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Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector

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

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 12267

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Special Issue Editors

Dr. Om Prakash Mahela

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Guest Editor

Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, Rajasthan, India
Interests: grid integration of renewable energy; design of grid integrated wind and solar power plants; power system protection; power quality; power system flexibility

Dr. Baseem Khan

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Guest Editor

Department of Electrical and Computer Engineering, Hawassa University, 05, Hawassa, Ethiopia
Interests: renewable energy; integration of renewable enrgy sources; smart grid; microgrid

Special Issue Information

Dear Colleagues,

Development requires energy, and for development to be sustainable, energy technology must be environmentally friendly. Three major improvements are required to accomplish sustainable energy development: reduction of emissions, substitution of fossil fuel-based power with renewable energy, and increase of energy efficiency. The globe has seen amazing progress in recent years; with renewable energy sources integration levels reaching double digit percentages of power output in certain nations, while many others are still in the early phases of renewable energy integration. By the end of 2020, renewable energy will account for a record 29 percent of the global power generating mix, up from 18 percent in 2009. However, fossil fuel-based power continues to dominate the power market by roughly 70%. Positively, the power industry's bright point is that renewable energy capacity expanded by more than 256 GW at the end of the year, the greatest rise ever. Net additions from renewable energy generation surpassed net additions from fossil fuel and nuclear capacity combined.

The aims of this special issue are as follows:

Provides an up-to-date analysis of the integrated trend of renewable energy in the global power industry, as well as its role in long-term growth. Examines past, current and future states of renewable energy sources, including wind and solar, hydropower, biofuel, geothermal energy, and marine/ocean energy. A thorough analysis of the recent challenges and probable solutions of renewables integration, including technical and operational challenges such as voltage stability, frequency stability, and power quality.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Recent challenges and probable solutions of renewables integration, including technical and operational challenges such as voltage stability, frequency stability, and power quality.
Integration policy and standards issues, resource assessment and location challenges, and societal obstacles were all mentioned in order for future research to identify acceptable solutions for sustainable energy.
The operational impact of large-scale renewable power plant penetration on the stability, security, dependability, and quality
Smart grid, artificial intelligence, and the internet of energy technologies that implemented for emission reduction strategies, efficient, cost-effective, and sustainable power sector.

We look forward to receiving your contributions.

Dr. Om Prakash Mahela
Dr. Baseem Khan
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.

Keywords

smart grid
microgrid
Internet of Energy
renewable energy integration
sustainable power system

Published Papers (8 papers)

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Research

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31 pages, 8054 KiB

Open AccessArticle

Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes

by Takele Ferede Agajie, Armand Fopah-Lele, Isaac Amoussou, Ahmed Ali, Baseem Khan, Om Prakash Mahela, Ramakrishna S. S. Nuvvula, Divine Khan Ngwashi, Emmanuel Soriano Flores and Emmanuel Tanyi

Sustainability 2023, 15(15), 11735; https://doi.org/10.3390/su151511735 - 30 Jul 2023

Cited by 2 | Viewed by 1659

Abstract

Access to cheap, clean energy has a significant impact on a country’s ability to develop sustainably. Fossil fuels have a major impact on global warming and are currently becoming less and less profitable when used to generate power. In order to replace the diesel generators that are connected to the university of Debre Markos’ electrical distribution network with hybrid renewable energy sources, this study presents optimization and techno-economic feasibility analyses of proposed hybrid renewable systems and their overall cost impact in stand-alone and grid-connected modes of operation. Metaheuristic optimization techniques such as enhanced whale optimization algorithm (EWOA), whale optimization algorithm (WOA), and African vultures’ optimization algorithm (AVOA) are used for the optimal sizing of the hybrid renewable energy sources according to financial and reliability evaluation parameters. After developing a MATLAB program to size hybrid systems, the total current cost (TCC) was calculated using the aforementioned metaheuristic optimization techniques (i.e., EWOA, WOA, and AVOA). In the grid-connected mode of operation, the TCC was 4.507 × 10⁶ EUR, 4.515 × 10⁶ EUR, and 4.538 × 10⁶ EUR, respectively, whereas in stand-alone mode, the TCC was 4.817 × 10⁶ EUR, 4.868 × 10⁶ EUR, and 4.885 × 10⁶ EUR, respectively. In the grid-connected mode of operation, EWOA outcomes lowered the TCC by 0.18% using WOA and 0.69% using AVOA, and by 1.05% using WOA and 1.39% using AVOA in stand-alone operational mode. In addition, when compared with different financial evaluation parameters such as net present cost (NPC) (EUR), cost of energy (COE) (EUR/kWh), and levelized cost of energy (LCOE) (EUR/kWh), and reliability parameters such as expected energy not supplied (EENS), loss of power supply probability (LPSP), reliability index (IR), loss of load probability (LOLP), and loss of load expectation (LOLE), EWOA efficiently reduced the overall current cost while fulfilling the constraints imposed by the objective function. According to the result comparison, EWOA outperformed the competition in terms of total current costs with reliability improvements. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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17 pages, 3904 KiB

Open AccessArticle

Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

by Chibuzor N. Obiora, Ali N. Hasan and Ahmed Ali

Sustainability 2023, 15(11), 8927; https://doi.org/10.3390/su15118927 - 1 Jun 2023

Cited by 3 | Viewed by 1317

Abstract

Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models’ accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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26 pages, 9258 KiB

Open AccessArticle

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant

by Takele Ferede Agajie, Armand Fopah-Lele, Ahmed Ali, Isaac Amoussou, Baseem Khan, Mahmoud Elsisi, Om Prakash Mahela, Roberto Marcelo Álvarez and Emmanuel Tanyi

Sustainability 2023, 15(7), 5739; https://doi.org/10.3390/su15075739 - 24 Mar 2023

Cited by 3 | Viewed by 1382

Abstract

In this paper, the electrical parameters of a hybrid power system made of hybrid renewable energy sources (HRES) generation are primarily discussed. The main components of HRES with energy storage (ES) systems are the resources coordinated with multiple photovoltaic (PV) cell units, a biogas generator, and multiple ES systems, including superconducting magnetic energy storage (SMES) and pumped hydro energy storage (PHES). The performance characteristics of the HRES are determined by the constant power generation from various sources, as well as the shifting load perturbations. Constant power generation from a variety of sources, as well as shifting load perturbations, dictate the HRES’s performance characteristics. As a result of the fluctuating load demand, there will be steady generation but also fluctuating frequency and power. A suitable control strategy is therefore needed to overcome the frequency and power deviations under the aforementioned load demand and generation conditions. An integration in the environment of fractional order (FO) calculus for proportion-al-integral-derivative (PID) controllers and fuzzy controllers, referred to as FO-Fuzzy-PID controllers, tuned with the opposition-based whale optimization algorithm (OWOA), and compared with QOHSA, TBLOA, and PSO has been proposed to control the frequency deviation and power deviations in each power generation unites. The results of the frequency deviation obtained by using FO-fuzzy-PID controllers with OWOA tuned are 1.05%, 2.01%, and 2.73% lower than when QOHSA, TBLOA, and PSO have been used to tune, respectively. Through this analysis, the algorithm’s efficiency is determined. Sensitivity studies are also carried out to demonstrate the robustness of the technique under consideration in relation to changes in the sizes of the HRES and ES system parameters. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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26 pages, 1294 KiB

Open AccessArticle

Current- and Voltage-Actuated Transmission Line Protection Scheme Using a Hybrid Combination of Signal Processing Techniques

by Ligang Tang, Om Prakash Mahela, Baseem Khan and Yini Miro

Sustainability 2023, 15(7), 5715; https://doi.org/10.3390/su15075715 - 24 Mar 2023

Cited by 2 | Viewed by 1331

Abstract

This paper presents a current- and voltage-driven protection scheme for transmission lines based on a hybrid mix of Stockwell transform (ST) and Hilbert transform (HT). Use of both current and voltage waveforms to detect and categorize faults, improves the reliability of this protection scheme and avoids false tripping. Current and voltage waveforms captured during a period of fault are analyzed using ST to compute a median intermediate fault index (MIFI), a maximum value intermediate fault index (MVFI), and a summation intermediate fault index (SIFI). Current and voltage signals are analyzed via applying HT to compute a Hilbert fault index (HFI). The proposed hybrid current and voltage fault index (HCVFI) is obtained from the MIFI, MVFI, SIFI, and HFI. A threshold magnitude for this hybrid current and voltage fault index (HCVFITH) is set to 500 to identify the faulty phase. The HCVFIT is selected after testing the method for various conditions of different fault locations, different fault impedances, different fault occurrence angles, and reverse flows of power. Fault classification is performed using the number of faulty phases and an index for ground detection (IGD). The ground involved in a fault is detected by comparison of peak IGD magnitude with a threshold for ground detection (THGD). THGD is considered equal to 1000 in this study. The study is carried out using a two-terminal transmission line modeled in MATLAB software. The performance of the proposed technique is better compared to a discrete wavelet transform (DWT)-based technique, a time–frequency approach, and an alienation method. Our algorithm effectively detected an AG fault, observed on a practical transmission line. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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24 pages, 2475 KiB

Open AccessArticle

Hybrid Combination of Network Restructuring and Optimal Placement of Distributed Generators to Reduce Transmission Loss and Improve Flexibility

by Ekata Kaushik, Vivek Prakash, Raymond Ghandour, Zaher Al Barakeh, Ahmed Ali, Om Prakash Mahela, Roberto Marcelo Álvarez and Baseem Khan

Sustainability 2023, 15(6), 5285; https://doi.org/10.3390/su15065285 - 16 Mar 2023

Cited by 1 | Viewed by 1373

Abstract

A high penetration of renewable energy (RE) in utility grids creates the problems of power system flexibility, high transmission losses, and voltage variations. These problems can be solved using a hybrid combination of transmission network restructuring and optimal placement of distributed energy generator (DEG) units. Hence, this work investigated a technologically and economically feasible solution for improving the flexibility of power networks and reducing losses in a practical transmission utility network by implementing a restructuring of the network and optimal deployment of the distributed energy generators (DEGs). Two solutions for this network restructuring were proposed. Furthermore, a grid-oriented genetic algorithm (GOGA) was designed by combining the conventional genetic algorithm (GA) and mathematical solutions to identify optimal DEG placement. A power system restructuring and GOGA flexibility index (PSRGFI) was formulated for the assessment of network flexibility. A cost–benefit assessment was also performed to estimate the payback period for the investment required for restructuring of the network and DEG placement. The least-square approximation technique was applied for load projection for the year 2031 considering the base year 2021. It was established that minimization of transmission losses, reduction in voltage deviations, and improvement of network flexibility were achieved through hybrid application of network restructuring and DEG placement using GOGA. A network loss saving of 61.19 MW was achieved via optimal restructuring and GOGA. For the projected year 2031, the PSRGFI increased from 30.94 to 132.78 after the placement of DEGs using GOGA and optimal restructuring, indicating that network flexibility increased significantly. The payback period for the investment was very small, equal to 0.985 years. The performance of the designed method was superior to the GA-based method, simulated annealing technique, and bee colony algorithm (BCA) used for placement of DEG units in the test network. The study was completed using MATLAB software, considering data from a practical transmission network owned by Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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30 pages, 1399 KiB

Open AccessArticle

Power Quality Detection and Categorization Algorithm Actuated by Multiple Signal Processing Techniques and Rule-Based Decision Tree

by Surendra Singh, Avdhesh Sharma, Akhil Ranjan Garg, Om Prakash Mahela, Baseem Khan, Ilyes Boulkaibet, Bilel Neji, Ahmed Ali and Julien Brito Ballester

Sustainability 2023, 15(5), 4317; https://doi.org/10.3390/su15054317 - 28 Feb 2023

Viewed by 1248

Abstract

This paper introduces a power quality (PQ) detection and categorization algorithm actuated by multiple signal processing techniques and rule-based decision tree (RBDT). This is aimed to recognize PQ events of simple nature and higher order multiplicity with less computational time using hybridization of the signal processing techniques. A voltage waveform with a PQ event (PQE) is processed using the Stockwell transform (ST) to compute the Stockwell PQ detection index (SPDI). The voltage waveform is also processed using the Hilbert transform (HT) to compute the Hilbert PQ detection index (HPDI). A voltage waveform is also decomposed using the Discrete Wavelet transform (DWT) to compute the classification feature index (CFI) [CFI1 to CFI4]. A combined PQ detection index (CPDI) is computed by multiplication of the SPDI, the HPDI and CFI1 to CFI4. Incidence of a PQE on a voltage signal is located with the help of a location PQ disturbance index (LPDI) which is computed by differentiating the CPDI with respect to time. CFI5, CFI6 and CFI7 are computed from the SPDI, the HPDI and the CPDI, respectively. Categorization of PQ events is performed using CFI1 to CFI7 by the rule-based decision tree (RBDT) with the help of simple decision rules. We conclude that the proposed algorithm is effective to identify the PQE with an accuracy of 98.58% in a noise-free environment and 97.62% in the presence of 20 dB SNR (signal-to-noise ratio) noise. Ten simple nature PQEs and eight combined PQ events (CPQEs) with multiplicity of two, three and four are effectively detected and categorized using the algorithm. The algorithm is also tested to detect a sag PQ event due to a line-to-ground (LG) fault incident on a practical distribution utility network. The performance of the investigated method is compared with a DWT-based technique in terms of accuracy of classification with and without noise, maximum computational time of PQ detection and multiplicity of PQE which can be effectively detected. A simulation is performed using the MATLAB software. MATLAB codes are used for modelling the PQE disturbances and the proposed algorithm using mathematical formulations. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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19 pages, 565 KiB

Open AccessArticle

Transmission Network Loss Reduction and Voltage Profile Improvement Using Network Restructuring and Optimal DG Placement

by Pramod Kumar, Nagendra Kumar Swarnkar, Ahmed Ali, Om Prakash Mahela, Baseem Khan, Divya Anand and Julien Brito Ballester

Sustainability 2023, 15(2), 976; https://doi.org/10.3390/su15020976 - 5 Jan 2023

Cited by 3 | Viewed by 1222

Abstract

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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Review

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22 pages, 1718 KiB

Open AccessReview

A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

by K. R. Sri Preethaa, Akila Muthuramalingam, Yuvaraj Natarajan, Gitanjali Wadhwa and Ahmed Abdi Yusuf Ali

Sustainability 2023, 15(17), 12914; https://doi.org/10.3390/su151712914 - 26 Aug 2023

Cited by 4 | Viewed by 1921

Abstract

The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned. Full article

(This article belongs to the Special Issue Trends, Environmental Implications, Recent Obstacles and Solutions in the Sustainable Growth of the Renewable Energy Integrated Power Sector)

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