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Smart Grid Technologies and Renewable Energy Applications
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Smart Grid Technologies and Renewable Energy Applications

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 29179

Special Issue Editors

Dr. Hassan M. Hussein Farh

E-Mail Website
Guest Editor
Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
Interests: smart grid technologies; renewable energy (wind and solar PV) applications; energy conservation measures; distributed power generation; power and energy infrastructure; power electronics applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saad Mekhilef

E-Mail Website
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, Department of Electrical Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
Interests: power conversion techniques; control of power converters; maximum power point tracking (MPPT); renewable energy; energy efficiency; smart grid; microwave and wireless technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Fathy

E-Mail Website
Guest Editor
Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia
Interests: renewable energy (solar energy, wind energy and hybrid systems); artificial intelligence applications; system security and system stability; operational planning and scheduling; optimal operation and control of power systems
Special Issues, Collections and Topics in MDPI journals
Dr. Abdullrahman Abdullah Al-Shamma'a

E-Mail Website
Guest Editor
Electrical Engineering Department, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
Interests: multilevel converters; power electronics applications; hybrid energy system; renewable energy technologies; energy management; distributed power generation; artificial intelligence techniques; energy conversion stratigies

Special Issue Information

Dear Colleagues,

Traditional distribution systems (TDSs) encounter some shortcomings, such as high power losses, unstable voltage, centralized generation sources, unidirectional design and low efficiency. Therefore, it is crucial to incorporate renewable distributed generation (DG) systems and/or Flexible AC Transmission Systems (FACTS) with optimal allocation and sizing into TDSs in order to improve the technical and economic performance of power systems as well as to address TDSs' shortcomings. On the other hand, smart grid technologies offer the opportunities for monitoring the distributed energy generation using remote reading/measurement devices, as well as two-way control facilitates between the generation and demand.

Solar photovoltaic (PV) and wind energy systems represent a promising option for renewable generation systems, which are clean, abundant, noise free and friendly to the environment. Thus, tracking the maximum power using artificial intelligence, machine learning and bio-inspired techniques from this energy is crucial to improve the PV system’s performance in terms of output power generated, efficiency, reliability and quality. From a practical perspective, the PV or wind cannot supply continuous energy by itself due to the intermittent nature of these sources. Therefore, hybrid renewable energy systems (HRESs) or microgrids have become remarkable solutions, especially to electrify off-grid urban areas. Taking into consideration the energy conservation measures can improve not only the energy efficiency of a microgrid but also the resilience and reliability of a microgrid.

This Special Issue aims at publishing a set of important research work and the latest advancements in smart grid technologies and renewable energy applications to mitigate its potential shortcomings and challenges. Specifically, authors are encouraged to submit their research work in theoretical or simulation models, practical and experimental, optimization algorithms and applications concerning smart grid technologies and renewable energy applications.

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

  • Smart grid technologies and applications
  • Renewable hybrid distributed generation: allocation and sizing strategies/techniques
  • Renewable energy (Solar PV, wind energy and hybrid systems)
  • Maximum Power Point Tracking (MPPT)
  • Energy Conservation Measures and Demand-side management strategies
  • Artifitial intelligence and machine learning algorithms for renewable energy system problems
  • Power Electronic applications in renewable energy systems
  • Optimal power system planning, operation and control
  • Smart power infrastructure for energy reliability, quality and security
  • Flexible AC Transmission Systems (FACTS) applications

We look forward to receiving your contributions.

Dr. Hassan M. Hussein Farh
Prof. Dr. Saad Mekhilef
Dr. Ahmed Fathy
Dr. Abdullrahman Abdullah Al-Shamma'a
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
  • renewable energy
  • power converters
  • maximum power point tracking
  • energy conservation measures
  • artificial intelligence
  • machine learning
  • flexible AC transmission systems

Published Papers (18 papers)

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Research

23 pages, 2205 KiB  
Article
Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks
by Ahmed A. Alguhi, Majed A. Alotaibi and Essam A. Al-Ammar
Sustainability 2024, 16(1), 290; https://doi.org/10.3390/su16010290 - 28 Dec 2023
Viewed by 877
Abstract
Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the [...] Read more.
Today, many countries are focused on smart grids due to their positive effects on all sectors of a power system, including those of operators, utilities, and consumers. Furthermore, the usage of renewable energy sources for power production is quickly expanding due to the depletion of fossil fuels and the emissions caused by their use. Additionally, intermittent power generation from renewable energy sources, such as wind and solar, necessitates the use of energy storage devices with which to ensure a continuous power supply to meet demand. This can be accomplished by employing an appropriate storage device with a sufficient storage capacity, thus enabling a grid-connected solar PV and wind system to have enhanced performance and to reduce adverse effects on the power quality of the grid. In this study, a probabilistic planning model that takes the intermittent natures of solar irradiances, wind speeds, and system demands into account is introduced. A novel criterion is also adopted to map the three-dimensional spaces of intermittency with the proposed model for optimizing BESS charging/discharging decisions. This planning model is intended to minimize the economic costs of investment and operation of a battery energy storage system (BESS) for a planning period. Moreover, the substation and feeder upgrade costs, as well as the overall system loss costs, are included in the proposed model. Particle swarm optimization (PSO) is utilized to find the optimal sizing, location, and operation of energy storage systems. The proposed methodology was validated using a 69-bus distribution system. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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16 pages, 2907 KiB  
Article
Electric Vehicle Load Estimation at Home and Workplace in Saudi Arabia for Grid Planners and Policy Makers
by Abdulaziz Almutairi, Naif Albagami, Sultanh Almesned, Omar Alrumayh and Hasmat Malik
Sustainability 2023, 15(22), 15878; https://doi.org/10.3390/su152215878 - 13 Nov 2023
Cited by 2 | Viewed by 891
Abstract
Electric vehicles (Evs) offer promising benefits in reducing emissions and enhancing energy security; however, accurately estimating their load presents a challenge in optimizing grid management and sustainable integration. Moreover, EV load estimation is context-specific, and generalized methods are inadequate. To address this, our [...] Read more.
Electric vehicles (Evs) offer promising benefits in reducing emissions and enhancing energy security; however, accurately estimating their load presents a challenge in optimizing grid management and sustainable integration. Moreover, EV load estimation is context-specific, and generalized methods are inadequate. To address this, our study introduces a tailored three-step solution, focusing on the Middle East, specifically Saudi Arabia. Firstly, real survey data are employed to estimate driving patterns and commuting behaviors such as daily mileage, arrival/departure time at home and workplace, and trip mileage. Subsequently, per-unit profiles for homes and workplaces are formulated using these data and commercially available EV data, as these locations are preferred for charging by most EV owners. Finally, the developed profiles facilitate EV load estimations under various scenarios with differing charger ratios (L1 and L2) and building types (residential, commercial, mixed). Simulation outcomes reveal that while purely residential or commercial buildings lead to higher peak loads, mixed buildings prove advantageous in reducing the peak load of Evs. Especially, the ratio of commercial to residential usage of around 50% generates the lowest peak load, indicating an optimal balance. Such analysis aids grid operators and policymakers in load estimation and incentivizing EV-related infrastructure. This study, encompassing data from five Saudi Arabian cities, provides valuable insights into EV usage, but it is essential to interpret findings within the context of these specific cities and be cautious of potential limitations and biases. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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17 pages, 2694 KiB  
Article
Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques
by Neeraj Kumar, Madan Mohan Tripathi, Saket Gupta, Majed A. Alotaibi, Hasmat Malik and Asyraf Afthanorhan
Sustainability 2023, 15(19), 14448; https://doi.org/10.3390/su151914448 - 03 Oct 2023
Viewed by 925
Abstract
This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in [...] Read more.
This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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16 pages, 4426 KiB  
Article
An Enhanced AC Fault Ride through Scheme for Offshore Wind-Based MMC-HVDC System
by Jahangeer Badar Soomro, Dileep Kumar, Faheem Akhtar Chachar, Semih Isik and Mohammed Alharbi
Sustainability 2023, 15(11), 8922; https://doi.org/10.3390/su15118922 - 01 Jun 2023
Cited by 1 | Viewed by 1105
Abstract
This study presents an improved, communication-free Fault Ride-Through (FRT) strategy for type-3 and type-4 wind turbine integrated modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems in offshore wind power plants (OWPPs). The research aims to enhance the reliability and resilience of OWPPs by [...] Read more.
This study presents an improved, communication-free Fault Ride-Through (FRT) strategy for type-3 and type-4 wind turbine integrated modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems in offshore wind power plants (OWPPs). The research aims to enhance the reliability and resilience of OWPPs by ensuring their connection with AC grids remains intact during and after faults. Simulation results conducted on a 580 kV, 850 MW MMC-HVDC system using PSCAD/EMTDC software v.4.6.2 demonstrate quick post-fault recovery operation and the ability to effectively manage DC link and capacitor voltages within safe limits. Furthermore, the circulating current (CC) and capacitor voltage ripple (CVR) remain within acceptable limits, ensuring safe and reliable operation. The study’s major conclusion is that the proposed FRT strategy effectively mitigates the adverse effects of short circuit faults, such as a rapid rise in DC-link voltage, on the performance of the MMC-HVDC system. By promptly suppressing DC-link overvoltage, the proposed FRT scheme prevents compromising the safe operation of various power electronics equipment. These findings highlight the significance of FRT capability in OWPPs and emphasize the practical applicability of the proposed strategy in enhancing the reliability of offshore wind power generation. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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24 pages, 5867 KiB  
Article
Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance
by Ali S. Allahloh, Mohammad Sarfraz, Atef M. Ghaleb, Abdullrahman A. Al-Shamma’a, Hassan M. Hussein Farh and Abdullah M. Al-Shaalan
Sustainability 2023, 15(11), 8808; https://doi.org/10.3390/su15118808 - 30 May 2023
Cited by 5 | Viewed by 1886
Abstract
In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite [...] Read more.
In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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19 pages, 4880 KiB  
Article
Machine Learning Supervisory Control of Grid-Forming Inverters in Islanded Mode
by Hammed Olabisi Omotoso, Abdullrahman A. Al-Shamma’a, Mohammed Alharbi, Hassan M. Hussein Farh, Abdulaziz Alkuhayli, Akram M. Abdurraqeeb, Faisal Alsaif, Umar Bawah and Khaled E. Addoweesh
Sustainability 2023, 15(10), 8018; https://doi.org/10.3390/su15108018 - 15 May 2023
Cited by 1 | Viewed by 1393
Abstract
This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage [...] Read more.
This research paper presents a novel droop control strategy for sharing the load among three independent converter power systems in a microgrid. The proposed method employs a machine learning algorithm based on regression trees to regulate both the system frequency and terminal voltage at the point of common coupling (PCC). The aim is to ensure seamless transitions between different modes of operation and maintain the load demand while distributing it among the available sources. To validate the performance of the proposed approach, the paper compares it to a traditional proportional integral (PI) controller for controlling the dynamic response of the frequency and voltage at the PCC. The simulation experiments conducted in MATLAB/Simulink show the effectiveness of the regression tree machine learning algorithm over the PI controller, in terms of the step response and harmonic distortion of the system. The results of the study demonstrate that the proposed approach offers an improved stability and efficiency for the system, making it a promising solution for microgrid operations. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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18 pages, 2991 KiB  
Article
Insulation Performance of Building Components and Effect on the Cooling Load of Homes in Saudi Arabia
by Abdulhamid Al-Abduljabbar, Majid Al-Mogbel, Syed Noman Danish and Abdelrahman El-Leathy
Sustainability 2023, 15(7), 5685; https://doi.org/10.3390/su15075685 - 24 Mar 2023
Viewed by 2860
Abstract
A common practice in the construction of residential and commercial buildings in Saudi Arabia is to insulate the outer walls and windows only. Other building components such as the roof, columns and slabs, and doors are usually neglected. Moreover, vital components such as [...] Read more.
A common practice in the construction of residential and commercial buildings in Saudi Arabia is to insulate the outer walls and windows only. Other building components such as the roof, columns and slabs, and doors are usually neglected. Moreover, vital components such as the roof and windows are especially neglected in commercially built residential and commercial buildings. The aim of this study is to put this common impression and practice to the test by quantifying the contribution of every building component to the overall air-conditioning load of the building. The hypothesis evaluated in this paper is that despite the common practices, there could be an optimum selection of insulators for the building components that yields the lowest energy consumption and maximum savings not only in energy costs but also installation costs. The required air-conditioning load is determined using manual calculations and the HAP software package for 1022 possible configurations. The findings of the analysis point to the importance of the roof, as it is the major contributor to the thermal load, followed closely by columns and slabs, with 44.2% of the overall cooling load. It is found that a single wall consisting of 2 cm of cement plaster, 20 cm of cement–polyurethane brick, and 2 cm of cement plaster is less expensive and has higher thermal resistance than any of the more expensive double walls. The study found one scenario of possible configurations with the optimized selection of building materials and their insulation materials that provides the most effective insulation at the lowest cost. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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22 pages, 3823 KiB  
Article
A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters
by Ahmed Fathy, Dalia Yousri, Abdullah G. Alharbi and Mohammad Ali Abdelkareem
Sustainability 2023, 15(7), 5667; https://doi.org/10.3390/su15075667 - 23 Mar 2023
Cited by 5 | Viewed by 1809
Abstract
Constructing a reliable equivalent circuit of Li-Ion batteries using real operating conditions by estimating optimal parameters is mandatory for many engineering applications, as it controls the energy management of the battery in a hybrid system. However, model parameters can vary according to the [...] Read more.
Constructing a reliable equivalent circuit of Li-Ion batteries using real operating conditions by estimating optimal parameters is mandatory for many engineering applications, as it controls the energy management of the battery in a hybrid system. However, model parameters can vary according to the electrochemical nature of the battery, so improving the accuracy of the battery model parameters is essential to obtain reliable and accurate equivalent circuits. Therefore, this paper proposes a new efficient hybrid optimization approach for determining the proper parameters of Li-ion battery Shepherd model equivalent circuits. The proposed algorithm comprises a white shark optimizer (WSO) and the whale optimization approach (WOA) for modifying the stochastic behavior of the WSO while searching for food sources. Minimizing the root mean square error between the estimated and measured battery voltages is the objective function considered in this work. The hybrid variant of the WSO (HWSO) was examined with two different types of batteries. Moreover, the proposed HWSO was validated versus a set of recent meta-heuristic approaches including the sea horse optimizer (SHO), artificial gorilla troops optimizer (GTO), coyote optimization algorithm (COA), and the basic version of the WSO. Furthermore, statistical analyses, mean convergence, and fitting curves were conducted for the comparisons. The proposed HWSO succeeded in achieving the least fitness values of 2.6172 × 10−4 and 5.6118 × 10−5 with standard deviations of 9.3861 × 10−5 and 3.2854 × 10−4 for battery 1 and battery 2, respectively. On the other hand, the worst fitness values were 6.5230 × 10−2 and 6.6197 × 10−5 via SHO and WSO for both considered batteries. The proposed HWSO results prove the efficiency of the proposed approach in providing highly accurate battery model parameters with high consistency and a unique convergence curve compared to the other methods. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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13 pages, 4194 KiB  
Article
Maximizing Bio-Hydrogen Production from an Innovative Microbial Electrolysis Cell Using Artificial Intelligence
by Ahmed Fathy, Hegazy Rezk, Dalia Yousri, Abdullah G. Alharbi, Sulaiman Alshammari and Yahia B. Hassan
Sustainability 2023, 15(4), 3730; https://doi.org/10.3390/su15043730 - 17 Feb 2023
Cited by 1 | Viewed by 1987
Abstract
In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of [...] Read more.
In this research work, the best operating conditions of microbial electrolysis cells (MECs) were identified using artificial intelligence and modern optimization. MECs are innovative materials that can be used for simultaneous wastewater treatment and bio-hydrogen production. The main objective is the maximization of bio-hydrogen production during the wastewater treatment process by MECs. The suggested strategy contains two main stages: modelling and optimal parameter identification. Firstly, using adaptive neuro-Fuzzy inference system (ANFIS) modelling, an accurate model of the MES was created. Secondly, the optimal parameters of the operating conditions were determined using the jellyfish optimizer (JO). Three operating variables were studied: incubation temperature (°C), initial potential of hydrogen (pH), and influent chemical oxygen demand (COD) concentration (%). Using some measured data points, the ANFIS model was built for simulating the output of MFC considering the operating parameters. Afterward, a jellyfish optimizer was applied to determine the optimal temperature, initial pH, and influent COD concentration values. To demonstrate the accuracy of the proposed strategy, a comparison with previous approaches was conducted. For the modelling stage, compared with the response surface methodology (RSM), the coefficient of determination increased from 0.8953 using RSM to 0.963 using ANFIS, by around 7.56%. In addition, the RMSE decreased from 0.1924 (using RSM) to 0.0302 using ANFIS, whereas for the optimal parameter identification stage, the optimal values were 30.2 °C, 6.53, and 59.98 (%), respectively, for the incubation temperature, the initial potential of hydrogen (pH), and the influent COD concentration. Under this condition, the maximum rate of the hydrogen production is 1.252 m3H2/m3d. Therefore, the proposed strategy successfully increased the hydrogen production from 1.1747 m3H2/m3d to 1.253 m3H2/m3d by around 6.7% compared to RSM. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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24 pages, 3618 KiB  
Article
Performance Assessment of an Ice-Production Hybrid Solar CPV/T System Combining Both Adsorption and Vapor-Compression Refrigeration Systems
by Mahmoud Badawy Elsheniti, Abdulrahman AlRabiah, Hany Al-Ansary, Zeyad Almutairi, Jamel Orfi and Abdelrahman El-Leathy
Sustainability 2023, 15(4), 3711; https://doi.org/10.3390/su15043711 - 17 Feb 2023
Cited by 4 | Viewed by 1544
Abstract
The technology of a hybrid solar concentration photovoltaic/thermal (CPV/T) system is an efficient way of converting solar energy to heat and electrical power, in which overall energy-extraction efficiency is at its highest. In this study, numerical dynamic simulation models were developed for a [...] Read more.
The technology of a hybrid solar concentration photovoltaic/thermal (CPV/T) system is an efficient way of converting solar energy to heat and electrical power, in which overall energy-extraction efficiency is at its highest. In this study, numerical dynamic simulation models were developed for a hybrid solar CPV/T system and an adsorption refrigeration system (ARS). Under the climatic conditions of Riyadh all year round, the electrical and thermal powers generated by the CPV/T system were used to estimate the ice production of both the vapor compression refrigeration system (VCS) and the ARS. The CPV/T system can provide a thermal energy of 37.6 kWh and electrical energy of 24.7 kWh a day on average over the year using a 12.5 m2 facing area of Fresnel lenses. The ARS employed an advanced approach which used Maxsorb III adsorbent packed in two aluminum foam beds. An optimum cycle time of the ARS was adapted for each month to match the variation in the thermal energy, while a variable-speed compressor was chosen for the VCS. Due to its higher coefficient of performance (COP), the proposed solar hybrid system can produce 494.4 kg of ice per day while sharing 84.5% of the VCS. The average solar COP over the year of the hybrid system can attain 0.875, which represents a promising value for a solar ice-production system. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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20 pages, 5513 KiB  
Article
A Hierarchical Coordinated Control Strategy for Power Quality Improvement in Energy Router Integrated Active Distribution Networks
by Xianyang Cui, Yulong Liu, Ding Yuan, Tao Jin and Mohamed A. Mohamed
Sustainability 2023, 15(3), 2655; https://doi.org/10.3390/su15032655 - 01 Feb 2023
Cited by 5 | Viewed by 1352
Abstract
The energy router (ER) is a current power electronic device which can integrate distributed energy, provide power for different types of loads, and simultaneously realize the free flow of energy. In traditional active distribution networks, power quality is affected due to the access [...] Read more.
The energy router (ER) is a current power electronic device which can integrate distributed energy, provide power for different types of loads, and simultaneously realize the free flow of energy. In traditional active distribution networks, power quality is affected due to the access of photovoltaics (PV) and various loads. Hence, this problem can be improved by accessing the ER. This paper shows the power quality improvement of the grid when the ER is used to integrate PV, energy storage, and AC/DC loads. At the same time, an energy coordination strategy for ER is proposed. The IEEE 13 node model is developed to analyze power quality fluctuations when distributed energy and AC/DC loads are directly connected to the grid. For the power quality analysis, five indicators were selected and the hierarchical analysis method was used to obtain the indicators of power quality. After the use of ER under the coordinated control of ER, the energy is distributed twice and the power quality of the grid improves. The feasibility of ER topology and the control strategy have been verified through an established active distribution networks model with ER. It is verified that when the ER is connected to active distribution networks, the power quality improves accordingly, and it can effectively deal with the characteristics of distributed energy fluctuations and improve the flexibility of the power grid. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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31 pages, 12476 KiB  
Article
Battery Power Control Strategy for Intermittent Renewable Energy Integrated Modular Multilevel Converter-Based High-Voltage Direct Current Network
by Md Ismail Hossain, Md Shafiullah and Mohammad A. Abido
Sustainability 2023, 15(3), 2626; https://doi.org/10.3390/su15032626 - 01 Feb 2023
Cited by 8 | Viewed by 1922
Abstract
Modular multilevel converters (MMC) play a dominant role in integrating remotely located renewable energy resources (RER) over the high-voltage direct current (HVDC) transmission network. The fault ride-through capabilities of the MMC-HVDC network during low-voltage faults and the power fluctuation due to RER intermittency [...] Read more.
Modular multilevel converters (MMC) play a dominant role in integrating remotely located renewable energy resources (RER) over the high-voltage direct current (HVDC) transmission network. The fault ride-through capabilities of the MMC-HVDC network during low-voltage faults and the power fluctuation due to RER intermittency are the major obstacles to the effective integration of renewable energy. In response, this article proposes a local voltage-based combined battery energy control scheme for a PV-wind-battery connected MMC-HVDC system to regulate the HVDC-link voltage during low-voltage faults at the point of common coupling of alternating current grids and to reduce the intermittent RER power fluctuation. The proposed technique removes the dynamic braking resistor from the HVDC-link and smoothly integrates the RER without active power reduction of renewable energy under low-voltage faults. Symmetrical and unsymmetrical low-voltage faults have been conducted to validate the effectiveness of the proposed control scheme for the battery in mitigating surplus energy in the HVDC-link. Additionally, wind speed, solar radiation, and temperature have been changed to confirm the improved performance of the battery energy management system. The complete systems have been simulated and tested in a real-time digital simulator (RTDS) and using dSPACE-based controller hardware in a loop setup. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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22 pages, 3405 KiB  
Article
Improved Prediction Model and Utilization of Pump as Turbine for Excess Power Saving from Large Pumping System in Saudi Arabia
by Zeyad Al-Suhaibani, Syed Noman Danish, Ziyad Saleh Al-Khalaf and Basharat Salim
Sustainability 2023, 15(2), 1014; https://doi.org/10.3390/su15021014 - 05 Jan 2023
Cited by 4 | Viewed by 1888
Abstract
The throttling process is frequently encountered in many industrial practices utilizing Pressure Reducing Valves (PRVs). This process is typically used to control pressure and flow in pipeline networks. The practice of utilizing PRVs is considered simple and cheap in terms of installation cost [...] Read more.
The throttling process is frequently encountered in many industrial practices utilizing Pressure Reducing Valves (PRVs). This process is typically used to control pressure and flow in pipeline networks. The practice of utilizing PRVs is considered simple and cheap in terms of installation cost and control. It dissipates the excess fluid energy that can be used for other purposes. This paper studies the feasibility of utilizing the Pump as Turbine (PAT) concept to partially recover the excess power dissipated from PRVs located at the discharge lines of refined product shipping pumps at one of the hydrocarbon distribution facilities in Saudi Arabia. Multiple PAT installation layouts have been studied to achieve this goal, selecting the optimum option to maximize the power recovery. The final selection of PAT was conducted to achieve a reasonable payback period. A new method for predicting the pump performance in reverse mode was developed depending on the manufacturer’s pump performance curves. The comparison of the proposed model with experimental data and previous models for three modes of operation reveals that the proposed model in this paper’s results either have the minimum deviation or the second minimum deviation out of all models. In the case of flow ratio prediction, the predicted deviation is merely 3.83%, −1.14%, and 1.35% in three modes of operation. For power prediction, the proposed model is the best and the only reliable model out of all with the least deviation of −7.48%, 0.07%, and −3.16% in three modes of operation. The economic analysis reveals the Capital Payback Time (CPP) for five optimum PATs is around 5 years. The new method was also validated against previous models showing more precise performance prediction of multistage centrifugal pumps running in turbine mode. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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17 pages, 9147 KiB  
Article
Simplified Model Predictive Current Control of Four-Level Nested Neutral Point Clamped Converter
by Rao Atif, Mannan Hassan, Muhammad Bilal Shahid, Hafiz Mudassir Munir, Mahmoud S. R. Saeed, Muhammad Shahzad, Semih Isik and Mohammed Alharbi
Sustainability 2023, 15(2), 955; https://doi.org/10.3390/su15020955 - 04 Jan 2023
Cited by 1 | Viewed by 1123
Abstract
Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared [...] Read more.
Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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15 pages, 5941 KiB  
Article
Submodule Fault-Tolerant Strategy for Modular Multilevel Converter with Scalable Control Structure
by Mohammed Alharbi, Semih Isik and Subhashish Bhattacharya
Sustainability 2022, 14(24), 16445; https://doi.org/10.3390/su142416445 - 08 Dec 2022
Viewed by 1058
Abstract
Modular Multilevel Converter (MMC) topology is considered a good candidate for high-voltage applications. One of the reasons is that an MMC can quickly generate a higher voltage with an excellent sine wave with the series connection of many power blocks, called Sub-Modules (SMs). [...] Read more.
Modular Multilevel Converter (MMC) topology is considered a good candidate for high-voltage applications. One of the reasons is that an MMC can quickly generate a higher voltage with an excellent sine wave with the series connection of many power blocks, called Sub-Modules (SMs). In such applications, the control system of an MMC can be challenging, and the possibility of an SM failure increases. As a result, the reliability and availability of the application reduce over time. To reduce the effects of SM failure, an MMC is usually equipped with Redundant SMs (RSMs). The RSMs are added into MMC arms as regular SMs to increase the application’s reliability and reduce downtime. This paper proposes a unique decentralized SM fault-tolerant control model for RSMs to participate in any SM sets. In an MMC arm, a dedicated controller is assigned to RSMs, while the group of SMs has their local controllers. The controller of the RSMs continually monitors the voltage of all the SM sets in the arm. If there is any failure, the controller of the RSMs activates a requested number of SMs to help local controllers to generate the desired voltage level. The proposed control system significantly reduces local controllers’ computational and communication requirements compared to conventional redundant controllers. The proposed control system is based on a distributed structure, so it does not limit hardware flexibility, such as the scalability and modularity of an MMC system. Besides, the separate controller for the RSMs significantly helps increase the reliability of an MMC application. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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21 pages, 6697 KiB  
Article
Productive and Sustainable H2 Production from Waste Aluminum Using Copper Oxides-Based Graphene Nanocatalysts: A Techno-Economic Analysis
by Mokhtar Ali Amrani, Yara Haddad, Firas Obeidat, Atef M. Ghaleb, Sobhi Mejjaouli, Ibrahim Rahoma, Mansour S. A. Galil, Mutahar Shameeri, Ahmed A. Alsofi and Amin Saif
Sustainability 2022, 14(22), 15256; https://doi.org/10.3390/su142215256 - 17 Nov 2022
Cited by 3 | Viewed by 2067
Abstract
Hydrogen has universally been considered a reliable source of future clean energy. Its energy conversion, processing, transportation, and storage are techno-economically promising for sustainable energy. This study attempts to maximize the production of H2 energy using nanocatalysts from waste aluminum chips, an [...] Read more.
Hydrogen has universally been considered a reliable source of future clean energy. Its energy conversion, processing, transportation, and storage are techno-economically promising for sustainable energy. This study attempts to maximize the production of H2 energy using nanocatalysts from waste aluminum chips, an abundant metal that is considered a potential storage tank of H2 energy with high energy density. The present study indicates that the use of waste aluminum chips in the production of H2 gas will be free of cost since the reaction by-product, Al2O3, is denser and can be sold at a higher price than the raw materials, which makes the production cost more efficient and feasible. The current framework investigates seven different copper oxide-based graphene nanocomposites that are synthesized by utilizing green methods and that are well-characterized in terms of their structural, morphological, and surface properties. Reduced graphene oxide (rGO) and multi-layer graphene (MLG) are used as graphene substrates for CuO and Cu2O NPs, respectively. These graphene materials exhibited extraordinary catalytic activity, while their copper oxide composites exhibited a complete reaction with feasible techno-economic production. The results revealed that the H2 production yield and rates increased twofold with the use of these nanocatalysts. The present study recommends the optimum reactor design considerations and reaction parameters that minimize water vaporization in the reaction and suggests practical solutions to quantify and separate it. Furthermore, the present study affords an economic feasibility approach to producing H2 gas that is competitive and efficient. The cost of producing 1 kg of H2 gas from waste aluminum chips is USD 6.70, which is both economically feasible and technically applicable. The unit cost of H2 gas can be steeply reduced by building large-scale plants offering mass production. Finally, the predicted approach is applicable in large, medium, and small cities that can collect industrial waste aluminum in bulk to generate large-scale energy units. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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13 pages, 3302 KiB  
Article
A Robust Artificial Bee Colony-Based Load Frequency Control for Hydro-Thermal Interconnected Power System
by Ahmed Fathy, Ahmed Kassem and Zaki A. Zaki
Sustainability 2022, 14(20), 13569; https://doi.org/10.3390/su142013569 - 20 Oct 2022
Cited by 2 | Viewed by 1212
Abstract
The presented work examines load frequency control (LFC) to develop the dynamic behavior of the power system under different load disturbances that have occurred in multi-interconnected power systems. An artificial bee colony (ABC) algorithm is proposed to design an optimal proportional integral derivative [...] Read more.
The presented work examines load frequency control (LFC) to develop the dynamic behavior of the power system under different load disturbances that have occurred in multi-interconnected power systems. An artificial bee colony (ABC) algorithm is proposed to design an optimal proportional integral derivative (PID) controller simulating the LFC installed in a hybrid hydro-thermal interconnected power system. The proposed approach incorporating ABC is employed to determine the optimal parameters of the controller during load disturbance applied on one area. The integral time absolute error (ITAE) of the frequency and exchange power violations is considered as the target to be minimized. Moreover, integral absolute error (IAE) and sum squared error (SSE) are calculated. To prove how the proposed model controller is effective, two-interconnected power systems are presented during a wide range of operating cases, and then the behavior of the proposed controller is compared to that of the designed via a chef-based optimization algorithm (CBOA), seagull optimization approach (SOA), and sine cosine approach. Regarding the 5% disturbance on the thermal plant, the ABC outperformed the other approaches hence achieving the best fitness value of 1.80936, IAE of 3.147938, and SSE of 0.1787486. On the other hand, during a 5% disturbance on the hydro plant, the ABC succeeded in getting ITAE, IAE, and SSE with values of 3.43291, 3.630509, and 0.5233815, respectively. The efficiency and prevalence of the proposed LFC-PID is confirmed by the achieved results. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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20 pages, 5857 KiB  
Article
Optimum Size of Hybrid Renewable Energy System to Supply the Electrical Loads of the Northeastern Sector in the Kingdom of Saudi Arabia
by Sulaiman Alshammari and Ahmed Fathy
Sustainability 2022, 14(20), 13274; https://doi.org/10.3390/su142013274 - 15 Oct 2022
Cited by 6 | Viewed by 1559
Abstract
Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. [...] Read more.
Due to the unpredictable nature of renewable sources such as sun and wind, the integration of such sources to a grid is complicated. However, a hybrid renewable energy system (HRES) can solve this problem. Constructing a reliable HRES in remote areas is essential. Therefore, this paper proposes a new methodology incorporating a crow search algorithm (CSA) for optimizing the scale of an HRES installed in a remote area. The constructed system comprises photovoltaic (PV) panels, wind turbines (WTs), batteries, and diesel generators (DGs). The target is to achieve the most economical and efficient use of renewable energy sources (RESs). The CSA is used as it is simple in implementation, it only requires a few parameters, and it has a high flexibility. The designed system is constructed to serve an electrical load installed in the northeastern region of the Kingdom of Saudi Arabia. The load data are provided by the Saudi Electricity Company, including those of the Aljouf region (Sakaka, Alqurayyat, Tabarjal, Dumat Aljandal, and its villages) and the northern border region (Arar, Tarif, Rafha, and its affiliated villages). The temperature, irradiance, and wind speed of the Aljouf region (latitude 29.764° and longitude 40.01°) are collected from the National Aeronautics and Space Administration (NASA) from 1 January to 31 December 2020. Three design factors are considered: the PV number, the WT number, and the number of days of battery autonomy (AD). We compared our results to the reported approaches of an elephant herding optimizer (EHO), a grasshopper optimization algorithm (GOA), a Harris hawks optimizer (HHO), a seagull optimization algorithm (SOA), and a spotted hyena optimizer (SHO). Moreover, the loss of power supply probability (LPSP) is calculated to assess the constructed system’s reliability. The proposed COA succeeded in achieving the best fitness values of 0.03883 USD/kWh, 0.03863 USD/kWh, and 0.04585 USD/kWh for PV/WT/battery, PV/battery, and WT/battery systems, respectively. The obtained results confirmed the superiority of the proposed approach in providing the best configuration of an HRES compared to the others. Full article
(This article belongs to the Special Issue Smart Grid Technologies and Renewable Energy Applications)
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