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Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Republic of Korea

MCAST Energy Research Group, Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Corradino Hill, PLA 1260 Paola, Malta

Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities

Abstract submission deadline

closed (31 October 2023)

Manuscript submission deadline

closed (31 December 2023)

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56720

Topic Information

Dear Colleagues,

Recently, EVs, SGs, energy storage, and smart cities have been gathering pace throughout the world, and advanced technologies have been introduced in the sustainable and reliable power grids for the optimal utilization of SGs and EVs for smart cities. The large-scale penetration of renewable energy sources (RESs) and EVs has presented profound challenges and opportunities to the power industry. The inherent variability and uncertainty associated with RESs have changed several aspects of the control, operation, and planning of power networks. However, the growing penetration of renewable energy resources and electric vehicles will bring more complexity to operation and planning tasks, which play an important role in a gradual transition of the traditional power grid to the smart grid—a driver for the development of smart cities. Smart cities rely on widely distributed smart devices, and they are designed to address various challenges, such as pollution, efficient utilization of energy, security, pollution, parking, traffic, and transportation. We would like to invite submissions to this Topic titled “Emerging Trends in Electric Vehicles, Smart Grids, and Smart Cities” to collect the latest developments, advanced technologies, and applications in these fields. The topics of interest include but are not limited to:

Electric vehicle planning and operation in the smart grid
Smart grid and smart cities modeling
Modeling flexibility of distributed energy resources
Smart grid and green energy integration
Distributed generation and distributed storage
Electricity market modeling and simulation for the integration of renewable sources
The Internet of Things (IoT) for smart cities
Energy management system in smart distribution grids
Role of Artificial Intelligence (AI) in smart grid and smart cities revolution
Smart grid technology and solutions for smart cities

Dr. Surender Salkuti
Prof. Dr. Brian Azzopardi
Topic Editors

Keywords

smart grid
electric vehicles
energy storage
smart cities
renewable energy sources
distributed generation
artificial intelligence
demand response
optimization
sustainable energy

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Smart Cities smartcities	6.4	8.5	2018	20.2 Days	CHF 2000
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400
World Electric Vehicle Journal wevj	2.3	3.7	2007	14.1 Days	CHF 1400

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Published Papers (28 papers)

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28 pages, 13205 KiB

Open AccessArticle

Predicting User Preference for Innovative Features in Intelligent Connected Vehicles from a Cultural Perspective

by Jun Ma, Yuqi Gong and Wenxia Xu

World Electr. Veh. J. 2024, 15(4), 130; https://doi.org/10.3390/wevj15040130 - 25 Mar 2024

Viewed by 584

Abstract

The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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20 pages, 524 KiB

Open AccessArticle

Marketing Strategy and Preference Analysis of Electric Cars in a Developing Country: A Perspective from the Philippines

by John Robin R. Uy, Ardvin Kester S. Ong and Josephine D. German

World Electr. Veh. J. 2024, 15(3), 111; https://doi.org/10.3390/wevj15030111 - 14 Mar 2024

Viewed by 1385

Abstract

The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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17 pages, 595 KiB

Open AccessArticle

Electric Vehicle Ordered Charging Planning Based on Improved Dual-Population Genetic Moth–Flame Optimization

by Shuang Che, Yan Chen, Longda Wang and Chuanfang Xu

Algorithms 2024, 17(3), 110; https://doi.org/10.3390/a17030110 - 06 Mar 2024

Cited by 1 | Viewed by 796

Abstract

This work discusses the electric vehicle (EV) ordered charging planning (OCP) optimization problem. To address this issue, an improved dual-population genetic moth–flame optimization (IDPGMFO) is proposed. Specifically, to obtain an appreciative solution of EV OCP, the design for a dual-population genetic mechanism integrated into moth–flame optimization is provided. To enhance the global optimization performance, the adaptive nonlinear decreasing strategies with selection, crossover and mutation probability, as well as the weight coefficient, are also designed. Additionally, opposition-based learning (OBL) is also introduced simultaneously. The simulation results show that the proposed improvement strategies can effectively improve the global optimization performance. Obviously, more ideal optimization solution of the EV OCP optimization problem can be obtained by using IDPGMFO. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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18 pages, 3439 KiB

Open AccessArticle

Connecting the Dots: A Comprehensive Modeling and Evaluation Approach to Assess the Performance and Robustness of Charging Networks for Battery Electric Trucks and Its Application to Germany

by Georg Balke, Maximilian Zähringer, Jakob Schneider and Markus Lienkamp

World Electr. Veh. J. 2024, 15(1), 32; https://doi.org/10.3390/wevj15010032 - 18 Jan 2024

Viewed by 1340

Abstract

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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18 pages, 16740 KiB

Open AccessArticle

Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

by Alfredo Medina-Garcia, Jonathan Duarte-Jasso, Juan-Jose Cardenas-Cornejo, Yair A. Andrade-Ambriz, Marco-Antonio Garcia-Montoya, Mario-Alberto Ibarra-Manzano and Dora-Luz Almanza-Ojeda

Smart Cities 2024, 7(1), 33-50; https://doi.org/10.3390/smartcities7010002 - 22 Dec 2023

Viewed by 1293

Abstract

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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25 pages, 8152 KiB

Open AccessArticle

Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

by Lingling Hu, Junming Zhou, Feng Jiang, Guangming Xie, Jie Hu and Qinglie Mo

Sustainability 2024, 16(1), 57; https://doi.org/10.3390/su16010057 - 20 Dec 2023

Viewed by 632

Abstract

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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17 pages, 3622 KiB

Open AccessArticle

OrthoDETR: A Streamlined Transformer-Based Approach for Precision Detection of Orthopedic Medical Devices

by Xiaobo Zhang, Huashun Li, Jingzhao Li and Xuehai Zhou

Algorithms 2023, 16(12), 550; https://doi.org/10.3390/a16120550 - 29 Nov 2023

Viewed by 1135

Abstract

The rapid and accurate detection of orthopedic medical devices is pivotal in enhancing health care delivery, particularly by improving workflow efficiency. Despite advancements in medical imaging technology, current detection models often fail to meet the unique requirements of orthopedic device detection. To address this gap, we introduce OrthoDETR, a Transformer-based object detection model specifically designed and optimized for orthopedic medical devices. OrthoDETR is an evolution of the DETR (Detection Transformer) model, with several key modifications to better serve orthopedic applications. We replace the ResNet backbone with the MLP-Mixer, improve the multi-head self-attention mechanism, and refine the loss function for more accurate detections. In our comparative study, OrthoDETR outperformed other models, achieving an AP50 score of 0.897, an AP50:95 score of 0.864, an AR50:95 score of 0.895, and a frame per second (FPS) rate of 26. This represents a significant improvement over the DETR model, which achieved an AP50 score of 0.852, an AP50:95 score of 0.842, an AR50:95 score of 0.862, and an FPS rate of 20. OrthoDETR not only accelerates the detection process but also maintains an acceptable performance trade-off. The real-world impact of this model is substantial. By facilitating the precise and quick detection of orthopedic devices, OrthoDETR can potentially revolutionize the management of orthopedic workflows, improving patient care, and enhancing the efficiency of healthcare systems. This paper underlines the significance of specialized object detection models in orthopedics and sets the stage for further research in this direction. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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31 pages, 7790 KiB

Open AccessArticle

Optimal Dispatch Strategy for Electric Vehicles in V2G Applications

by Ali M. Eltamaly

Smart Cities 2023, 6(6), 3161-3191; https://doi.org/10.3390/smartcities6060141 - 20 Nov 2023

Cited by 1 | Viewed by 1234

Abstract

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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33 pages, 34118 KiB

Open AccessArticle

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

by Mehul Bose, Bivas Ranjan Dutta, Nivedita Shrivastava and Smruti R. Sarangi

Smart Cities 2023, 6(6), 3060-3092; https://doi.org/10.3390/smartcities6060137 - 15 Nov 2023

Viewed by 1096

Abstract

The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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28 pages, 6610 KiB

Open AccessArticle

Optimizing Energy Usage and Smoothing Load Profile via a Home Energy Management Strategy with Vehicle-to-Home and Energy Storage System

by Modawy Adam Ali Abdalla, Wang Min, Gehad Abdullah Amran, Amerah Alabrah, Omer Abbaker Ahmed Mohammed, Hussain AlSalman and Bassiouny Saleh

Sustainability 2023, 15(20), 15046; https://doi.org/10.3390/su152015046 - 19 Oct 2023

Viewed by 884

Abstract

This study investigates an energy utilization optimization strategy in a smart home for charging electric vehicles (EVs) with/without a vehicle-to-home (V2H) and/or household energy storage system (HESS) to improve household energy utilization, smooth the load profile, and reduce electricity bills. The proposed strategy detects EV arrival and departure time, establishes the priority order between EV and HESS during charge and discharge, and ensures that the EV battery state of energy at the departure time is sufficient for its travel distance. It also ensures that the EV and HESS are charged when electricity prices are low and discharged in peak hours to reduce net electricity expenditure. The proposed strategy operates in different modes to control the energy amount flowing from the grid to EV and/or HESS and the energy amount drawn from the HESS and/or EV to feed the demand to maintain the load curve level within the average limits of the daily load curve. Four different scenarios are presented to investigate the role of HESS and EV technology in reducing electricity bills and smoothing the load curve in the smart house. The results demonstrate that the proposed strategy effectively reduces electricity costs by 12%, 15%, 14%, and 17% in scenarios A, B, C, and D, respectively, and smooths the load profile. Transferring valley electricity by V2H can reduce the electricity costs better than HESS, whereas HESS is better than EV at flattening the load curve. Transferring valley electricity through both V2H and HESS gives better results in reducing electricity costs and smoothing the load curve than transferring valley electricity by HESS or V2H alone. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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27 pages, 2488 KiB

Open AccessArticle

Elevating B2B Mobility with Sharing Autonomous Electric Vehicles: Exploring Prerequisite Criteria and Innovative Business Models

by Koteshwar Chirumalla, Sara Klaff, Rania Zako and Anna Sannö

Sustainability 2023, 15(18), 13757; https://doi.org/10.3390/su151813757 - 15 Sep 2023

Viewed by 989

Abstract

The transition towards a circular economy compels manufacturing companies in the transportation industry to reassess how they create, deliver, and capture value for their customers. Autonomous electric vehicles, with their advanced connectivity, autonomy, and efficiency, offer innovative business opportunities and services. However, there is limited knowledge concerning the sharing of autonomous electric vehicles in the business-to-business (B2B) market, particularly for industrial manufacturing companies. This study aims to identify the prerequisite criteria and potential innovative business models for sharing autonomous electric vehicles within a B2B context. To investigate this phenomenon, the study employs a case study approach within the heavy-duty vehicle industry, which involves a vehicle manufacturer and customers from a specific industry sector. The findings reveal that economic gain, service quality, and accessibility serve as prerequisite criteria for sharing autonomous electric vehicles in a B2B context. Furthermore, by leveraging a morphological framework, the study outlines five business model scenarios to explore the potential of sharing autonomous electric vehicles in enhancing B2B mobility. This research contributes to the field of business model innovation in a B2B context by introducing a model that delineates both the prerequisite criteria and potential business model concepts for the B2B sharing of autonomous electric vehicles. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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22 pages, 2879 KiB

Open AccessCase Report

Optimum Scenarios of EV Charging Infrastructure: A Case Study for the Saudi Arabia Market

by Mohamed Azab

Energies 2023, 16(13), 5186; https://doi.org/10.3390/en16135186 - 05 Jul 2023

Viewed by 1285

Abstract

The lack of an EV charging infrastructure is of the top five barriers preventing the adoption of EVs on a large scale. A long charging time is also one of the five barriers, according to the latest survey published by the IEA in 2021. The estimated increase in demand for EVs is a big challenge in many countries all around the world. This challenge exists in many EU and Middle East countries. The main reason for this problem is the requirement of huge funds to install enough public charging points that result in satisfactory charging services. Hence, the phase-out plans of internal combustion engine (ICE) vehicles can be carried out successfully and smoothly. Unfortunately, there is a trade-off between the cost of installing charging points and EV charging time. Therefore, it is important to optimize both factors simultaneously. This way, the charging services can be provided at the minimum possible cost and at a satisfactory level of quality. This study determines the optimum ratio of the number of chargers to the number of EVs in a certain province. The optimal number of chargers that are necessary to optimally serve a certain number of EVs has been determined. Two well-known evolutionary search techniques have solved the optimization problem: particle swarm optimization (PSO) and genetic algorithms (GA). Both algorithms have succeeded in providing many optimal charging infrastructure scenarios. Hence, the decision maker can select the most convenient scenario from several alternatives based on the available budgets and the most convenient charging time. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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23 pages, 5940 KiB

Open AccessArticle

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

by Yuhang Bai and Yun Zhu

Energies 2023, 16(13), 4879; https://doi.org/10.3390/en16134879 - 22 Jun 2023

Viewed by 899

Abstract

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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25 pages, 3314 KiB

Open AccessArticle

Assessment of Electric Two-Wheelers Development in Establishing a National E-Mobility Roadmap to Promote Sustainable Transport in Vietnam

by Dinh Van Hiep, Nam Hoai Tran, Nguyen Anh Tuan, Tran Manh Hung, Ngo Viet Duc and Hoang Tung

Sustainability 2023, 15(9), 7411; https://doi.org/10.3390/su15097411 - 29 Apr 2023

Cited by 2 | Viewed by 3953

Abstract

Faced with increasing environmental pollution due to traffic concentration in big cities, Vietnam, as well as many countries worldwide, has encouraged its people to use environmentally-friendly vehicles. Because the transport mode is dominated by two-wheelers (i.e., motorcycles and mopeds) (2Ws), electrifying 2Ws has the potential for significant air pollution reductions as an alternative to gasoline-powered vehicles in Vietnam. Therefore, there has recently been an increasing trend of shifting from traditional gasoline two-wheeler vehicles to electric two-wheelers (E2Ws). Depending on different local contexts, some countries/regions quickly adopted the policies/incentives, and new technologies for E2W usage, while others acted more slowly. In order to advance the use of E2Ws in Vietnam, assessing E2W user preferences is essential to classify and prioritize further solutions, which would be instrumental in fulfilling user expectations. However, a few academic works pay attention to this field of the Vietnamese E2W market. In response to this research gap, this paper aims to overview the current status of E2W usage, assess the market development of E2Ws, and evaluate the battery charging business models in Vietnam. The questionnaire survey was carried out to evaluate the preferences of E2W users in the Vietnamese market, while the assessment of E2W development was conducted based on the SWOT (strengths, weaknesses, opportunities, and threats) analysis. The results demonstrated that E2W deployment is still at an exploratory stage in the transportation industry and is growing significantly in Vietnam. This study also revealed significant challenges for E2W adoption, especially the E2W battery charging/swapping system. Thus, it is recommended that incentives for E2W uptake and the battery charging infrastructure system should be improved and implemented. The evaluation of E2W perceptions in the three-city context is realized as exploratory, generating the baseline for further research when the survey can engage more respondents in other places to confirm the current research findings. The study can also assist policymakers and investors in comprehensively assessing the opportunities and challenges and provide recommendations for accelerating the growth of E2Ws in Vietnam for establishing a national e-mobility roadmap and thereby promoting sustainable transport in alignment with the COP26. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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35 pages, 6941 KiB

Open AccessReview

An Overview of Major Synthetic Fuels

by Vishal Ram and Surender Reddy Salkuti

Energies 2023, 16(6), 2834; https://doi.org/10.3390/en16062834 - 18 Mar 2023

Cited by 7 | Viewed by 7491

Abstract

Artificial fuels have been researched for more than a decade now in an attempt to find alternative sources of energy. With global climatic conditions rapidly approaching the end of their safe line, an emphasis on escalating the change has been seen in recent times. Synthetic fuels are a diverse group of compounds that can be used as replacements for traditional fuels, such as gasoline and diesel. This paper provides a comprehensive review of synthetic fuels, with a focus on their classification and production processes. The article begins with an in-depth introduction, followed by virtually classifying the major synthetic fuels that are currently produced on an industrial scale. The article further discusses their feedstocks and production processes, along with detailed equations and diagrams to help readers understand the basic science behind synthetic fuels. The environmental impact of these fuels is also explored, along with their respective key players in the industry. By highlighting the benefits and drawbacks of synthetic fuels, this study also aims to facilitate an informed discussion about the future of energy and the role that synthetic fuels may play in reducing our reliance on fossil fuels. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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16 pages, 3517 KiB

Open AccessArticle

Study on the Spatial Pattern and Influencing Factors of China’s New Energy Vehicle Industry—Based on Data of Relevant Listed Companies from 2008–2021

by Daoyuan Chen, Guoen Wang, Ziwei Yuan and Ershen Zhang

Sustainability 2023, 15(4), 3058; https://doi.org/10.3390/su15043058 - 08 Feb 2023

Cited by 1 | Viewed by 1702

Abstract

As a pillar industry carrying China’s ambition in manufacturing upgrades and energy transformation, the new energy vehicle (NEV) industry has received much attention from the government and investment institutions. The spatial pattern of the industry, which is undergoing dramatic changes, urgently needs to be studied retrospectively. This paper explores the spatial distribution pattern of related industries and their influencing factors using data related to NEV industry listed companies from 2008 to 2021. Spatial statistical analysis and stepwise regression analysis were conducted in this study. At the national level, the study found that an “8” industrial axis was formed, with the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei city cluster as the core. At the provincial level, it was found that the traditional auto industry-dominant regions do not have a competitive advantage in the NEV field. After stepwise regression of the potential factors, five key factors determining the number of listed NEV enterprises in each province were identified, namely, policy strength, patents, per capita wage, tertiary industry share, and road density. The research results improve the understanding of NEV industry development rules in related disciplines and provide a reference for the spatial arrangement of the NEV industry to be coordinated and optimized at the regional level. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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16 pages, 2773 KiB

Open AccessArticle

Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders

by Sopheap Key, Chang-Sung Ko, Kwang-Jae Song and Soon-Ryul Nam

Energies 2023, 16(3), 1528; https://doi.org/10.3390/en16031528 - 03 Feb 2023

Cited by 3 | Viewed by 1906

Abstract

Malfunctions in relay protection devices are predominantly caused by current transformer (CT) saturation which produces distortion in current measurements and disturbances in power system protection. The development of deep learning in power system protection is on the rise recently because of its robustness. This study presents a CT saturation detection where the secondary current becomes distorted. The proposed scheme offers a wide range of saturation detection and consists of a moving-window technique and stacked denoising autoencoders. Moreover, Bayesian optimization was used to minimize the difficulty of determining neural network structure for the proposed approach. The performance of the algorithm was evaluated for a-g faults on 154 kV and 345 kV overhead transmission line in South Korea. The waveform variation has been generated by PSCAD for different scenarios that heavily influence CT saturation. Moreover, a comparative analysis with other methods demonstrated the superiority of the proposed DNN method. With the proposed algorithm to detect CT saturation, it significantly yielded high accuracy and precision for CT saturation detection which were approximately 99.71% and 99.32%, respectively. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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30 pages, 16595 KiB

Open AccessArticle

Emerging Cybersecurity and Privacy Threats to Electric Vehicles and Their Impact on Human and Environmental Sustainability

by Zia Muhammad, Zahid Anwar, Bilal Saleem and Jahanzeb Shahid

Energies 2023, 16(3), 1113; https://doi.org/10.3390/en16031113 - 19 Jan 2023

Cited by 18 | Viewed by 5819

Abstract

With the global energy crisis, increasing demand, and a national-level emphasis on electric vehicles (EVs), numerous innovations are being witnessed throughout the EV industry. EVs are equipped with sensors that maintain a sustainable environment for the betterment of society and enhance human sustainability. However, at the same time, as is the case for any new digital technology, they are susceptible to threats to security and privacy. Recent incidents demonstrate that these sensors have been misused for car and energy theft, financial fraud, data compromise, and have caused severe health and safety problems, amongst other things. To the best of our knowledge, this paper provides a first systematic analysis of EV sustainability, digital technologies that enhance sustainability, their potential cybersecurity threats, and corresponding defense. Firstly, three robust taxonomies have been presented to identify the dangers that can affect long-term sustainability domains, including (1) life and well-being, (2) safe environment, and (3) innovation and development. Second, this research measures the impact of cybersecurity threats on EVs and correspondingly to their sustainability goals. Third, it details the extent to which specific security controls can mitigate these threats, thereby allowing for a smooth transition toward secure and sustainable future smart cities. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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15 pages, 5496 KiB

Open AccessArticle

Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System

by Bishwajit Dey, Soham Dutta and Fausto Pedro Garcia Marquez

Sustainability 2023, 15(3), 1795; https://doi.org/10.3390/su15031795 - 17 Jan 2023

Cited by 9 | Viewed by 1670

Abstract

In a typical microgrid (MG) structure, the requisite of load varies from hour to hour. On the basis of the rise and fall of the load demand curve, the power system utilities fix the rate of electric power at different times of the day. This process is known as time-of-usage (TOU)-based pricing of electricity. The hourly basis load demand can be categorized into elastic hourly load demand and inelastic hourly load demand. For the duration of the peak hours, when the utility charges more, the elastic loads are shifted to low demand hours by the demand side management (DSM) to save the cost. This rebuilds the total demand model on the pillars of demand price elasticity. Keeping in view the fact that the total load in an hour in an MG structure consists of 10% to 40% of elastic loads, the paper proposes an intelligence-technique-based DSM to achieve reduction in the overall cost of using loads in an MG structure. Seven different cases are studied which cover diverse grid participation and electricity market pricing strategies, including DSM programs. The results obtained for all the MGs showcase the applicability and appropriateness of using the proposed DSM strategy in terms of cost savings. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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14 pages, 6789 KiB

Open AccessArticle

Optimized Power Pads for Charging Electric Vehicles Based on a New Rectangular Spiral Shape Design

by Nadir Benalia, Kouider Laroussi, Idriss Benlaloui, Abdellah Kouzou, Abed-Djebar Bensalah, Ralph Kennel and Mohamed Abdelrahem

Sustainability 2023, 15(2), 1230; https://doi.org/10.3390/su15021230 - 09 Jan 2023

Cited by 1 | Viewed by 1562

Abstract

Electric vehicles (EVs) can be charged wirelessly using inductive charging technology. This process has a number of advantages in terms of automation, safety in harsh environments, reliability in the event of natural disasters and adaptability. On the other hand, the inductive charger has many issues, including a complex design, sensitivity to misalignment, safety concerns, and a high cost. The transmitting and receiving coils are the primary causes of the cited problems. This paper presents an in-depth study of an electric vehicle charging system based on the magnetic coupling between two coils by introducing different materials to concentrate the magnetic flux and hence improving the overall efficiency of the charging system and its design. Three situations of the magnetic coupling between two identical rectangular coils as a function of both the horizontal (X axis) and vertical (Z axis) alignment are examined. In the first case, the analysis of the magnetic coupling between two copper coils separated by an air gap is presented. The results show that the magnitude of the fields decreases according to the distance between the transmitter and the receiver coils and the obtained coupling coefficient was very low with a high leakage flux which affected the performance of the charging system. In the second case, a straightforward shielding method that involves inserting a magnetic material of the ferrite type is proposed to overcome these problems. The use of ferrite magnetic shielding contributes to channeling the field lines as well as reducing leakage flux which makes the transmitted power higher. This perspective shows that simple shielding is still only a partial and insufficient solution. In the third situation, an aluminum sheet was consequently placed on the top of the ferrite to provide an adequate shielding structure. A 3D analysis of the self and mutual induction parameters separating the two coils as well as a magnetic field is also performed using the Ansys Maxwell software. The results highlight the significance of the enhanced proposed design. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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18 pages, 9315 KiB

Open AccessArticle

A Comprehensive Performance Comparison between Segmental and Conventional Switched Reluctance Machines with Boost and Standard Converters

by Yuanfeng Lan, Julien Croonen, Mohamed Amine Frikha, Mohamed El Baghdadi and Omar Hegazy

Energies 2023, 16(1), 43; https://doi.org/10.3390/en16010043 - 21 Dec 2022

Cited by 1 | Viewed by 1053

Abstract

This paper presents the comparisons between two types of switched reluctance machines (SRMs) and SRM converters. An SRM with a segmental rotor is compared with a conventional SRM (CSRM), and an SRM converter containing a passive boost circuit is compared with a conventional asymmetric half-bridge (AHB) converter. The segmental SRM has an asymmetric rotor with a segmented structure. The four rotor segments are made of steel laminations. Two segments are misaligned with the other two by 15 degrees. The torque ripple of the SRM with this structure is decreased, and the static torque is increased compared to a conventional SRM. The boost converter comprises a front-end circuit and a conventional AHB converter. The front-end circuit boosts the voltage level. The boosted voltage accelerates the rising and falling progress of the phase current. In this way, the SRM can obtain a greater speed and a smaller torque ripple. The comparison is conducted in simulation and validated through the experimental results. The experiment results have demonstrated that the segmental SRM obtains a maximum 7% torque ripple reduction at a low-speed range, compared to the CSRM. With the boost converter, both the CSRM and the segmental SRM can achieve a lower torque ripple and a higher maximum speed. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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27 pages, 9804 KiB

Open AccessArticle

Soft-Switching Smart Transformer Design and Application for Photovoltaic Integrated Smart City Power Distribution

by Burak Esenboğa and Tuğçe Demirdelen

Sustainability 2023, 15(1), 32; https://doi.org/10.3390/su15010032 - 20 Dec 2022

Cited by 2 | Viewed by 1905

Abstract

Smart city power distributions have become promising technologies to meet the demand for energy in developed countries. However, increase in smart grids causes several power quality problems on the smart grid, in particular, current and voltage harmonic distortions, sudden voltage sag and swells, fault current, and isolation deterioration. Smart transformers are potential solutions to improve the power quality on the electric grid. They present energy efficiency, ensure grid reliability and power flow control, voltage regulation, bidirectional power flow, fault current limiting, harmonic blocking, and galvanic isolation. Therefore, this paper offers an optimal selection of a three-stage (AC-DC-DC-AC) smart transformer model and power control strategy for solar PV power plant integrated smart grids. The topology of the rectifier, isolated bidirectional converter, and inverter has soft-switching features. This enables low conduction loss, low electromagnetic interference (EMI), high efficiency, achievable zero-voltage switching for converters, and zero-current switching for electrical auxiliary systems. Operation strategies of the proposed ST, PWM control, voltage, and current control between converters, including a medium-voltage (MV) high-frequency transformer to realize a 10 kVA, 450 Vdc to 220 Vdc, or 220 Vac ST, are presented. Significantly, the ST prototype achieves 96.7% conversion efficiency thanks to its control strategy, even under unstable power generation conditions from the solar PV plant. Experimental results obtained on the 344 Vac 10.4 A load current validates the dv/dt rate 6.8 kV/us. The dynamic and experimental results of the proposed bidirectional smart transformer demonstrate the success in preventing power quality problems for photovoltaic integrated smart city power distribution. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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24 pages, 1483 KiB

Open AccessReview

An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks

by Morteza Nazari-Heris, Mehdi Abapour and Behnam Mohammadi-Ivatloo

Sustainability 2022, 14(23), 15747; https://doi.org/10.3390/su142315747 - 26 Nov 2022

Cited by 9 | Viewed by 2266

Abstract

Electric vehicles (EVs) are predicted to be highly integrated into future smart grids considering their significant role in achieving a safe environment and sustainable transportation. The charging/discharging flexibility of EVs, which can be aggregated by an agent, provides the opportunity of participating in the demand-side management of energy networks. The individual participation of consumers at the system level would not be possible for two main reasons: (i) In general, their individual capacity is below the required minimum to participate in power system markets, and (ii) the number of market participants would be large, and thus the volume of individual transactions would be difficult to manage. In order to facilitate the interactions between consumers and the power grid, an aggregation agent would be required. The EV aggregation area and their integration challenges and impacts on electricity markets and distribution networks is investigated in much research studies from different planning and operation points of view. This paper aims to provide a comprehensive review and outlook on EV aggregation models in electrical energy systems. The authors aim to study the main objectives and contributions of recent papers and investigate the proposed models in such areas in detail. In addition, this paper discusses the primary considerations and challenging issues of EV aggregators reported by various research studies. In addition, the proposed research outlines the future trends around electric vehicle aggregators and their role in electrical energy systems. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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14 pages, 1259 KiB

Open AccessArticle

Measuring Customers’ Satisfaction and Preferences for Ride-Hailing Services in a Developing Country

by Nazam Ali, Muhammad Ashraf Javid, Tiziana Campisi, Krisada Chaiyasarn and Panumas Saingam

Sustainability 2022, 14(22), 15484; https://doi.org/10.3390/su142215484 - 21 Nov 2022

Cited by 2 | Viewed by 4045

Abstract

Ride-hailing services play an important role in developing countries where conventional transport systems are not enough to meet the needs of commuters because of increased populations. This form of transport has gained much popularity in developing regions because of the inclusion of motorcycles and rikshaws in ride-hailing services. To the best of the authors’ knowledge, there has been little research on passengers’ behavior towards these ride-hailing services that focuses on social protection and the fare system in developing regions. Therefore, this research study is aimed at investigating the behavior of commuters towards these ride-hailing services in Lahore, which is the second largest city in Pakistan and can be considered as a case study of a developing country. A total of 531 useable valid responses were collected through face-to-face interactions, including the sociodemographics (SEDs) and behavior of commuters towards these services. The results of an explanatory factor analysis (EFA) and structural equation modeling (SEM) revealed that some of the significant latent variables of these ride-hailing services are comfort, convenience, privacy and security, the fare system, social protection, and safety. The commuters’ overall evaluation of these services is positive and affects their present and future preferences. The structural coefficient between convenience and the variable of present preference is significant and negative, which shows that there are respondents who infrequently use ride-hailing services despite having high satisfaction. The riders’ satisfaction with privacy, security, social protection, safety, and comfort has a positive and direct impact on their present preferences as the structural estimates are positive, which means that the higher their views on privacy, security, and comfort, the more frequently they intend to use ride-hailing services for commuting. Increased social protection, safety, privacy, and security will improve the evaluations of the commuters and influence their present preferences for these ride-hailing services. Even though there are regulations on these ride-hailing services, some concrete policy interventions are needed for improvements in commuters’ overall evaluations of these services in order to influence their future preferences. The findings of this research study, if applied in the real world, can improve the overall evaluation of the commuters and positively influence their present and future preferences for these ride-hailing services. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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22 pages, 16819 KiB

Open AccessArticle

Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System

by Ajit Kumar Mohanty, Perli Suresh Babu and Surender Reddy Salkuti

Energies 2022, 15(22), 8702; https://doi.org/10.3390/en15228702 - 19 Nov 2022

Cited by 11 | Viewed by 1653

Abstract

Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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21 pages, 6448 KiB

Open AccessArticle

Power Quality Analysis by H-Bridge DSTATCOM Control by Icosθ and ESRF SOGI-FLL Methods for Different Industrial Loads

by Srikanth Islavatu, Pradeep Kumar, Amit Kumar and Surender Reddy Salkuti

Smart Cities 2022, 5(4), 1590-1610; https://doi.org/10.3390/smartcities5040081 - 17 Nov 2022

Cited by 3 | Viewed by 1526

Abstract

This work develops the analysis of power quality by the H-bridge Static Distribution Compensator (DSTATCOM) as well as its control techniques in different industry-based loading conditions. The function of DSTATCOM is to diminish power quality problems arising due to commercial as well as industrial loads. For reference current extraction, the novel Icosθ and proposed enhanced SRF SOGI-FLL (synchronous reference frame second-order generalized integrator frequency-locked loop) controller have been adopted in the H-bridge DSTATCOM. The Icosθ controller’s performance is dependent on the in-phase and quadrature-phase angle, which changes accordingly as load changes, whereas the proposed enhanced SRF SOGI-FLL controller works in synchronization with the grid with an inverter. The two control techniques were compared in terms of balancing, power factor improvement, DC-link voltage control, and harmonic minimization. The harmonics minimization of the proposed controller has been validated by IEEE 519 standards. The extracted reference currents are fed to the hysteresis current controller for the generation of pulses toward the inverter switches of DSTATCOM. The DSTATCOM system along with control algorithms have been tested on various loading conditions, i.e., voltage source- and current source-based non-linear loads, induction heating-based loads, and electric arc furnace. The complete DSTATCOM systems were implemented and executed in the MATLAB/Simulink platform and then power quality improvement features were investigated. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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19 pages, 16149 KiB

Open AccessArticle

Optimizing PV-Hosting Capacity with the Integrated Employment of Dynamic Line Rating and Voltage Regulation

by Ramitha Dissanayake, Akila Wijethunge, Janaka Wijayakulasooriya and Janaka Ekanayake

Energies 2022, 15(22), 8537; https://doi.org/10.3390/en15228537 - 15 Nov 2022

Cited by 1 | Viewed by 1423

Abstract

A record amount of renewable energy has been added to global electricity generation in recent years. Among the renewable energy sources, solar photovoltaic (PV) is the most popular energy source integrated into low voltage distribution networks. However, the voltage limits and current-carrying capacity of the conductors become a barrier to maximizing the PV-hosting capacity in low voltage distribution networks. This paper presents an optimization approach to maximize the PV-hosting capacity in order to fully utilize the existing low voltage distribution network assets. To achieve the maximum PV-hosting capacity of the network, a novel method based on the dynamic line rating of the low voltage distribution network, the coordinated operation of voltage control methods and the PV re-phasing technique was introduced and validated using a case study. The results show that the proposed methodology can enhance the PV-hosting capacity by 53.5% when compared to existing practices. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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30 pages, 16343 KiB

Open AccessArticle

Optimal Design of an Artificial Intelligence Controller for Solar-Battery Integrated UPQC in Three Phase Distribution Networks

by Koganti Srilakshmi, Canavoy Narahari Sujatha, Praveen Kumar Balachandran, Lucian Mihet-Popa and Naluguru Udaya Kumar

Sustainability 2022, 14(21), 13992; https://doi.org/10.3390/su142113992 - 27 Oct 2022

Cited by 17 | Viewed by 1940

Abstract

In order to minimize losses in the distribution network, integrating non-conventional energy sources such as wind, tidal, solar, and so on, into the grid has been proposed in many papers as a viable solution. Using electronic power equipment to control nonlinear loads impacts the quality of power. The unified power quality conditioner (UPQC) is a FACTS device with back-to-back converters that are coupled together with a DC-link capacitor. Conventional training algorithms used by ANNs, such as the Back Propagation and Levenberg–Marquardt algorithms, can become trapped in local optima, which motivates the use of ANNs trained by evolutionary algorithms. This work presents a hybrid controller, based on the soccer league algorithm, and trained by an artificial neural network controller (S-ANNC), for use in the shunt active power filter. This work also presents a fuzzy logic controller for use in the series active power filter of the UPQC that is associated with the solar photovoltaic system and battery storage system. The synchronization of phases is created using a self-tuning filter (STF), in association with the unit vector generation method (UVGM), for the superior performance of UPQC during unbalanced/distorted supply voltage conditions; therefore, the necessity of the phase-locked-loop, low-pass filters, and high-pass filters are totally eliminated. The STF is used for separating harmonic and fundamental components, in addition to generating the synchronization phases of series and shunt filters. The prime objective of the suggested S-ANNC is to minimize mean square error in order to achieve a fast action that will retain the DC-link voltage’s constant value during load/irradiation variations, suppress current harmonics and power–factor enhancement, mitigate sagging/swelling/disturbances in the supply voltage, and provide appropriate compensation for unbalanced supply voltages. The performance analysis of S-ANNC, using five test cases for several combinations of loads/supply voltages, demonstrates the supremacy of the suggested S-ANNC. Comparative analysis was carried out using the GA, PSO, and GWO training methods, in addition to other methods that exist in the literature. The S-ANNC showed an extra-ordinary performance in terms of diminishing total harmonic distortion (THD); thus PF was improved and voltage distortions were reduced. Full article

(This article belongs to the Topic Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Smart Cities smartcities	6.4	8.5	2018	20.2 Days	CHF 2000
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400
World Electric Vehicle Journal wevj	2.3	3.7	2007	14.1 Days	CHF 1400

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