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In order to achieve low carbon emissions in the power grid, the impact of new energy grid connections on the power grid should be reduced, as well as the peak-to-valley load difference caused by large-scale electric vehicle grid connections. This paper proposes a two-tier, low-carbon optimal dispatch master–slave game model involving virtual power plant operators as well as electric vehicle operators. Firstly, the carbon flow is tracked based on the proportional sharing principle, and the carbon emission factor during the charging and discharging process of electric vehicles is calculated. Secondly, the node carbon potential and time-sharing tariff are used to guide and change the charging behaviour of electric vehicles and to construct a master–slave game model for low-carbon optimal scheduling with the participation of multiple subjects, with economic scheduling at the upper level of the model and demand response scheduling at the lower level. Finally, the IEEE30 node system is used as an example to verify that the method adopted in this paper can effectively reduce the peak-to-valley difference of loads, reduce the carbon emissions of the grid, and reduce the cost of each participating entity. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) with renewable energy technologies is revolutionizing modern power systems by enhancing efficiency, reliability, and sustainability. This paper examines the role of the IoT in optimizing the integration and management of renewable energy sources, such as solar and wind power, into the electrical grid. The IoT enables real-time monitoring, data analysis, and automation, facilitating advanced load management, demand response, and energy storage solutions. Key advancements in IoT technologies, including smart grids and energy management systems, are discussed, highlighting their impact on improving grid stability and promoting the use of renewable energy. The paper also finds some challenges such as data security, privacy, and the need for standardized communication protocols. Furthermore, it finds how the IoT optimizes electric vehicle performance through advanced battery management, real-time energy consumption monitoring, and improved interaction with the electrical grid. Future research directions emphasize the potential of the IoT to further enhance renewable energy integration through artificial intelligence and machine learning, driving the transition towards a more sustainable and resilient energy future. [ABSTRACT FROM AUTHOR]

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid's reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed "vehicle-for-grid". Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85 % , compared to the MPC algorithm's THD of about 2.85 % . This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0 KVAR in the vehicle-for-grid mode. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

Power grids are progressing, and the possibility of incorporating DC grids toward hybrid AC/DC grids is gaining increasing relevance, as several technologies available nowadays are operating natively in DC. This paper proposes a topology of a full-controlled bidirectional dual-stage interleaved converter for interfacing hybrid AC/DC grids. The topology is based on a dual-stage architecture, constituted by an AC/DC converter and by a DC/DC converter, both based on interleaved power converters. On the AC side, which is connected to the main AC power grid, the proposed dual-stage architecture operates with sinusoidal current in phase or phase opposition with the voltage, meaning a bidirectional operation. In addition, it has the possibility of interfacing with other AC loads, such as domestic electrical appliances, or with an AC microgrid. The DC link, formed by the AC/DC power stage, is interfaced with a DC power grid, which provides numerous advantages, e.g., for interfacing battery electric vehicles directly charged in DC, as well as other DC loads, such as renewable energy sources. The DC/DC power stage is considered for interfacing with an energy storage system, which is capable of bidirectional power exchange with the DC grid or with the AC grid through the AC/DC power stage. A complete laboratory prototype was designed and developed, with the unified control algorithms implemented on a digital signal processor. The experimental results validated the operation of the full-controlled bidirectional dual-stage interleaved converter based on the specifications for the hybrid AC/DC grid, such as bidirectional operation, synchronization with the AC power grid, predictive current control, interleaved operation on both AC/DC and DC/DC power stages, DC-link voltage control for the DC grid, as well as the operation with different power levels. [ABSTRACT FROM AUTHOR]

This paper's research question is to evaluate the potential impact of large numbers of battery electric vehicles (BEVs) on the future electric grid, and whether the flexibility of BEV charging can induce enough system benefits to remunerate BEV users for the change in their recharging pattern. The considered scenario refers to the Italian situation and what might occur through the year 2040, where a share of BEV stock of 40% can be foreseen, as well as significant increases in PV and wind generation. Although this study is focused on Italy, its results are applicable, with minor differences, to several EU countries. This paper first shows that the future impact of increasing penetration of BEVs appears to be compatible with the expected growth of generation from renewable energy sources (RES) and the corresponding reduction in fossil fuel-based generation. It also gives an estimate of the CO2 emission reduction resulting from these changes, considering an unmanaged BEV charge profile and two different managed profiles that shift the car's charging period to hours of the day when they have no negative impact on the grid and maximize the utilization of RES. Finally, it shows an evaluation of the economic benefits of displacing private car charging ranging from 4 to 10 cEUR/kWh, which could be used as tariff incentives to stimulate this displacing in recharging time. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid. [ABSTRACT FROM AUTHOR]

Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier's profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption. [ABSTRACT FROM AUTHOR]

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia's grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia. [ABSTRACT FROM AUTHOR]

With the update of the power transaction mode and the access of increasing electric vehicles with high randomness to the power grid, the existing carbon emission flow calculation method cannot consider the influence of carbon emission due to the fluctuation in electric vehicle charging and various transaction modes. Given the above shortcomings, this paper proposes an improved carbon emission flow model considering electric vehicle charging fluctuations and hybrid power transactions. The model first considers different transaction modes, allocates the network loss to both sides of power generation, and forms a lossless network, realizing the decoupling of the bilateral transaction mode, pool transaction mode, and network loss and then calculating the day-ahead network's carbon emission. Then, the changes in different transaction modes caused by the fluctuation in electric vehicle charging under different transaction modes are analyzed and the nodes of 'spontaneous change' are found to form a day-ahead intra-day deviation network and calculate its carbon emission flow. Finally, the calculation results are combined to obtain an improved power system carbon emission flow considering electric vehicle charging fluctuations and transaction modes. In this paper, the 33-node system is used for verification. [ABSTRACT FROM AUTHOR]

There is a global trend to reduce emissions from cars through the adoption of other alternatives, such as electric vehicles (EVs). The increasing popularity of EVs has led to a growing demand for electric vehicle chargers. EV chargers are essential for charging the batteries of EVs. Since the EV charger stays connected to the grid for long periods of time to charge the EV battery, it must be able to handle disturbances in the power grid. The goal of this paper is to present an overview of the impact of grid events on EV battery chargers. As well as the impact of grid unbalances on EV chargers, this paper also provides an overview of the impact of grid faults on other, similar power electronics interfaced resources such as PV and energy storage systems. [ABSTRACT FROM AUTHOR]

Electric vehicles are increasingly appearing on Polish roads due to a number of technical, legal and marketing conditions. However, electromobility is developing primarily in urban areas, mainly due to the unevenly developed infrastructure for charging vehicle batteries and the power grid. Therefore, solutions should be created that use the existing power infrastructure, including the use of railway power infrastructure (RPI). The railway power network covers a significant part of the country, including forest areas, and, above all, it very often intersects with road infrastructure or runs along roads. This paper raises issues related to the possibility of using RPI to charge the batteries of electric vehicles. After characterizing the technical, operational and legal requirements related to these technical systems, a concept of an electric vehicle charging system using RPI was developed, along with a demonstration of the possibility of its implementation, which was simulated using mathematical models developed by the authors. [ABSTRACT FROM AUTHOR]

The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method's effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The expected increase in electric vehicles necessitates an expansion in charging stations. However, this increase could introduce issues to the power grid, such as the deterioration of voltage stability and an increase in microgrid loading. To address these issues, innovative solutions are imperative. One potential solution is the implementation of charging control mechanisms. This paper analyzes the impact of a residential charging station on a low voltage microgrid from the power quality point of view using a one-year operation simulation. Thirty-seven charging station scenarios with different locations within the network were examined, including instances with no charging control and various combinations involving charging control, photovoltaic power plant, and battery energy storage system. The simulation results of the charging station without charging control show a decrease in some power quality indicators, such as exceeding voltage change thresholds after charging station connection and increasing power losses. On the contrary, the scenarios implementing charging control and local resources effectively mitigate the adverse effects of charging stations on the microgrid. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners' disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs' uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations. [ABSTRACT FROM AUTHOR]

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid's voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid. [ABSTRACT FROM AUTHOR]

The electrification of the transportation sector contributes to a cleaner environment in non-interconnected island (NII) systems or standalone islands. Moreover, e-mobility can significantly contribute to achieving very high renewable energy source (RES) penetration levels in islands, allowing a reduction both in the emissions due to the conventional generation and the system's cost. Ιncreased RES penetration, however, can pose technical challenges for an island's system. In order to overcome these challenges, new technologies like grid-forming converters are important. Moreover, the provision of new ancillary services in relation to battery storage systems might be considered, while novel control and protection schemes are needed to ensure secure operation. E-mobility can also contribute to solving technical problems that arise from very high RES penetration by providing frequency containment reserves or reactive power compensation. Since EV charging demand introduces modifications in the system's load curve, e-mobility may affect the power grid for long-term planning and short-term operation, i.e., line loading and voltages. The application of specifically developed smart charging methodologies can mitigate the relevant grid impact, while effective exploitation of EV–RES synergies can achieve higher RES penetration levels. This paper examines how e-mobility can contribute to increasing RES penetration in islands while considering the technical issues caused. In particular, this paper takes into account the distinct characteristics of NIIs towards the identification of solutions that will achieve very high RES penetration while also addressing the relevant technical challenges (voltage control, frequency control, short circuit protection, etc.). The effect of e-mobility in the power grid of NII systems is evaluated, while smart charging methodologies to mitigate the relevant impact and further increase RES penetration are identified. [ABSTRACT FROM AUTHOR]

As the world is transitioning to electric vehicles (EVs), the existing power grids are facing several challenges. In particular, the additional charging power demand may repeatedly overload the traditionally-sized distribution transformers and adversely impact their operational life. To address this challenge, this paper proposes an EV-based reactive power compensation strategy for transformer overloading mitigation. Specifically, a low-bandwidth centralized recursive controller is proposed to determine a set point for the EV's onboard charger's reactive power. Importantly, the proposed strategy is practically implementable in existing distribution grids as it does not rely on smart grid infrastructure and is stable under potential communication delays and partial failures. This paper discusses the controller's structure, design, and stability in detail. The proposed solution is tested with a realistic secondary distribution system considering four different EV charging scenarios with both Level 1 and Level 2 residential EV charging. Specifically, IEEE Standard C57.91-2011 is used to quantify the impact of EV charging on the transformer's life. It is shown that with the proposed method, transformer overloading is significantly reduced, and the transformer's life improves by an average of 47% over a year in all four scenarios. [ABSTRACT FROM AUTHOR]

Load forecasting is an important component of power system and resource planning for electrical grids. The adoption of electric vehicles (EVs), behind-the-meter (BTM) solar, and heat pumps will significantly change the amount and variability of loads. Electrification adoption and load forecasting in arctic regions and Alaska is limited. This paper provides the first load and electrification adoption forecast for the Alaska Railbelt transmission system, including yearly adoption rates of EVs, BTM solar, and heat pumps and hourly load data for the forecasted year of 2050. The adoption rates were based on the available historical data and compared to other regional and national trends. Two forecasts were created: (1) a moderate adoption forecast based on projections from current adoption rates and comparisons to other regional and national projections and (2) an aggressive forecast, which provides an illustrative comparison of a high adoption rate of 90% for all technologies. The results of these forecasts demonstrate a significant increase in both energy, 80% and 116%, and peak load demand, 113% and 219%, for the moderate and aggressive electrification adoption scenarios, respectively. These findings highlight a need for resource planning and demand management in this region due to the adoption of EVs, BTM solar, and heat pumps. [ABSTRACT FROM AUTHOR]

Electric vehicles have become a crucial component of modern power systems, possessing substantial energy reserves that can be important power supplies in blackouts where the power grid has weak reserves or limited connections to other grids. In order to clarify the technical conditions and control methods of the centralized electric vehicle charging station as the black-start power source of the power grid, assuming that the centralized electric vehicle charging station can be considered a single, large-scale energy storage system, this paper proposes a three-stage coordinated recovery strategy based on the centralized electric vehicle charging station. The strategy involves three distinct stages, beginning with the establishment of AC frequency and voltage by the electric vehicle charging station to initiate the auxiliary load of the power plant. In the middle stage, considering the traditional generator has been connected, the charging station's control mode is set to provide constant active and reactive power output, providing extra voltage and frequency support to the grid-connected generating units and crucial loads. Finally, in the later stage, control strategies are tailored to the charging power stations' capacities, with one group of additional oscillation damping controllers, while the other group adopts additional frequency control to decrease power disturbances, ensuring a smooth recovery of the power grid. A PSCAD/EMTDC-based model was constructed to verify the proposed coordinated grid recovery strategies. The results demonstrated that the centralized station successfully established the voltage and frequency of the AC system, and the designed additional controller also made the recovery process much more stable. [ABSTRACT FROM AUTHOR]

The forthcoming surge in electric vehicle (EV) adoption demands the comprehensive advancement of associated charging infrastructure. In this study, an exploration of EV charging's impact on the power distribution system is conducted via the simulation of a parking lot equipped with six distinct types of EVs, each showcasing unique charging curves, charging power, and battery capacities. A charging profile is synthesized and compared with laboratory-obtained data to ascertain the implications on the grid. To further understand the effects of smart parking on the power distribution system, a mathematical algorithm was created and applied to a segment of an urban electrical grid that includes 70 private residences. Basic electrical parameters were computed using the node voltage method. Four scenarios were simulated: (1) the existing distribution system, (2) the current system plus smart parking, (3) the current system plus 50% of houses equipped with 3.5 kW photovoltaic installations, and (4) the current system plus photovoltaics and smart parking. This paper examines the core distribution system parameters, namely voltage and current, across these four scenarios, and the simulation results are extensively detailed herein. [ABSTRACT FROM AUTHOR]

The transportation sector is a significant source of greenhouse gas emissions. Electric vehicles (EVs) have gained popularity as a solution to reduce emissions, but the high load of charging stations poses a challenge to the power grid. Nuclear-Renewable Hybrid Energy Systems (N-RHES) present a promising alternative to support fast charging stations, reduce grid dependency, and decrease emissions. However, the intermittent problem of renewable energy sources (RESs) limits their application, and the synergies among different technologies have not been fully exploited. This paper proposes a predictive and adaptive control strategy to optimize the energy management of N-RHES for fast charging stations, considering the integration of nuclear, photovoltaics, and wind turbine energy with a hydrogen storage fuel cell system. The proposed dynamic model of a fast-charging station predicts electricity consumption behavior during charging processes, generating probabilistic forecasting of electricity consumption time-series profiling. Key performance indicators and sensitivity analyses illustrate the practicability of the suggested system, which offers a comprehensive solution to provide reliable, sustainable, and low-emission energy to fast-charging stations while reducing emissions and dependency on the power grid. [ABSTRACT FROM AUTHOR]

With the continuous development of intelligent transportation technologies, new ways of energy usage in transportation continue to emerge, which puts forward new requirements for the planning and design of energy systems. However, comprehensive analyses on the characteristics of transportation energy systems and the development trend of energy usage patterns brought by intelligent technologies have rarely been carried out so far. This paper explores this subject by reviewing the recent development and utilization of intelligent technologies in the transportation system and its impacts on energy usage. This review is carried out from three aspects, covering the representative intelligent transportation and energy technologies on vehicles, infrastructures and systems. The scope is limited within road, railway and water transport domains, with a focus on the recent developments in China as a representative. In terms of vehicles, the development trend of the power systems for new energy vehicles, the characteristics of energy usages in electric vehicles and the effects on energy saving and emission reduction are summarized. In terms of infrastructures, new technologies on smart road, smart port, intelligent railway energy system and the usage of clear energy on electric grid for transportation are reviewed, with the consideration of their potential influences on energy usages and the energy consumption characteristics of typical facilities also being analyzed. As for the transportation system, this review has focused on intelligent and connected transportation systems, train control and autonomous systems, and intelligent shipping system, with the emphasis on the energy saving and emission reduction effects of applying these intelligent technologies. The overall development trend of the transportation energy system is then analyzed based on the above materials, in particular, the future energy usage patterns in transportation system are given and the major challenges and obstacles approaching the future scenarios are also identified. [ABSTRACT FROM AUTHOR]

The diffusion of electric vehicles will be strongly related to the capacity to charge them in short times. To do so, the necessity of widespread fast charging stations arises. However, their intermittent demand represents a challenging load for grid operators. In order to relieve their impact on the electrical grid operation, integrating storage systems in the charging stations represents a potential solution, although it complicates the overall system management. Moreover, standard converter architectures for the MV grid interface require the installation of bulky transformers and filters. In order to cope with the mentioned problems, this paper proposes an ultra-fast charging station topology based on a modular multilevel converter (MMC) structure and dual-active bridge (DAB) converters. Thanks to the multilevel converter properties, the proposed charging station can be directly interfaced with the MV grid without requiring transformers or filters. Additionally, exploiting the degree of freedom in the converter control system, such as circulating components, offers uneven power distribution among the converter submodules that can be managed. Along with the MMC control strategy, the article addresses a straightforward methodology to select the main parameters of the DAB converter as a function of the involved grid power and circulating power contributions, with the primary goal of obtaining a trade-off between internal balancing performances and a broad soft-switching region without incurring in converter oversizing. The effectiveness of the proposed charging station is finally discussed through numerical simulations, where its behavior during a power demand cycle is analyzed. [ABSTRACT FROM AUTHOR]

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users' charging requirements, the EV aggregator's power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users' requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users' requirements, and improving the system frequency stability. [ABSTRACT FROM AUTHOR]

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined. [ABSTRACT FROM AUTHOR]

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution. [ABSTRACT FROM AUTHOR]

In order to further evaluate the impact of vehicle-to-grid (V2G) on the distribution network, this paper studies a method to assess the influence of electric vehicles participating in charge and discharge on the voltage quality of the distribution network. First, considering the state of charge of the EV, the participation of the owner and other factors, the charging and discharging model is built. Then, the probabilistic power flow calculation based on Latin hypercube sampling is used to obtain the probability distribution of the voltage amplitude of the charge and discharge load connected to the distribution network, and finally the evaluation index is established to quantify and calculate the voltage quality of the distribution network participating in the V2G process of electric vehicles. Simulation results show that the evaluation method has the advantage of fast calculation speed while ensuring known accuracy, introduces the probability distribution of expected value and variance quantification of voltage amplitude, more intuitively understands the degree of influence on voltage quality before and after V2G, and can effectively assess the impact of electric vehicles accessing the distribution network in V2G mode on the power quality of low-voltage residential areas and industrial and commercial areas, and this evaluation method can provide useful reference for the formulation of future V2G control strategies and the planning of future urban power grids. [ABSTRACT FROM AUTHOR]

In this paper, a survey is presented on the use of optimization models for the integration of electric vehicles (EVs) and charging stations (CSs) in the energy system, paying particular attention both to planning problems (i.e., those problems related to long term decisions such as the siting and sizing of CSs), and operational management problems (i.e., the optimal scheduling of EVs in smart grids, microgrids and buildings taking into account vehicle-to-grid (V2G) capabilities). Moreover, specific attention was dedicated to decision problems that couple transportation and electrical networks, such as the energy demand assessment for a vehicle over a path and routing and charging decision problems for goods and people transportation. Finally, an effort was dedicated to highlighting the integration and the use of EVs in very recent regulation frameworks, with specific reference to the participation in the balancing market through the figure of an aggregator and the inclusion in the management of Energy Communities (ECs) and sustainable districts. [ABSTRACT FROM AUTHOR]

Energy-environmental planning for road transportation involves a vast investigation of vehicles' technologies and electricity production. However, in developing countries where the public transportation sector is growing quickly, energy-environmental planning is urgently needed. This paper evaluates the future electricity demand, as well as fuel consumption and CO2 emissions reduction, due to the operation of an expected increasing number of electric vehicles (EVs) in Pakistan. The planning of EVs up to 2040 is performed with the ePop simulator that calculates the future EVs' electricity demand, while EnergyPLAN® assesses the expected new power capacities. Two scenarios are investigated by penetrating 30% and 90% of 2/3 electric wheelers and cars by 2030 and 2040 compared to 2020, respectively. To fulfill the expected energy demand, PV in the daytime and the national electric grid at nighttime are here considered. Finally, a 9 GW of PV capacity is needed to satisfy the EVs' electricity demand of 14.7 TWh/year, and a 0.7 GW power plants capacity is needed to fulfill 4.7 TWh/year by 2040. Consequently, EVs' charging scenarios at daytime and nighttime are assessed. Results indicated a total reduction of 10.4 Mtonnes of CO2 emissions and 9.1 Mtoe of fuel consumption by 2040 in the transportation sector. [ABSTRACT FROM AUTHOR]

The recent forecasts regarding the penetration of electric vehicles (EVs) in the transport market and their impact on national electricity distribution grids has presented new challenges in the fields of both of application and research. In this context, vehicle-to-grid (V2G) technology presents itself as an extremely valid solution in terms of application of the "demand side flexibility" paradigm. In this context, the aim of the paper is to analyze from a technical and economical point of view the use of EVs as new flexibility resources to provide network flexibility services in an Italian framework. Within this scope, a methodology for evaluating the flexibility service that a single EV or an EV fleet can offer, and therefore for estimating the EV storage system charge and discharge profile and determining its economic benefit, is proposed. Some numerical results and observations are reported to highlight possible incentive mechanisms for motivating EV end-users to offer flexibility services. [ABSTRACT FROM AUTHOR]

Based on the characteristics of electric vehicles (EVs), this paper establishes the load models of EVs under the autonomous charging mode and the coordinated charging and discharging mode. Integrating the EVs into a microgrid system which includes wind turbines (WTs), photovoltaic arrays (PVs), diesel engines (DEs), fuel cells (FCs) and a storage battery (BS), this paper establishes multi-objective economic dispatch models of a microgrid, including the lowest operating cost, the least carbon dioxide emissions, and the lowest pollutant treatment cost. After converting the multi-objective functions to a single objective function by using the judgment matrix method, we analyze the dynamic economic dispatch of the microgrid system including vehicle-to-grid (V2G) with an improved particle swarm optimization algorithm under different operation control strategies. With the example system, the proposed models and strategies are verified and analyzed. Simulation results show that the microgrid system with EVs under the coordinated charging and discharging mode has better operation economics than the autonomous charging mode. Meanwhile, the greater the load fluctuation is, the higher the operating cost of the microgrid system is. [ABSTRACT FROM AUTHOR]

The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles' state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data. [ABSTRACT FROM AUTHOR]

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategies guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper first analyzes the charging users’ various responses to an elastic charging service fee, and introduces the index of charging balance degree to a target region by considering the influence of fast charging loads on the power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing a fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by reasonably guiding electric vehicles (EVs) to different fast charging stations. The simulation results demonstrate the effectiveness of the proposed dynamic charging service pricing and the corresponding fast charging load guidance strategy. [ABSTRACT FROM AUTHOR]

As the penetration of photovoltaic (PV) systems on building rooftops increases, the accumulated effect of the rooftop PV power outputs on electric network operation is no longer negligible. Energy storage resources (ESRs) have been used to smooth PV power outputs, particularly when building load becomes low. In commercial buildings, the batteries of plug-in electric vehicles (PEVs) can be regarded as distributed ESRs. This paper proposes a DC electrical system in a commercial building that enables PEVs to compensate for rooftop PV power fluctuation and participate in tracking signals for grid frequency regulation (GFR). The proposed building system and associated controllers are modeled considering steady-state and dynamic operations of the PV system and PEV batteries. Simulation case studies are conducted to demonstrate the performance of the proposed building system under various conditions, determined by such factors as the maximum voltage, minimum state-of-charge, and desired charging end-time of PEVs batteries. [ABSTRACT FROM AUTHOR]

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator's perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control. [ABSTRACT FROM AUTHOR]

The Smart Transformer (ST) is being envisioned as the possible backbone of future distribution grids given the enhanced controllability it provides. Moreover, the ST offers DC-link connectivity, making it an attractive solution for the deployment of hybrid AC/DC distribution grids which offer important advantages for the deployment of Renewable Energy Sources, Energy Storage Systems (ESSs) and Electric Vehicles. However, compared to traditional low-frequency magnetic transformers, the ST is inherently more vulnerable to fault disturbances which may force the ST to disconnect in order to protect its power electronic converters, posing important challenges to the hybrid AC/DC grid connected to it. This paper proposes a Fault-Ride-Through (FRT) strategy suited for grid-tied ST with no locally available ESS, which exploits a dump-load and the sensitivity of the hybrid AC/DC distribution grid's power to voltage and frequency to provide enhanced control to the ST in order to handle AC-side voltage sags. The proposed FRT strategy can exploit all the hybrid AC/DC distribution grid (including the MV DC sub-network) and existing controllable DER resources, providing FRT against balanced and unbalanced faults in the upstream AC grid. The proposed strategy is demonstrated in this paper through computational simulation. [ABSTRACT FROM AUTHOR]

To solve the problem, because of which conventional quick-charging strategies (CQCS) cannot meet the requirements of quick-charging for multiple types of electric vehicles (EV) on highways where vehicle inflow is excessive, this paper proposed a new quick-charging strategy (NQCS) for EVs: on the premise of not affecting those EVs being charged, the remaining power of the quick-charging pile with multiple power output interfaces is used to provide a synchronous charging service for EVs waiting in the queue. To verify the effectiveness of this strategy, a power distribution model of charging pile and a queuing model of charging station (CS) were constructed. In addition, based on an actual highway service area where vehicle inflow is excessive during the simulation period (0:00-24:00), charging situations of CQCS and NQCS were respectively simulated in a charging station (CS), with different number of chargers, by basic queuing algorithm and an improved queuing algorithm. The simulation results showed that when the relative EV inflow is excessive, compared to CQCS, NQCS not only can reduce user waiting time, charging time, and stay time, but also can improve the utilisation rate of charging infrastructure and service capacity of CS and reduce the queue length of CS. At the same time, NQCS can reduce the impact on the power grid. In addition, in NQCS, the on-demand power distribution method is more efficient than the average power distribution method. Therefore, NQCS is more suitable for quick-charging for multiple types of EVs on highways where vehicle inflow is excessive. [ABSTRACT FROM AUTHOR]

This paper presents the control and simulation of an electric vehicle (EV) charging station using a three-level converter on the grid-side as well as on the EV-side. The charging station control schemes with three-level AC/DC power conversion and a bidirectional DC/DC charging regulator are described. The integration of EVs to the power grid provides an improvement of the grid reliability and stability. EVs are considered an asset to the smart grid to optimize effective performance economically and environmentally under various operation conditions, and more significantly to sustain the resiliency of the grid in the case of emergency conditions and disturbance events. The three-level grid side converter (GSC) can participate in the reactive power support or grid voltage control at the grid interfacing point or the common coupling point (PCC). A fuzzy logic proportional integral (FL-PI) controller is proposed to control the GSC converter. The controllers used are verified and tested by simulation to evaluate their performance using MATLAB/SIMULINK. The comparison of a PI-controller and a PI-Fuzzy controller for the EV charging station shows the effectiveness of the proposed FL-PI controller over conventional PI controller for same circuit operating conditions. A good performance for PI-Fuzzy in terms of settling time and peak overshoot can observed from the simulation results. [ABSTRACT FROM AUTHOR]

Charging of electric vehicles (EVs) on a large scale can cause problems for the grid. Utilizing local flexibility resources, such as smart charging, stationary battery, vehicle-to-grid applications, and local generation can be an efficient way to contain the grid challenges and mitigate the need for grid reinforcement. Focusing on the INSPIRIA charging station located in Norway, this paper investigates the possibility of coping with imminent grid challenges by means of local flexibility. First, the potential grid challenges are estimated with the help of Monte Carlo simulations. Second, cost and performance for the various local flexibility sources are presented. Third, an analysis of the choice of battery, charging process, and battery economy are provided. Finally, the paper discusses the optimal mix of flexibility resources to efficiently mitigate grid challenges at the INSPIRIA charging station. [ABSTRACT FROM AUTHOR]

The objective of this paper is to implement a two-phase, interleaved, bidirectional DC/DC converter topology with an improved voltage conversion ratio for electric vehicle (EV) and DC-microgrid systems. In this study, a two-phase interleaved charge-pump topology is introduced to achieve a high voltage conversion ratio with very simple control circuits. In discharge mode, the circuit topology acts as a voltage-multiplier boost converter to achieve a high step-up conversion ratio (48 V to 240 V). In charge mode, the circuit topology acts as a voltage-divider buck converter to achieve a high voltage step-down conversion ratio (240 V to 48 V). The circuit configuration, operating principle, steady-state analysis and the closed-loop control of the proposed converter are presented. Experiments conducted on a laboratory prototype with 500 W power-rating are presented to verify the effectiveness. The maximum efficiency levels in discharge and charge modes are about 97.7% and 98.4% respectively. [ABSTRACT FROM AUTHOR]

Automatically identifying the new equipment after its integration and adjusting operation strategy to realize "plug and play" functionality are becoming essential for micro-grid operations. In order to improve and perfect the micro-grid "plug and play" function with the increased amount of equipment with different information protocols and more diverse system applications, this paper presents a solution for adaptive micro-grid operation based on IEC 61850, and proposes the design and specific implementation methods of micro-grid "plug and play" function and system operation mode conversion in detail, by using the established IEC 61850 information model of a micro-grid. Actual operation tests based on the developed IED and micro-grid test platform are performed to verify the feasibility and validity of the proposed solution. The tests results show that the solution can automatically identify the IEC 61850 information model of equipment after its integration, intelligently adjust the operation strategies to adapt to new system states and achieves a reliable system operation mode conversion. [ABSTRACT FROM AUTHOR]

Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML. [ABSTRACT FROM AUTHOR]

The influence of electric vehicle charging stations on power grid harmonics is becoming increasingly significant as their presence continues to grow. This paper studies the operational principles of the charging current in the continuous and discontinuous modes for a three-phase uncontrolled rectification charger with a passive power factor correction link, which is affected by the charging power. A parameter estimation method is proposed for the equivalent circuit of the charger by using the measured characteristic AC (Alternating Current) voltage and current data combined with the charging circuit constraints in the conduction process, and this method is verified using an experimental platform. The sensitivity of the current harmonics to the changes in the parameters is analyzed. An analytical harmonic model of the charging station is created by separating the chargers into groups by type. Then, the harmonic current amplification caused by the shunt active power filter is researched, and the analytical formula for the overload factor is derived to further correct the capacity of the shunt active power filter. Finally, this method is validated through a field test of a charging station. [ABSTRACT FROM AUTHOR]

In the grid-tied micro-grid context, energy resilience can be defined as the time period that a local energy system can supply the critical loads during an unplanned upstream grid outage. While the role of renewable-based micro-grids in enhancing communities' energy resilience is well-appreciated, the academic literature on the techno-economic optimisation of community-scale micro-grids lacks a quantitative decision support analysis concerning the inclusion of a minimum resilience constraint in the optimisation process. Utilising a specifically-developed, time-based resilience capacity characterisation method to quantify the sustainability of micro-grids in the face of different levels of extended grid power outages, this paper facilitates stakeholder decision-making on the trade-off between the whole-life cost of a community micro-grid system and its degree of resilience. Furthermore, this paper focuses on energy infrastructure expansion planning, aiming to analyse the importance of micro-grid reinforcement to meet new sources of electricity demand—particularly, transport electrification—in addition to the business-as-usual demand growth. Using quantitative case study evidence from the Totarabank Subdivision in New Zealand, the paper concludes that at the current feed-in-tariff rate (NZ$0.08/kWh), the life cycle profitability of resilience-oriented community micro-grid capacity reinforcement is guaranteed within a New Zealand context, though constrained by capital requirements. [ABSTRACT FROM AUTHOR]

This paper presents the analysis and experimental validation of a single-phase off-board multi-functional electric vehicle (EV) charging station (MF-EVCS), which has a single ac interface and two dc interfaces. As innovative aspects, the proposed MF-EVCS handles the interface of the ac power grid, the dc interface of a renewable energy source (RES), as well as the dc interface of an EV to perform dc charging or discharging of the batteries (in off-board grid-to-vehicle (G2V) or vehicle-to-grid (V2G) modes). Considering the power grid, the individual operation modes of the RES and the EV interfaces can be considered. Moreover, a combination of these modes is also possible. Besides, the MF-EVCS has as key innovative aspect the possibility of operating as an active power filter (APF), supporting the operation with reactive power and/or selected current harmonics. This possibility can be associated with any of the previous mentioned modes. These new features are framed in two distinct scenarios: in a smart home, where the ac-side current can be determined as a function of the other electrical appliances; in a smart grid, where the ac-side current can be determined as a requisite of the power grid. The proposed power theory, as well as the current control strategies for both ac-side and dc-side of the MF-EVCS, are presented in the paper for all the possible operation scenarios. A laboratory prototype was developed to validate the proposed MF-EVCS and the experimental results confirm its suitability for smart homes. [ABSTRACT FROM AUTHOR]

The city of Stockholm is close to hitting the capacity limits of its power grid. As an additional challenge, electricity has been identified as a key resource to help the city to meet its environmental targets. This has pushed citizens to prefer power-based technologies, like heat pumps and electric vehicles, thus endangering the stability of the grid. The focus of this paper is on the district of Hammarby Sjöstad. Here, plans are set to switch from district heating to heat pumps. A previous study verified that this choice will cause overloadings on the electricity distribution grid. The present paper tackles this problem by proposing a new energy storage option. By considering the increasing share of electric vehicles, the potential of using the electricity stored in their batteries to support the grid is explored through technical performance simulations. The objective was to enable a bi-directional flow and use the electric vehicles' (EVs)' discharging to shave the peak demand caused by the heat pumps. It was found that this solution can eliminate overloadings up to 50%, with a 100% EV penetration. To overcome the mismatch between the availability of EVs and the overloadings' occurrence, the minimum state of charge for discharging should be lower than 70%. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are being endorsed as the uppermost successor to fuel-powered cars, with timetables for banning the sale of petrol-fueled vehicles announced in many countries. However, the range and charging times of EVs are still considerable concerns. Fast charging could be a solution to consumers' range anxiety and the acceptance of EVs. Nevertheless, it is a complicated and systematized challenge to realize the fast charging of EVs because it includes the coordinated development of battery cells, including electrode materials, EV battery power systems, charging piles, electric grids, etc. This paper aims to serve as an analysis for the development of fast-charging technology, with a discussion of the current situation, constraints and development direction of EV fast-charging technologies from the macroscale and microscale perspectives of fast-charging challenges. If the problem of fast-charging can be solved, it will satisfy consumers' demand for 10-min charging and accelerate the development of electric vehicles. This paper summarized the development statuses, issues, and trends of the macro battery technology and micro battery technology. It is emphasized that to essentially solve the problem of fast charging, the development of new battery materials, especially anode materials with improved lithium ion diffusion coefficients, is the key. Finally, it is highlighted that red phosphorus is one of the most promising anodes that can simultaneously satisfy the double standards of high-energy density and fast-charging performance to a maximum degree. [ABSTRACT FROM AUTHOR]

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV's information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery's desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems. [ABSTRACT FROM AUTHOR]