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Applications of Artificial Intelligence in New Energy Technology Systems

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 26902

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Special Issue Editors

Dr. Mehdi Seyedmahmoudian

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Guest Editor

School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria, Australia
Interests: Renewable Energy Systems; Smart Grids and Microgrids; Electric vehicle – Battery; Energy Management Systems; Electric Power train

Prof. Dr. Alex Stojcevski

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Guest Editor

School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC, Australia
Interests: power system stability and control; smart grids and microgrids; electric vehicle research; electric vehicle battery; electric power train; autonomous control and its networked robotics
Special Issues, Collections and Topics in MDPI journals

Assoc. Prof. Dr. Ben Horan

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Guest Editor

School of Engineering, Deakin University, Geelong, Victoria, Australia
Interests: renewable energy; smart grids and microgrids; virtual reality; industrial electronics; mechatronics; robotics

Prof. Dr. Saad Mekhilef

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Guest Editor

1. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
2. Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Universiti Malaya, Kuala Lumpur 50603, Selangor, Malaysia
Interests: power system stability and control; smart grids and microgrids; electric vehicle research; electric vehicle - battery; electric power train; autonomous control and ITS networked robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Enhancing the capability of distributed power generation has recently received considerable attention, and this has meant that the need to integrate new energy technologies has become widespread. In order for these technologies to meet the needs of our daily activities and be integrated to the current power system structure, there are several challenges that need to be overcome. These challenges include low efficiency performance, unreliable control strategies, inaccurate prediction, and high operating cost. To contribute to addressing these challenges, many researchers have employed innovative soft computing and Artificial Intelligence (AI) techniques to operate, manage, and control these new energy technologies.

This Special Issue invites original papers dealing with the application of artificial intelligence techniques to improve, develop, and manage new energy technologies. Invited papers may cover topics including but not limited to:

AI-enabled control systems for renewable energy systems;
Advanced energy management systems;
Advanced energy prediction techniques;
AI-enabled energy planning strategies;
Grid integration of new energy systems;
AI-enabled smart grid communication system;
Power electronics and industrial electronics;
Electric vehicles and storage systems;
Virtual reality visualization and simulation for new energy technologies;
Virtual power plants.

Dr. Mehdi Seyedmahmoudian
Prof. Dr. Alex Stojcevski
Assc. Prof. Dr. Ben Horan
Prof. Dr. Saad Mekhilef
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. Sustainability 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 2400 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 (9 papers)

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Research

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24 pages, 8290 KiB

Open AccessArticle

Artificial Neural Network Led Optimization of Oxyhydrogen Hybridized Diesel Operated Engine

by Muhammad Usman, Haris Hussain, Fahid Riaz, Muneeb Irshad, Rehmat Bashir, Muhammad Haris Shah, Adeel Ahmad Zafar, Usman Bashir, M. A. Kalam, M. A. Mujtaba and Manzoore Elahi M. Soudagar

Sustainability 2021, 13(16), 9373; https://doi.org/10.3390/su13169373 - 20 Aug 2021

Cited by 8 | Viewed by 2343

Abstract

The prevailing massive exploitation of conventional fuels has staked the energy accessibility to future generations. The gloomy peril of inflated demand and depleting fuel reservoirs in the energy sector has supposedly instigated the urgent need for reliable alternative fuels. These very issues have been addressed by introducing oxyhydrogen gas (HHO) in compression ignition (CI) engines in various flow rates with diesel for assessing brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The enrichment of neat diesel fuel with 10 dm³/min of HHO resulted in the most substantial decrease in BSFC and improved BTE at all test speeds in the range of 1000–2200 rpm. Moreover, an Artificial Intelligence (AI) approach was employed for designing an ANN performance-predicting model with an engine operating on HHO. The correlation coefficients (R) of BSFC and BTE given by the ANN predicting model were 0.99764 and 0.99902, respectively. The mean root errors (MRE) of both parameters (BSFC and BTE) were within the range of 1–3% while the root mean square errors (RMSE) were 0.0122 kg/kWh and 0.2768% for BSFC and BTE, respectively. In addition, ANN was coupled with the response surface methodology (RSM) technique for comprehending the individual impact of design parameters and their statistical interactions governing the output parameters. The R² values of RSM responses (BSFC and BTE) were near to 1 and MRE values were within the designated range. The comparative evaluation of ANN and RSM predicting models revealed that MRE and RMSE of RSM models are also well within the desired range but to be outrightly accurate and precise, the choice of ANN should be potentially endorsed. Thus, the combined use of ANN and RSM could be used effectively for reliable predictions and effective study of statistical interactions. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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15 pages, 3315 KiB

Open AccessArticle

Real-Time Implementation of an Optimized Model Predictive Control for a 9-Level CSC Inverter in Grid-Connected Mode

by Alamera Nouran Alquennah, Mohamed Trabelsi, Khaled Rayane, Hani Vahedi and Haitham Abu-Rub

Sustainability 2021, 13(15), 8119; https://doi.org/10.3390/su13158119 - 21 Jul 2021

Cited by 11 | Viewed by 2120

Abstract

The Crossover Switches Cell (CSC) is a recent Single DC-Source Multilevel Inverter (SDCS-MLI) topology with boosting abilities. In grid-connected PV applications, the CSC should be controlled to inject a sinusoidal current to the grid with low THD% and unity power factor, while balancing the capacitor voltage around its reference. These two objectives can be met through the application of a finite control set model predictive control (FCS-MPC) method. Thus, this paper proposes a design of an optimized FCS-MPC for a 9-level grid-tied CSC inverter. The switching actions are optimized using the redundant switching states. The design is verified through simulations and real-time implementation. The presented results show that the THD% of the grid current is 1.73%, and the capacitor voltage is maintained around its reference with less than 0.5 V mean error. To test the reliability of the control design, different scenarios were applied, including variations in the control reference values as well as the AC grid voltage. The presented results prove the good performance of the designed controller in tracking the reference values and minimizing the steady-state errors. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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17 pages, 2878 KiB

Open AccessArticle

The Way towards an Energy Efficient Transportation by Implementation of Fuel Economy Standards: Fuel Savings and Emissions Mitigation

by Ahmad Zuhairi Muzakir, Eng Hwa Yap and Teuku Meurah Indra Mahlia

Sustainability 2021, 13(13), 7348; https://doi.org/10.3390/su13137348 - 30 Jun 2021

Viewed by 2648

Abstract

Final energy use in Malaysia by the transport sector accounts for a consistent share of around 40% and even more in some years within the past two decades. Amongst all modes of transport, land transport dominates and within land transport, private travels are thought to be the biggest contributor. Personal mobility is dominated by the use of conventional internal-combustion-engine-powered vehicles (ICE), with the ownership trend of private cars has not shown any signs of tapering-off. Fuel consumption by private cars is currently not governed by a national policy on fuel economy standards. This is in contrast against not only the many developed economies, but even amongst some of the ASEAN neighbouring countries. The lack of fuel economy standards has resulted in the loss of potentially tremendous savings in fuel consumption and emission mitigation. This study analysed the increase in private vehicle stock to date, the natural fuel economy improvements brought by technology in a business as usual (BAU) situation, and the additional potential energy savings as well as emissions reduction in the ideal case of mandatory fuel economy standards for motor vehicles, specifically cars in Malaysia. The model uses the latest available data, relevant and most current parameters for the simulation and projection of the future scenario. It is found that the application of the fuel economy standards policy for cars in Malaysia is long overdue and that the country could benefit from the immediate implementation of fuel economy standards. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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15 pages, 3977 KiB

Open AccessArticle

Thermodynamic and Energy Efficiency Analysis of a Domestic Refrigerator Using Al₂O₃ Nano-Refrigerant

by Farhood Sarrafzadeh Javadi and Rahman Saidur

Sustainability 2021, 13(10), 5659; https://doi.org/10.3390/su13105659 - 18 May 2021

Cited by 11 | Viewed by 2991

Abstract

Refrigeration systems have experienced massive technological changes in the past 50 years. Nanotechnology can lead to a promising technological leap in the refrigeration industry. Nano-refrigerant still remains unknown because of the complexity of the phase change process of the mixture including refrigerant, lubricant, and nanoparticle. In this study, the stability of Al₂O₃ nanofluid and the performance of a nano-refrigerant-based domestic refrigerator have been experimentally investigated, with the focus on the thermodynamic and energy approaches. It was found that by increasing the nanoparticle concentration, the stability of nano-lubricant was decreased and evaporator temperature gradient was increased. The average of the temperature gradient increment in the evaporator was 20.2% in case of using 0.1%-Al₂O₃. The results showed that the energy consumption of the refrigerator reduced around 2.69% when 0.1%-Al₂O₃ nanoparticle was added to the system. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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21 pages, 5588 KiB

Open AccessArticle

Modeling Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System for Power Prediction

by Jafar Tavoosi, Amir Abolfazl Suratgar, Mohammad Bagher Menhaj, Amir Mosavi, Ardashir Mohammadzadeh and Ehsan Ranjbar

Sustainability 2021, 13(6), 3301; https://doi.org/10.3390/su13063301 - 17 Mar 2021

Cited by 35 | Viewed by 2045

Abstract

A novel Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) is presented for the modeling of renewable energy systems. Not only does this paper present a new architecture of the type-2 fuzzy system (T2FS) for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also, it introduces an exquisite technique to acquire an optimal number of membership functions (MFs) and their corresponding rules. Using nonlinear functions in the “Then” part of fuzzy rules, introducing a new mechanism in structure learning, using an adaptive learning rate and performing convergence analysis of the learning algorithm are the innovations of this paper. Another novel innovation is using optimization techniques (including pruning fuzzy rules, initial adjustment of MFs). Next, a solar photovoltaic cell and a wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge as convincing. The root-mean-square-error (RMSE) is less than 0.006 and the number of fuzzy rules is equal to or less than four rules, which indicates the very good performance of the presented fuzzy neural network. Finally, the obtained model is used for the first time for a geographical area to examine the feasibility of renewable energies. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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20 pages, 4626 KiB

Open AccessArticle

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

by Hossein Moayedi and Amir Mosavi

Sustainability 2021, 13(6), 3198; https://doi.org/10.3390/su13063198 - 15 Mar 2021

Cited by 26 | Viewed by 2471

Abstract

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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18 pages, 1060 KiB

Open AccessArticle

Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment

by Rostislav Krč, Martina Kratochvílová, Jan Podroužek, Tomáš Apeltauer, Václav Stupka and Tomáš Pitner

Sustainability 2021, 13(5), 2954; https://doi.org/10.3390/su13052954 - 09 Mar 2021

Cited by 10 | Viewed by 2209

Abstract

As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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18 pages, 6189 KiB

Open AccessArticle

Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

by Hossein Moayedi and Amir Mosavi

Sustainability 2021, 13(4), 2336; https://doi.org/10.3390/su13042336 - 21 Feb 2021

Cited by 22 | Viewed by 2554

Abstract

Predicting the electrical power (P_E) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of P_E, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the P_E with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the P_E and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the P_E with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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Review

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33 pages, 3624 KiB

Open AccessReview

Energy Management System in Microgrids: A Comprehensive Review

by Younes Zahraoui, Ibrahim Alhamrouni, Saad Mekhilef, M. Reyasudin Basir Khan, Mehdi Seyedmahmoudian, Alex Stojcevski and Ben Horan

Sustainability 2021, 13(19), 10492; https://doi.org/10.3390/su131910492 - 22 Sep 2021

Cited by 66 | Viewed by 6018

Abstract

As promising solutions to various social and environmental issues, the generation and integration of renewable energy (RE) into microgrids (MGs) has recently increased due to the rapidly growing consumption of electric power. However, such integration can affect the stability and security of power systems due to its complexity and intermittency. Therefore, an optimal control approach is essential to ensure the efficiency, reliability, and quality of the delivered power. In addition, effective planning of policies for integrating MGs can help promote MG operations. However, outages may render these strategies inefficient and place the power system at risk. MGs are considered an ideal candidate for distributed power systems, given their capability to restore these systems rapidly after a physical or cyber-attack and create reliable protection systems. The energy management system (EMS) in an MG can operate controllable distributed energy resources and loads in real-time to generate a suitable short-term schedule for achieving some objectives. This paper presents a comprehensive review of MG elements, the different RE resources that comprise a hybrid system, and the various types of control, operating strategies, and goals in an EMS. A detailed explanation of the primary, secondary, and tertiary levels of MGs is also presented. This paper aims to contribute to the policies and regulations adopted by certain countries, their protection schemes, transactive markets, and load restoration in MGs. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in New Energy Technology Systems)

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