Emerging Trends in Electric Vehicles, Smart Grids, and Smart Cities

1. Introduction

Recently, electric vehicles (EVs), smart grids (SGs), energy storage (ES), and smart cities have been gaining momentum worldwide, with advanced technologies being introduced to create sustainable and reliable power grids that optimize the utilization of SGs and EVs in smart cities. A smart distribution system is an advanced and technologically sophisticated power distribution network that incorporates various intelligent technologies to enhance efficiency, reliability, and sustainability [1]. This type of system is a subset of the broader concept of an SG, focusing specifically on the distribution stage of the electricity supply chain. The largest penetration of renewable energy sources (RESs) and EVs provides opportunities and significant challenges to the power industry. The variable output and stochastic behavior of RESs may impact real-time balancing challenges in the power system. There is a pressing need to increase the power reserves provided by conventional power stations. These modern hybrid systems would give more economical solutions with better availability [2,3]. However, when RESs are included to establish hybrid systems due to the uncertain nature of RERs, they effect the power quality of power electronic devices, which increases reliability and safety issues. The emissions from internal combustion engine (ICE) vehicles in the transportation sector create a considerable carbon footprint. However, EVs offer a promising solution to replace ICE vehicles. RERs and EVs are used to reduce carbon footprints. EVs are a clean mode of transportation and strong alternatives to conventional ICE vehicles. However, a proper charging infrastructure is required for the large-scale adoption of EVs. The interest in the large-scale integration of EVs, energy storage, and RESs is growing rapidly with the gradual exhaustion of fossil-based fuel sources.

The uncertainty and inherent variability in RERs have altered many aspects of the operation, control, and planning of power networks. High penetration of RERs and EVs may impact operation and planning tasks, which play a significant role in the gradual transition of a traditional power grid to a smart grid (SG). To develop smart cities, there is a need to overcome various challenges around the efficient utilization of energy, pollution, security, parking, traffic, and transportation [4,5]. This work deals with several topics including EV planning and operation in the modeling of SGs and smart cities, the modeling flexibility of distributed energy resources (DERs), the integration of SG and green energy, distributed generation (DG) and distributed storage, electricity market modeling and simulation for the integration of RESs, Internet of Things (IoT) in smart cities, energy management systems in smart distribution grids, the role of Artificial Intelligence (AI) in SG and smart cities, and SG technology and solutions for smart cities.

2. Editorial Content: Review of Emerging Trends for Sustainable Development

With the rapid developments in the sustainability in modern power grids, the integration of EVs and RESs into smart grids and smart cities has become a critical focus. These developments present a range of challenges, including the procurement of EV charging infrastructure, the integration and control of RESs, sustainable transportation, coordinated protection, and the stability and security of power systems for reliable and efficient operation. This editorial provides a comprehensive analysis of the design and operational challenges related to power electronic converter technologies for EV charging and discharging, controllers for EV chargers, the integration and control of EV charging in smart grids and smart cities, and the effects on grid quality, stability, security, and resiliency after the significant penetration of RESs and EVs.

2.1. Emerging Trends in Electric Vehicles

Developments in hardware, computing, control, and communication technologies emerged in the modeling of cyber-physical systems (CPSs). This created an evolution in the power grid industry, leading to the transformation of conventional power grids into smart grids. Ma et al. [6] presents the rapid growth of autonomous driving (AD) technology. This study investigated Hofstede’s cultural dimensions theory. It considered individual culture and human factors that have an impact on the consumers (or) users. This study collected more than 500 samples of data from Chinese consumers and divided them into three categories. The proposed work mainly focused on cultural differences between nations and countries. This research considered the impacts of individual cultural orientation on consumer requirements for innovative human–machine interaction (HMI) features.

Uy et al. [7] proposes the integration of EVs in developed countries to minimize carbon emissions. This study surveyed the Philippines and collected more than 311 customers’ opinions using conjoint analysis with an orthogonal approach. This conjoint analysis was utilized to obtain the combination of EV attributes like charging method, charging speed, regenerative brakes, and type of battery, etc. The results obtained from this study indicate that cost is the major concern and regenerative brakes are the minor concern in adopting EVs. This paper primarily aims to evaluate the information regarding the awareness of the performance and environmental impacts of EV technologies.

Che et al. [8] analyzed the optimization problem related to EV ordered charging planning (EVOCP). EVs are rapidly used in developed countries, not only to save energy, but also for environmental protection. To solve this problem, an improved dual-population genetic moth flame optimization (IDPGMFO) was proposed. To obtain the best solution for EVOCP, an IDPGMFO that integrated an opposition-based learning strategy, dual-population genetic mechanism, and adaptive nonlinear decreasing strategies of selection, crossover, and mutation probability was used. This proposed work collected data from economic and technological development zones in China. Using the collected data, the simulation results are revealed.

Balke et al. [9] introduces battery electric trucks that fully depend on public charging infrastructure. The main aim of this research was to decarbonize the industrial sector, including the transportation of goods. A promising solution for commercial vehicles is battery electric trucks. The work proposed in this paper presents a technique for fast-charging networks for electric trucks in Germany. This study analyses the resilience and performance of proposed charging networks and potential derivatives. The case studies were implemented on two existing networks and their associated derivatives in Germany. Proper charging infrastructure planning plays a significant role. This paper also investigates how outages in the network induce an adapted strategy.

Medina-Garcia et al. [10] presents a vision-based object detection and localization module to aid EV navigation. In recent years, worldwide companies, universities, and research laboratories have developed autonomous and driverless cars. Autonomous vehicles support various functions like self-driving, connecting to the internet, quick decision-making, sensing the environment, etc. To improve system intelligence, machine learning (ML) algorithms are used in advanced driver-assistance systems (ADAS) to provide useful and current information from the environment. This paper proposes various navigation strategies for static environments.

Hu et al. [11] presents the optimization of valley-filling charging for vehicle network systems using a multi-objective optimization. In recent years, EVs have been increasingly used due to environmental and economic issues. The high penetration of EVs may impact the grid, causing many problems. To overcome these problems, this research work proposes an efficient and economical valley-filling charging scheme for grid users and EVs in the vehicle network system. This work first used convex optimization theory to achieve the optimization effect of valley filling. Secondly, the proposed work analyzed the relationship between EV charging cost and battery life. The proposed work used multi-objective optimization (MOO) to obtain the best solutions. By using this MOO, the charging cost is reduced and also improves the battery life.

Eltamaly et al. [12] presented an optimal dispatch strategy for EVs in vehicle-to-grid (V2G) applications. The widespread use of EVs can result in various difficulties, such as the reliability and resilience of traditional power systems. To address these issues, RE-based distributed generators provide the best solution. This work proposes an innovative wear model that aims to assess the wear cost of EV batteries, resulting from their involvement in V2G activities. This study also presents a unique approach for efficient charging scheduling that aims to optimize the charging and discharging patterns of EVs by using a day-ahead pricing technique. To obtain the best solution, the proposed work used the Grey Wolf optimization algorithm.

Bai et al. [13] presents an adaptive optimization process for EV energy replenishment stations considering the degradation of energy storage batteries. Currently, emission-free transportation is considered a key strategy for achieving energy efficiency, savings, and improving energy utilization efficiency. This work considered an energy replenishment station (ERS) as the research object. The proposed work uses a deep reinforcement learning based method for optimizing the operation strategies of the battery swapping station (BSS) and the energy storage system (ESS) in the ERS. This work analyzes the mathematical representation of each device in the ERS. Secondly, the dimensional disaster problem is solved. Finally, the proposed method is verified using simulation calculations. This methodology considers three factors, i.e., the degradation of ESS, the availability of a battery swapping service, and the economic efficiency of the ERS.

Benalia et al. [14] presents an approach for wirelessly charging EVs by using inductive charging technology. In recent years, most developed countries have used EVs because of several advantages, such as enabling a clean environment, reduced cost, etc. Currently, most researchers are focused on wireless charging technology because it has several benefits, such as safety in harsh environments, complex design, and high investment cost. This paper analyzed three cases of magnetic coupling between two identical rectangular spiral coils with the same number of turns, depending on both the vertical and horizontal axes. In this work, the additional insertion of an aluminum plate into the coil is discussed. According to the results, aluminum worked effectively. Additionally, this paper studied the resonance phenomenon and the matrix of emitting/receiving coils to improve the efficiency of the charging system.

Nazari-Heris et al. [15] presents an updated review and outlook on EV aggregators in electric energy networks. The demand for EVs is growing exponentially due to environmental and economic issues. EVs are significantly integrated into the power and transportation sectors. EVs play a significant role in achieving sustainable transportation and a safe environment. This paper investigated EV aggregation along with its integration challenges and impacts on electricity markets, etc. The main aim of this work was to provide a comprehensive review of EV aggregation models in electrical energy systems. This paper mainly focuses on the challenging issues and primary considerations of EV aggregators reported in different research studies.

Mohanty et al. [16] presents a Fuzzy-based simultaneous optimal placement of EVCSs, distributed generators, and DSTATCOM in a distribution system. In recent years, EVs have been increasingly used due to environmental and economic issues. The high penetration of EVs may impact the performance of distribution systems. As the number of EVs is increasing exponentially, there is a pressing need to consider the optimal allocation of EVCSs in the planning problem. This paper proposes a fuzzy classification method for the optimal size and location of DGs, DSTATCOM, and EVCS. The proposed work was tested on a 69-bus radial distribution system using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were used in a load flow analysis to optimize EV battery charging loads. The fuzzy multi-objective function was used for the simultaneous placements of DG, EVCS, and DSTATCOM. The simulation results reveal that the multi-objective optimization provided better performance, reduced real power loss, and enhanced the voltage profile.

2.2. Emerging Trends in Smart Grids

Globally, there is increasing investment from governments and industries towards the development of smart grids and smart cities. Smart grid technology enables the integration of multiple RESs such as wind, solar PV, fuel cells, micro-turbines, energy storage systems (ESSs), and advanced communication systems. Zhang et al. [17] presents an efficient transformer-based approach for the precision detection of orthopedic healthcare devices. In recent years, orthopedic medical devices have played a significant role in the healthcare and control of different musculoskeletal disorders. Despite advancements in medical imaging technology, current detection models often fail to meet the distinct needs of orthopedic device detection. To bridge this gap, this work proposed orthoDETR, a transformer-based object detection model specifically optimized and designed for ortho-healthcare devices.

Chirumalla et al. [18] presents business-to-business (B2B) mobility with sharing autonomous electric vehicles (AEVs). The mitigation of climate change is one of the most significant challenges in the modern world, requiring entire industries, society, and systems to transform. To overcome this problem, the circular economy (CE) method has attracted increased attention among academia, policymakers, and practitioners as a guiding principle to keep materials, products, and components at the highest value and utility rate at all times. The main aim of this research is to find new and promising business models for sharing self-driving electric cars within a B2B context. This methodology presented five business model scenarios derived from the selected attributes in the morphological framework.

Hiep et al. [19] presents an evaluation of electric two-wheeler development in shaping a national e-mobility roadmap to advance sustainable transport in Vietnam. In recent years, due to environmental reasons, many developed countries have encouraged people to use carbon emission-free vehicles or EVs. This work conducted a survey in Vietnam on the charging options, legal frameworks, and market assessments used. This paper collected data in three selected cities on people who were moving from gasoline two-wheeler (G2W) vehicles to electric two-wheelers (E2Ws), and also collected data from non-E2W users. Due to various reasons, users do not switch to E2Ws, such as the expensive and complicated systems for charging and swapping batteries. This research also analyzes an overview of the opportunities and challenges facing decision-makers/policymakers and researchers around recommending policies for the development of E2Ws in Vietnam.

Ram et al. [20] presents an extensive review of synthetic fuels, emphasizing their production and classification processes. Adopting synthetic fuels has many issues, such as a lack of infrastructure, consumer awareness, high production costs, etc. This paper analyzes synthetic fuel production processes along with detailed equations and diagrams.

Key et al. [21] presents a rapid method for detecting current transformer (CT) saturation using stacked denoising autoencoders. Generally, CT saturation distorts waveforms due to incorrect initial results. This paper proposes a scheme that provides a wide range of saturation detection, and it incorporates a moving window technique and stacked denoising encoders. The proposed approach uses the Bayesian optimization technique to decrease the difficulty of determining neural network structures. The performance of the moving data algorithm was evaluated in the Republic of Korea on 345 kV and 154 kV overhead transmission lines. The performance of stacked denoising autoencoders was deeply evaluated on the simulated data from PSCAD/EMTDC, which covered many saturation variations like power system levels, fault inception angles, etc.

Dey et al. [22] presents a smart demand-side management (DSM) approach for a comprehensive techno-economic analysis of microgrid (MG) systems. Currently, RE technologies are growing economically and environmentally. Due to these technologies, the usage of economic load dispatch (ELD) has also increased. ELD means that different generating units in a distributed system do not equally share and supply the load. In microgrid structures, the load fluctuates from hour to hour. Based on this load demand curve, the power system utilities decide the rate of electric power at different times of the day. This process is referred to as the time-of-use (TOU) pricing of electricity. The hourly load demand can be divided into two types: the first one is elastic hourly load demand, and the second one is inelastic hourly load demand. The proposed work considered minimizing the cost as an objective function. This paper presents an intelligence technique based on DSM to reduce the total cost of using loads in the MG structure. This paper studied seven different cases, which include electricity market pricing strategies, DSM programs, diverse grid participation, etc. The main aim of this research was to minimize the total cost.

Lan et al. [23] analyzes the comparison between two types of switched reluctance machines (SRMs) and the corresponding SRM converters. Compared to all other electric machines, SRM has more advantages, like high reliability, a simple machine structure, and low manufacturing cost. This machine has many advantages, so most researchers have used it for EV applications. However, the disadvantage of SRM is high torque ripple at low speeds; to overcome this problem, the proposed paper presents a multi-stack configuration. It can reduce the SRM torque ripple, including three-stack SRMs. The experimental results of the boost converter using a conventional SRM showed that it generated a lower torque ripple within a specific range.

Dissanayake et al. [24] presents an approach for optimizing photovoltaic (PV) hosting capacity with the combined application of dynamic line rating and voltage regulation. Renewable energies are becoming increasingly popular due to the global electricity generation in recent years. Compared to other non-conventional energy sources, PV is the most widely used energy source in the low-voltage distribution networks. A new methodology was developed to investigate control techniques by utilizing an optimization approach. This approach was applied to a real LVDN and demonstrated that, by using the DLR as a constraint of the optimization problem instead of SLR, PVHC can be evaluated by 40.9% compared to the case of SLR with the OLTC voltage regulation.

Srilakshmi et al. [25] proposes the optimal design of an AI-based controller for a solar-battery-integrated UPQC in three-phase distribution networks. Recently, the adoption of non-conventional energy sources like solar, wind, tidal, etc., has increased exponentially due to the high penetration of RESs that may impact the distribution systems. The primary objective of this paper was to minimize the losses in the distribution network. This work proposed a hybrid controller based on the soccer league algorithm, and an artificial neural network controller was used for the shunt active power filter. This work also presents a fuzzy logic controller for use in a series of active power filters of the UPQC that is associated with the solar PV system and battery storage system.

Nandigam et al. [26] used a hybrid optimization algorithm which combines the gradient descent and adaptive sheep flock optimizations to handle various issues related to load balancing and energy dispatch in the microgrid, including solar PV, wind turbines, battery storage, fuel cells, microturbines, and diesel generators. Gomez-Redondo et al. [27] presents a comprehensive review on AC microgrids (ACMGs) considering five research questions. These are standards, grid connection schemes, control systems, and the evolution of ACMGs, along with outcomes from the review articles.

Hu et al. [28] presents a systematic literature review of uncertainties in modern hybrid power grids, including modeling, impact, and handling. This paper also describes the modeling of scenario-based scheduling and operations methodologies for smart grids, including energy storage and EVs. Liu et al. [29] reviews the pricing mechanism in a centralized power system, as well as microgrid-to-microgrid (M2M) and peer-to-peer (P2P) energy sharing and trading. This work covers various pricing mechanisms in hybrid modern power grids, considering renewable energy resources, electric vehicles, and storage batteries, as well as controllable load applications.

2.3. Emerging Trends in Smart Cities

Bose et al. [30] presents a quick and easy-to-use method to locate electric vehicle charging stations (EVCSs) in smart cities. The rapid development of EVs requires the optimal planning and allocation of EVCSs. Many metaheuristic and classical-based techniques provide appropriate solutions, but they are not easy to use and are not well-designed for large cities. The proposed work analyses more than 50 layouts of the most popular cities in the world. Based on this survey, the proposed paper designed a new charging station placement algorithm. The paper primarily aims to summarize the quality, intuitiveness, and computational efficiency without compromising quality.

Abdalla et al. [31] analyzes an energy efficiency optimization strategy in a smart home for EVCSs with/without a vehicle-to-home (V2H) and household energy storage system (HESS) to optimize household energy usage and lower electricity costs. The proposed methodology detects EV departure and arrival time, establishes the priority order between HESS and EV while charging and discharging, and ensures that the EV battery state of energy at the departure time is sufficient for its travel distance. This work presents four different scenarios to investigate the role of HESS and EV technology for reducing electricity bills and smoothing load curves in smart houses. This work also studied the impact of V2H and HESS on electricity cost reduction and load curve flattening.

Azab et al. [32] present ideal scenarios for EV charging infrastructure. EVs are a substitute for conventional ICE vehicles in developed countries. This work used an evolutionary search optimization algorithm to obtain the ideal number of EV charging stations to be installed in a specific province. The case study has been investigated in the Kingdom of Saudi Arabia. The proposed work considers two objective functions: cost and charging time. The main aim of this work was to reduce the cost and charging time simultaneously. This work used two well-known evolutionary search algorithms, that is, particle swarm optimization and the genetic algorithm, to solve the optimization problem. Both algorithms provide many optimal charging infrastructure scenarios. The analyzed computational approach gives several scenarios and options for the number of charges.

Chen et al. [33] investigated the spatial distribution pattern of related industries and their influencing factors using data related to new energy vehicle (NEV) industry-listed companies from 2008 to 2021. This work also conducted stepwise regression analysis and spatial statistical analysis. This paper surveyed China’s NEV industry and used data related to NEV. Initially, the proposed work collected data of 459 NEV-related listed companies in four major sectors of China. This work also refers to numerous existing studies on the influencing factors of spatial distribution in various areas such as sci-tech enterprises, vehicle production, corporate headquarters, etc.

Muhammad et al. [34] analyzed the cybersecurity and privacy threats facing EVs and their effects on human and environmental conservation. EVs are capable of decreasing air pollution, reducing dependence on fossil fuels, and helping to secure the transportation industry. In recent years, most developed countries have focused on intelligent transportation systems (ITS). EVs are equipped with sensors for the advancement of society and to enhance human sustainability. Advanced digital technology brings betterment to society, but there are many threats to privacy and security. EVs are equipped with sensors, but these have been misused for financial fraud, energy theft, data compromise, etc. To overcome these challenges, the proposed work provides a first systematic analysis of EV sustainability. The main aim of this work was to increase EV safety and privacy and to develop the charging infrastructure, improve the energy efficiency of batteries, and reduce EV costs.

Esenboğa et al. [35] investigated smart city power distribution technologies in advanced countries. Currently, RE usage is growing drastically. Renewable energies and distributed generation cause power quality problems in smart grids, especially in sudden voltage sags and swells, fault currents, and voltage harmonic distortions. To overcome these problems, the proposed work presents smart transformers. The paper proposes an optimal selection of various three-stage (AC-DC-DC-AC) smart transformer models and a power control strategy for a solar PV power plant integrated with SGs. The isolation stage is a major component of smart transformers (STs), as it increases the system’s efficiency and controllability. The DC-DC converter topologies utilized in various ST applications are investigated.

Ali et al. [36] explored the factors influencing commuters’ satisfaction with ride-hailing services in developing countries. This paper surveyed the city of Lahore. Ride-hailing services play a significant role in advanced countries. This study investigated more than 531 passengers’ attitudes towards ride-hailing services and collected data through face-to-face interactions. According to the explanatory factor analysis and structural equation modeling, these ride-hailing services provide comfort, convenience, social protection, and safety. In this paper, the commuter satisfaction index was calculated. The results obtained from commuters during travel revealed that the commuters were more satisfied with the driver’s skills and experience, as well as the condition of the vehicle. Furthermore, this study evaluated individuals who did not use ride-hailing services.

Islavatu et al. [37] presents a power quality (PQ) analysis using H-Bridge DSTATCOM control using the Icosθ and ESRF SOGI-FLL methods for different industrial loads. DSTATCOM was used to reduce the power quality issues of both commercial and industrial applications. Traditionally, the Icosθ technique is used to control H-Bridge DSTATCOM, but in this paper, the ESRF SOGI-FLL method was used to control H-Bridge DSTATCOM. This is an efficient control technique that is highly efficient in grid synchronization and reference current generation for dynamic systems. The proposed paper compares two control techniques based on their effectiveness in improving the power factor, harmonic minimization, and DC-link voltage control. The proposed work considered harmonic minimization as a controller by IEEE 519 standards [38]. The DSTATCOM system, along with control algorithms, was tested on different load conditions such as electric arc furnaces, induction-heating-based loads, etc. The simulation results were obtained and analyzed using the MATLAB/SIMULINK 2018a platform. The proposed work also investigated the PQ improvement features.

Park et al. [39] describes and shares the validated results of KEPCO’s project consortium to create a transmission system operator (DSO)–distribution system operator (DSO)–distributed energy resource aggregator (DERA) interaction system.

3. Closing Remarks and Future Challenges

This editorial focuses on the adoption of renewable energy sources (RESs) and electric vehicles (EVs) into existing grid infrastructures, addressing both technical and operational challenges. We highlight the importance of renewable energy (RE) integration, emphasizing solar PV, wind, and other distributed energy resources (DERs) and EVs as key components in modern hybrid power systems. These components offer substantial benefits, including diminished carbon emissions and a shift toward cleaner energy sources. However, the inherent unpredictability and fluctuations associated with these components pose significant challenges. The integration of renewable DERs into modern power distribution networks is a critical step toward building a more sustainable, resilient, and environmentally friendly energy infrastructure.