, development of new algorithms, original application of existing knowledge or new facilities applied to power systems.

Papers including but not limited to the following are invited:

  • AI, Machine Learning And Deep Learning for Power System Data Analytics;
  • Supervised, Unsupervised, and Reinforcement Learning;
  • Big Data, Computational Intelligence, and Energy Data Analytics;
  • Intelligent Forecasting, Modeling, Mitigation in Renewable Energy;
  • Intelligent Estimation and Classification Techniques;
  • Advanced Heuristic Optimization Techniques;
  • Reliability, Security, and Resiliency Assessment;
  • Planning and Operation of Energy Storage System;
  • Intelligent Control Applied to Protection System.

Prof. Dr. Chao-Rong Chen
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

  • artificial intelligence
  • swarm intelligence
  • artificial neural network (ANN)
  • long short-term memory (LSTM)
  • power protection
  • renewable energy

Published Papers (7 papers)

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Research

20 pages, 3518 KiB  
Article
Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)
by Huu Khoa Minh Nguyen, Quoc-Dung Phan, Yuan-Kang Wu and Quoc-Thang Phan
Energies 2023, 16(9), 3792; https://0-doi-org.brum.beds.ac.uk/10.3390/en16093792 - 28 Apr 2023
Cited by 2 | Viewed by 1239
Abstract
Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting [...] Read more.
Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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22 pages, 3563 KiB  
Article
Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island
by Mustafa Saglam, Catalina Spataru and Omer Ali Karaman
Energies 2022, 15(16), 5950; https://0-doi-org.brum.beds.ac.uk/10.3390/en15165950 - 17 Aug 2022
Cited by 10 | Viewed by 2451
Abstract
This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used [...] Read more.
This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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15 pages, 1727 KiB  
Article
Exploring the Influence of the Parameters’ Relationship between Reliability and Maintainability for Offshore Wind Farm Engineering
by I-Hua Chung
Energies 2022, 15(15), 5610; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155610 - 02 Aug 2022
Cited by 5 | Viewed by 1302
Abstract
The two main research goals of this study are to develop a relationship diagram between the parameters of reliability and maintainability and to investigate the impact of reliability and maintenance on engineering design costs. In this study, we use the theory of reliability [...] Read more.
The two main research goals of this study are to develop a relationship diagram between the parameters of reliability and maintainability and to investigate the impact of reliability and maintenance on engineering design costs. In this study, we use the theory of reliability and maintainability parameters to derive the relationship between the parameters using block diagrams. Compared with onshore wind farms, offshore wind farms have higher reliability requirements, but the maintenance degree of offshore wind farms is lower due to environmental factors. This study proposes an important concept of reliability and maintenance for value engineering, which can help system design engineers and project engineers integrate reliability concerns in the design phase and operation and maintenance phase. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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18 pages, 5173 KiB  
Article
Intelligent Tuned Hybrid Power Filter with Fuzzy-PI Control
by Tzu-Chiao Lin and Bawoke Simachew
Energies 2022, 15(12), 4371; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124371 - 15 Jun 2022
Cited by 5 | Viewed by 1543
Abstract
With the growing impact of power regulators, machines, and other power electronic devices on the quality of utility electric service, harmonics has recently emerged as a major research topic in the area of power quality and energy consumption. In this work, a fuzzy [...] Read more.
With the growing impact of power regulators, machines, and other power electronic devices on the quality of utility electric service, harmonics has recently emerged as a major research topic in the area of power quality and energy consumption. In this work, a fuzzy intelligent inductor–capacitor–inductor hybrid power filter is designed to reduce harmonics in the distribution line. The shunt active power filter and passive filter design and consideration have been improved. Since the nature of proportional integral control is only efficient with linear variations and the real condition of the distribution system is always varying nonlinearly, to create a minimum steady error, fuzzy-tuned proportional integral control is designed to intelligently consider the input changes and revise the decision rules using fuzzy reasoning. The content of the harmonic current with respect to the varying load is momentarily commutated. Thus, using Clarke’s transformation, the errors are summed with instantaneous power that is converted to a compensation current with the hysteresis band monitored pulse generator. The output current would turn off and on the IGBT inverter’s switches, supplying the current to reject the harmonics. The overall work was tested using MATLAB/SIMULINK® and was effective in canceling harmonics. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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20 pages, 6009 KiB  
Article
Feasibility and Techno-Economic Analysis of Electric Vehicle Charging of PV/Wind/Diesel/Battery Hybrid Energy System with Different Battery Technology
by Yirga Belay Muna and Cheng-Chien Kuo
Energies 2022, 15(12), 4364; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124364 - 15 Jun 2022
Cited by 25 | Viewed by 3536
Abstract
Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to [...] Read more.
Promoting the development of green technologies and replacing fossil fuel vehicles with electric ones can abate the environmental anxieties and issues associated with energy supply security. The increasing demand for electric vehicles requires an upgrade and expansion of the available charging infrastructure to accommodate the fast public adoption of this type of transportation. Ethiopia set a pro-electric cars policy and made them excise-free even before the first electric vehicle charging stations were launched by Marathon Motors Engineering in 2021. This paper presents the first ever technical, economic and environmental evaluation of electric vehicle charging stations powered by hybrid intermittent generation systems in three cities in Ethiopia. This paper tests this model using three different battery types: Lead-acid (LA), Flow-Zince-Bromine (ZnBr) and Lithium-ion (LI), used individually. Using these three battery technologies, the proposed hybrid systems are then compared in terms of system sizing, economy, technical performance and environmental stability. The results show that the feasible configuration of Solar Photovoltaic (PV)/Diesel Generator (DG)/ZnBr battery systems provide the lowest net present cost (NPC), with values of $2.97M, $2.72M and $2.85M, and cost of energy (COE), with values $0.196, $0.18 and $0.188, in Addis Ababa, Jijiga and Bahir Dar, respectively. Of all feasible systems, the Wind Turbine (WT)/PV/LI, PV/LI and WT/PV/LI configurations have the highest values of NPC and COE in Addis Ababa, Jijiga and Bahir Dar. Using this configuration, the results demonstrate that ZnBr battery is the most favorable choice because the economic parameters, including total NPC and COE, are found to be lowest. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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39 pages, 16994 KiB  
Article
Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition
by I-Hua Chung and Yu-Hsun Lin
Energies 2022, 15(11), 3940; https://0-doi-org.brum.beds.ac.uk/10.3390/en15113940 - 26 May 2022
Cited by 3 | Viewed by 1225
Abstract
Gas-insulated switchgear malfunctions during power system operation may occur due to electrical, thermal, or human errors in the manufacturing process. The leading causes of insulation deterioration of gas-insulated switchgear are discharging along the surface caused by dirt on the insulating material, internal discharge [...] Read more.
Gas-insulated switchgear malfunctions during power system operation may occur due to electrical, thermal, or human errors in the manufacturing process. The leading causes of insulation deterioration of gas-insulated switchgear are discharging along the surface caused by dirt on the insulating material, internal discharge caused by impurities and cavities in the insulating material, corona discharge caused by poor assembly or construction at the site, and electric tree channel discharge caused by the intense internal discharge. Since different defects produce different partial discharge characteristics, the operating power equipment can be analyzed using measurement instruments to detect partial discharge for preventive equipment fault diagnosis, avoiding unnecessary power outages and losses; therefore, evaluating the defects in gas-insulated switchgear is essential. In this study, three gas-insulated switchgears were prefabricated with different defects before encapsulation, and the partial discharge data of each defect were measured by applying different test voltages. The adaptive neuro-fuzzy inference system (ANFIS) input data were used to evaluate the recognition effect, showing that the average recognition rate of the core for all defects was over 90%. The proposed system architecture can continuously accumulate the defect measurement database of gas-insulated switchgear and be used as a reference for constructing electrical equipment defect recognition systems. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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18 pages, 4489 KiB  
Article
Artificial Intelligence Applications in Estimating Invisible Solar Power Generation
by Yuan-Kang Wu, Yi-Hui Lai, Cheng-Liang Huang, Nguyen Thi Bich Phuong and Wen-Shan Tan
Energies 2022, 15(4), 1312; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041312 - 11 Feb 2022
Cited by 7 | Viewed by 1643
Abstract
In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a [...] Read more.
In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in the Power Grid and Renewable Energy)
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