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Alternative energy sources have significantly impacted the global electrical sector by providing continuous power to consumers. The deployment of renewable energy sources in order to serve the charging requirements of plug-in electric vehicles (PEV) has become a crucial area of research in emerging nations. This research work explores the techno-economic and environmental viability of on-grid charging of PEVs integrated with renewable energy sources in the Surat region of India. The system is designed to facilitate power exchange between the grid network and various energy system components. The chosen location has contrasting wind and solar potential, ensuring diverse renewable energy prospects. PEV charging hours vary depending on the location. A novel metaheuristic-based optimization algorithm, the Pufferfish Optimization Algorithm (POA), was employed to optimize system component sizing by minimizing the system objectives including Cost of Energy (COE) and the total net present cost (TNPC), ensuring a lack of power supply probability (LPSP) within a permissible range. Our findings revealed that the optimal PEV charging station configuration is a grid-tied system combining solar photovoltaic (SPV) panels and wind turbines (WT). This setup achieves a COE of USD 0.022/kWh, a TNPC of USD 222,762.80, and a life cycle emission of 16,683.74 kg CO2-equivalent per year. The system also reached a 99.5% renewable energy penetration rate, with 3902 kWh/year of electricity purchased from the grid and 741,494 kWh/year of energy sold back to the grid. This approach could reduce reliance on overburdened grids, particularly in developing nations. [ABSTRACT FROM AUTHOR]

The location of Electric Vehicle Charging Stations (EVCSs) is gaining significant importance as part of the conversion to a full-electric vehicle fleet. Positive or negative impacts can be generated mainly based on the quality of service offered to customers and operational efficiency, also potentially involving the electrical grid to which the EVCSs are connected. The EVCS location problem requires an in-depth and comprehensive analysis of geographical, market, urban planning, and operational aspects that can lead to several potential alternatives to be evaluated with respect to a defined number of features. This paper discusses the possible use of a multi-criteria decision-making approach, considering the differences between multi-objective decision making (MODM) and multi-attribute decision-making (MADM), to address the EVCS location problem. The conceptual evaluation leads to the conclusion that the MADM approach is more suitable than MODM for the specific problem. The identification of suitable attributes and related features is then carried out based on a systematic literature review. For each attribute, the relative importance of the features is obtained by considering the occurrence and the dedicated weights. The results provide the identification of the most used attributes and the categorization of the selected features to shape the proposed MADM framework for the location of the electric vehicle charging infrastructure. [ABSTRACT FROM AUTHOR]

Interest in Unmanned Aerial Vehicles (UAVs) has been increasingly growing in recent years, especially for purposes other than those for which they were initially used (civil and military purposes). Currently, in fact, they are used for advanced monitoring and control purposes, for 3D reconstructions of the territory and cultural heritage, and for freight transport. The problem in using these systems consists of the limited flight autonomy. In fact, commercially used drones, today, are sold with a set of batteries of limited duration which do not allow flights over large areas and, therefore, detailed surveys. The present work seeks to overcome these limitations by proposing an intelligent automatic charging system (Intelligent Charging Network) created using PC Engines Alix and an experimental drone prototype using a Raspberry Pi 3 and a Navio 2 module. At the same time, an efficient Intelligent Charging Network–drone communication system and a data transmission system are proposed, which allow images acquired by the drone to be transferred directly to the server used for data storage for their subsequent processing as well as the transmission of the flight plan from the QGroundControl application to the drone. The proposed system represents technological innovation in the field of drones with potential future developments linked to the implementation of sustainable drones. [ABSTRACT FROM AUTHOR]

With the government's strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user's time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs. [ABSTRACT FROM AUTHOR]

Summary: With the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs. [ABSTRACT FROM AUTHOR]

How to effectively utilize industrial policies to enable the coordinated development of new energy vehicles and charging stations is a policy challenge faced by the Chinese government. This study focused on the impact of heterogeneous industrial policies on the development of charging infrastructure for new energy vehicles in China. Based on panel data from 31 provinces in China from 2010 to 2022, this study used a staggered difference-in-differences model to examine the impact and mechanisms of policies such as charging station construction subsidies, preferential tariff policies, land supply, and layout planning on charging station ownership and discussed the effect differences between subsidies by power and those by total investment. The results found that: ① Charging station ownership was significantly higher in areas where industrial incentive policies were implemented than in non-implemented areas, with a marginal effect of 33.4%. This result remained robust under multiple scenarios, such as changing the window period, using a change-in-change model, and relaxing the parallel trend. ② Among the two types of subsidies, charging power subsidies had a more pronounced effect compared with subsidies based on the amount of investment. ③ Among the four types of industrial policies, preferential tariff policies and construction subsidies had more pronounced effects. ④ Heterogeneity analysis showed that the effect of the charging station construction subsidy policy was more pronounced in areas with lower population density and lower land transfer prices of the demonstration and promotion areas; in addition, the effects of the subsidy policy were better in the short term than in the long term and were more significant in areas with lower population density and lower land transfer prices. ⑤ The charging station construction subsidies indirectly affected the number of charging stations by increasing the amount of infrastructure investment, the area of land used for facility construction, and charging technology patents. Based on these empirical results, this study recommends that we should implement a differentiated charging station construction subsidy policy, optimize the current charging power subsidy policy, improve the current preferential tariff policy, improve the accuracy of land supply and layout planning, formulate a differentiated policy according to the characteristics of different regions, encourage technological innovation, and pay attention to the feedback of policy implementation. [ABSTRACT FROM AUTHOR]

Problem. The increasing popularity of electric cars worldwide is also seen in Ukraine, leading to a growing need for more charging stations. Studies show that 80% of electric car charging happens at home. This home charging usually occurs either through the standard AC power grid or through dedicated AC charging stations. This raises concerns about the safety of these charging stations and their potential interference with other electrical and electronic devices nearby. Goal. The goal of this work is to determine the main electromagnetic parameters in the connection cable during the operation of an AC electric vehicle charging station. Methodology. To achieve this goal, it is necessary to study the electromagnetic parameters of the charging station connection cable when AC flows through it and to identify the electromagnetic parameters of the interference that occurs during the operation of the AC charging station. Classical electrophysical methods for calculating electric and magnetic fields and methods for determining the parameters of quadrupoles from the theoretical foundations of electrical engineering are used. Results. The main electromagnetic parameters in the connection cable during the operation of the AC electric vehicle charging station have been identified. Formulas for calculating the current strength and magnetic field strength have been obtained. Originality. New formulas for calculating the electromotive force of interference generated during the operation of the electric vehicle charging station, when AC flows through the charging cable, have been developed. Parameters of capacitive and magnetic coupling between two conductors in a common bundle have been identified. Formulas for determining the current induced by these parasitic connections have been obtained. Practical value. Accurately determining and calculating these parameters allows for the design of a charging station that operates reliably over a long period without causing interference with nearby electrical and electronic systems or devices.

Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

The rapid development of electric vehicles has led to a yearly increase in charging load levels， characterized by strong randomness and unpredictability. Therefore， research on load forecasting for charging stations holds significant importance. Firstly， to address the inaccuracy of single-factor forecasting models that only consider load fluctuation trends， this paper analyzes the impact of multiple factors on the accuracy of charging station load forecasting. A load forecasting model is established that takes into account multiple influencing factors and is based on CNN-LSTM （convolutional neural network， long short-term memory）. Subsequently， given the impact of strong randomness of charging load on the model， an error correction method based on the random forest （RF） algorithm is proposed. Finally， the paper conducts simulation verification using real charging station load data as a case study. The research results indicate that the load prediction of the CNN-LSTM model， corrected by the RF algorithm， can accurately cover real values. Compared to the LSTM single model and the non-corrected CNN-LSTM model， it exhibits higher forecasting accuracy and practical value.

Abstract Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

This paper investigates the competition between two charge point operators at the same site for future battery electric long-haul trucks. The charge point operators are located along one of the busiest highways in Sweden. The investigation is carried out using an agent-based model where trucks select charge point operators based on charging prices and the length of any queues, while charge point operators adjust their prices and number of chargers to improve their profitability. The study aims to predict conditions for trucks and charge point operators in a future public fast-charging market. Our findings indicate the potential for a well-functioning future public fast-charging market with small queuing problems, high utilisation, and reasonable prices for public fast charging. Assuming a price for electricity of EUR 0.08/kWh and a minimum profit margin of EUR 0.001/kWh for charge point operators, the findings indicate that the price level outside rush hours will be low, approximately EUR 0.1/kWh. The prices during rush hours will likely be much higher, but it is harder to predict the value due to uncertainties of how charge point operators will act in the future market. Still, from the model result, the price during rush hours is suggested to be just above EUR 0.5/kWh, with an average charging price of around EUR 0.15/kWh. It also seems likely that it is profitable for charge point operators to build enough chargers so that charging queues are short. [ABSTRACT FROM AUTHOR]

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid. [ABSTRACT FROM AUTHOR]

An optimization technique for the control of a photovoltaic (PV)-fed electric vehicle (EV) solar charging station with a high gain of step-up dc-to-dc converter. An optimization approach is the Namib beetle optimization (NBOA) approach. This approach is used to control the EV solar charging station. Also, the principles of a switched capacitor and a coupled inductor are integrated into the interleaved structure of the NBOA converter to produce low-current, high-efficiency, and high-voltage gain. However, the major contribution is to minimize the total harmonic distortion (THD) and to control the EV solar Charging Station. The bi-directional DC-to-DC converter in an energy-storage-system has the advantages of high efficiency and fast response speed. By then, the NBOA technique is done in MATLAB software, and the performance is evaluated with the existing techniques. The NBOA system has low THD and high efficiency, which is compared with the existing ant-lion optimizer, wild horse optimizer, and salp-swarm algorithm, methods. From the analysis, the NBOA method provides a better outcome than the existing one. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations' energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station's performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station's performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system's simulation results are provided and scrutinized to confirm the concept's suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations. [ABSTRACT FROM AUTHOR]

This study proposes a deep-reinforcement-learning (DRL)-based vehicle-to-grid (V2G) operation strategy that focuses on the dynamic integration of charging station (CS) status to refine solar power generation (SPG) forecasts. To address the variability in solar energy and CS status, this study proposes a novel approach by formulating the V2G operation as a Markov decision process and leveraging DRL to adaptively manage SPG forecast errors. Utilizing real-world data from the Korea Southern Power Corporation, the effectiveness of this strategy in enhancing SPG forecasts is proven using the PyTorch framework. The results demonstrate a significant reduction in the mean squared error by 40% to 56% compared to scenarios without V2G. Our investigation into the effects of blocking probability thresholds and discount factors revealed insights into the optimal V2G system performance, suggesting a balance between immediate operational needs and long-term strategic objectives. The findings highlight the possibility of using DRL-based strategies to achieve more reliable and efficient renewable energy integration in power grids, marking a significant step forward in smart grid optimization. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks. [ABSTRACT FROM AUTHOR]

Summary: Energy internet permits the power to stream lithely for broadcast and it aids in transporting energy to each user using electric vehicles (EVs). The energy that EVs utilize is generated using certain sources. Still, considering poor weather, the power station averts the production of energy and needs to acquire power transmitted to another location. One resolution is to set stations that aid to save surfeit energies and minimize energy loss during transportation. This paper devises a new model for choosing charging stations to charge EVs. Energy is transferred from the energy source to the charge station via bus station through optimal path using Battle Royale Jaya Optimization (BRJO) routing with fitness attributes like distance to select the shortest path and energy. The prediction of energy is done with a deep recurrent neural network (DRNN) and the charge is distributed optimally to EVs by employing a proposed Competitive Swarm‐Battle Royale Jaya Optimization (CSBRJO) by utilizing multi‐parameters, like priority, distance, predicted energy, waiting time, and EV count needed for charging. The CSBRJO outperformed with the smallest distance of 6.935 km, energy consumption of 1.676 J, path length of 5.05 m, and waiting time of 4.645 sec. [ABSTRACT FROM AUTHOR]

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an "iterative route cover optimization method" to suggest charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PV hosting capacity of a practical three-phase unbalanced distribution network is expected to increase, thus increasing safe PV penetration and speeding up the decarbonization of power systems. However, power unbalances and bus voltage violations are two major obstacles limiting further increases in the PV hosting capacity. With the popularization of electric vehicles (EVs), EV charging stations (EVCSs) as controllable loads can effectively provide grid ancillary services, which helps enhance the PV hosting capacity. In this regard, this paper proposes a hierarchical control method for EVCSs in an unbalanced network, consisting of central day-ahead scheduling and local real-time dynamic control. It is novel that the local control capability of EVCSs is reserved in the central scheduling. A three-phase day-ahead EV charging scheduling optimization model is developed to reduce the power unbalance and the bus voltage violation, and in turn, improve the PV hosting capacity. Moreover, active power transfer and voltage droop control are developed as two ancillary service functions for local real-time control responding to dynamic network operating conditions. Numerical simulations verify the effectiveness of the proposed method in enhancing PV hosting capacity and providing grid ancillary services.

Reinforcement learning algorithms are increasingly utilized across diverse domains within power systems. One notable challenge in training and deploying these algorithms is the acquisition of large, realistic datasets. It is imperative that these algorithms are trained on extensive, realistic datasets over numerous iterations to ensure optimal performance in real-world scenarios. In pursuit of this goal, we curated a comprehensive dataset capturing electric vehicle (EV) charging details over a span of 29,600 days within a designated parking facility. This dataset encompasses necessary information such as connection times, charging durations, and energy consumption of individual EVs. The methodology involved employing conditional tabular generative adversarial networks (CTGAN) to craft a pool of synthetic dataset from a smaller initial dataset collected from an EV charging facility located on the Caltech campus. Subsequently, multiple post-processing techniques were implemented to extract data from this pool, ensuring compliance with the charging station's capacity constraint while maintaining a realistic daily EV demand profile derived from historical data. Using kernel density estimation (KDE), the distributional characteristics of the historical data, especially concerning the timing of EV connections, were faithfully replicated. The developed dataset is specifically useful in training offline reinforcement learning algorithms.

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Solar electric vehicle charging can substantially reduce carbon emissions compared to conventional utility grid-based electric vehicle charging. In the future, the need for the use of electric vehicle charging will continue to increase so that an energy charging management system that is simple and has ubiquitous components is needed. This paper presented a simple charging management system. The current solution is full of complexity with various algorithms, deep learning and machine learning solutions, and artificial intelligence, which can lead to errors for which no known solution exists. The main novelty in the proposed strategy is to compare the components used to get a simple but reliable system. The component that will compare is Raspberry Pi and ESP32. The ESP32 can offset the advantages of the Raspberry Pi with the help of Blynk, so for a simple and cheaper but reliable system, the ESP32 system is better for an energy charging management system.

Abstract The expansion of the DC fast‐charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast‐charging stations. The review is closely aligned with current state‐of‐the‐art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

This article studies a game theory-based pricing competition among multiple non-cooperative electric vehicle charging stations (EVCS) that seek to maximize expected profit. In contrast, electric vehicle (EV) drivers aim to minimize charging costs. In this regard, we introduce a modified game setup that alleviates the limitations of a conventional Hotelling’s game (CHG) application to achieve the price equilibrium among multiple non-cooperative players in a discrete dynamic market. In a CHG, it is assumed that each player has a positive market share. However, a CHG provides positive utilities to all players at Nash equilibrium (NE) only if they choose prices that satisfy the market sharing condition (MSC). The NE price depends on the players’ locations. Therefore, positive utilities can be achieved only if the players’ locations are confined such that the resultant NE price satisfies the MSC. The proposed game is called the St-Hot (Stackelberg-Hotelling), which can be applied to achieve the price equilibrium for those locations of players where the CHG fails to satisfy the MSC. The proposed game is applied in an EV charging environment, wherein multiple competitive EVCSs are placed at strategic locations in a linear space. The equilibrium condition is analyzed using closed-form solutions, and the results are compared with the existing CHG model. The analysis reveals that every EVCS remains profitable in a competitive market, irrespective of their location. This demonstrates that the proposed game model ensures profitability for each EVCS, showcasing its superiority over CHG.

Abstract The growing interest in electric vehicles (EVs) for transportation has led to increased production and government support through legislation since they offer environmental benefits such as reduced air pollution and carbon emissions compared to conventional combustion engine vehicles. This shift toward EV technology aligns with the goal of preserving the natural environment. To fully utilize EVs, effective management of the power grid is crucial, particularly in radial distribution network systems (RDNS) as they pose stress and deviation of power system parameters from their normal. This study proposes a novel strategy for maximizing EV utilization through EV charging stations (EVCSs) in an RDNS by considering factors such as load voltage deviation, line losses, and the presence of distributed solar photovoltaic systems at load centers. The research begins by segmenting the RDNS into zones, followed by the application of an artificial intelligence-based hybrid genetic algorithm (GA) and particle swarm optimization (PSO) approach known as hybrid GA–PSO. This approach identifies optimal locations for EVCSs integrated with photovoltaics within the network. Subsequently, the employment of individual GA and PSO algorithms to optimize EVCS placement focuses on minimizing power loss and enhancing voltage. The effectiveness of the hybrid GA–PSO algorithm is compared to that of separate GA and PSO methods. Extensive simulations using the IEEE 33-node test feeders validate the proposed techniques, demonstrating the usefulness of the hybrid GA–PSO algorithm in identifying optimal EVCS placement within each zone. The results also highlight the advantages and novelty of hybrid GA–PSO in achieving optimal EVCS placement with stochastically sized and distributed photovoltaic in an RDNS.

Rapid growth in the electrification of bus fleets, driven by substantial environmental benefits, is facing challenges such as range anxiety, prolonged charging durations, and reduced flexibility compared to combustion engine buses. This study first conducts a comprehensive bibliometric analysis of diverse publications to identify key research trends in electric buses (E-buses). It then offers a thorough comparison of charging technologies, encompassing topologies, power flow capabilities, costs, grid impacts, and efficiency, along with an examination of existing standards, norms, and challenges. With a classification of nearly 150 references, the study aims to illuminate the strengths and weaknesses of each charging technology, providing a solid background for selecting optimal topologies and strategies for specific applications. Emphasizing the importance of a nuanced trade-off between the quantity and type of chargers and E-bus battery capacity in each scenario, the research goes beyond technical considerations to explore potential future trends in the field. The information gathered in this review is a helpful guide for policymakers, industry experts, and researchers dealing with the complexities of E-bus charging infrastructure.

Electric vehicles (EVs) are a promising solution to urban environmental challenges, but their unplanned growth could strain both the road and electric networks. To address this issue, this paper introduces the Combined Model of Road Transport and Electric Distribution Network (CoRTED), a framework for strategically deploying EV charging stations considering the dynamics of both networks. The CoRTED model minimizes EV user travel costs, power loss, and voltage deviation. The model employs a bi-level optimization approach with PSO-DS algorithm for station deployment and combined convex optimization (CCO) for traffic network equilibrium. Grid economic operation is formulated as AC OPF and solved using a primal-dual interior point method. Validation is performed using a modified Nguyen-Dupius RTN and IEEE 33 bus EDN, demonstrating CoRTED’s effectiveness in integrated network planning for EV infrastructure. Due to the increase in power demand by EVs, the loss and voltage deviation are increased to 4.679 MW and 24.23%, respectively. The proposed model reduces the losses and voltage deviation by 4.487 MW and 1.24%. Also, the total travel cost on the traffic network is reduced by 1.9% as the charging costs at each charging station are reduced.