Emerging Trends in Electric Vehicles, Smart Grids and Smart Cities

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

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

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 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

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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 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

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

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