Search

Renewable energy‐based electric vehicle (EV) charging systems have become increasingly popular in recent years, particularly in commercial and industrial environments. This study looks at a broad‐spectrum bidirectional buck boost DC to DC converter employing solar photovoltaic (PV) technology. This combination is intended for usage in vehicle to grid (V2G) and grid to vehicle (G2V) modes for electric car applications. In both operating modes, the converter attains a high‐voltage transfer gain ratio (VTGR) without any theoretical output voltage restrictions. It provides evidence on suitable nature for EV applications with versatility, redundancy, and practicality. The theoretical evaluation was verified by the development and testing of a 500 W experimental model. A peak efficiency of 97.8% and 97.4% in V2G and G2V modes is indicated by the data, confirming the usefulness and efficacy of this integrated strategy. These results show that the bidirectional buck boost DC to DC converter, when combined with the suggested integration of PV and EV, improves V2G‐G2V operations in EV charging systems and is both practicable and feasible. [ABSTRACT FROM AUTHOR]

The profitable commercialization and swift growth in electrified transportation requires load management controllers. Effective load management controllers can reduce the peak load on the power grid, balance the load demand and improve the stability of the power grid. Several techniques are available to optimize the load, including battery swapping, vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-home (V2H) approaches. This paper addresses effects of rising adoption of electric vehicles (EVs) and the potential for an increased load on the distribution system, which requires load optimization to ensure sustainable development. This paper offers a comprehensive review of all possible energy management controllers used in EVs for load optimization and power split controllers for splitting power between the battery and super-capacitor in hybrid energy storage systems (HESS) to increase battery life span. These controllers are classified into three categories, fuzzy logic controller, voltage-based controller and soft computing-based controller, with their constraints, advantages and disadvantages and finally sum up with their current challenges and future recommendation. The paper also reveals the wireless charging with their pros and cons. The findings have implications for the design and implementation of controllers for EVs to ensure their sustainable development and reduce fossil fuel usage. This study will be highly helpful to the researchers, industrialist and investors as a ready literature of energy management controllers for EVs, with information of their recommendations and challenges. [ABSTRACT FROM AUTHOR]

The transportation sector is undergoing a profound transformation, shifting from fossil fuel reliance to electric and hybrid semi-electric alternatives. In response, European countries are implementing novel concepts like electrified highways for trucks and buses, bridging the gap between traditional and electric mobility. This paper centers on the management of electric vehicle (EV) charging infrastructure within industrial zones, crucial nodes for charging networks due to their concentrated economic activity and vehicular movement. The study delves into optimal strategies for deploying charging stations in these zones, considering factors such as station placement, capacity planning, and integration with smart grids to ensure efficient and accessible EV charging. Moreover, the research extends its focus to the integration of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies, illustrating their potential within industrial zones. In our research, we have developed algorithms tailored for the infrastructure of industrial zones, focusing on the integration of storage systems and the charging and discharging dynamics of electric vehicles (EVs). Our case study, supported by numerical simulations, illustrates the outcomes of a 24-hour timeframe, where 126 vehicles were charged, and 134 were discharged. The results provide a comprehensive view of how the grid-maintained balance throughout these operations, ensuring that industrial facilities received the required power to fulfill their operational demands. [ABSTRACT FROM AUTHOR]

This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault tolerance. The varied DC-link voltage can improve driving performance and reduce battery energy consumption over a wide speed range. Through a well-designed current control scheme, speed control scheme, and dynamic commutation tuning scheme, the established SRM drive possesses good performance in the motor driving mode. During deceleration, the regenerative braking energy can be effectively recovered to the battery. When the EV is in idle mode, the grid-to-vehicle (G2V) charging operation can be conducted through the bidirectional switch mode rectifier (SMR) and CLLC resonant converter. Satisfactory charging performance with good line drawn power quality and galvanic isolation is preserved. Conversely, the vehicle-to-grid (V2G) discharging operation can be performed. The EV can make movable energy storage device applications. Finally, the interconnected operations of the developed EV SRM drive to vehicle and microgrid are presented. Through vehicle-to-vehicle (V2V) operation, it can supply energy to the nearby EV when the battery is exhausted and needs roadside assistance. In addition, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) operations can also be conductible. The EV battery can be charged from the microgrid. Conversely, it can also provide energy support to the microgrid. [ABSTRACT FROM AUTHOR]

Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff ( $\beta $ ) is ( $0.02~{\$}/kWh$ )when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus $\beta $ relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.

In recent days the high penetration of EVs have an impact on the load demand and it will be increased further. This results in stress on the grid and proper energy management in the charging stations. Recent development also enables smart communication between EV user and charging station. A bidirectional converter-based charging station works on V2G and G2V modes for charging the EV battery and supports the grid or isolated power station when it is needed. In this paper, a brief discussion on the previous development of bidirectional conversion is presented. A bidirectional converter is modeled and simulated in Simulink. In G2V mode, the time of energy-based tariff is used for the cost calculation, and for V2G mode, the system monitoring of the EV charging parameters is presented where the pricing varies from 11.09 Rs. To 16.27 Rs. For an hour of power consumption using a 2.54 kW charger on a probabilistic model. For V2G operation, the inverter voltage and grid voltage in synchronization is presented. An IoT-based system is initiated in Simulink using thing speak. This put the data in the cloud and further the data is monitored and fetched from the cloud using a web application.

Este artigo apresenta a análise e operação de um conversor CA-CC trifásico, bidirecional e isolado em alta frequência, de único estágio com correção do fator de potência, destinado para aplicação como carregador embarcado de baterias de veículos elétricos. Este conversor pode operar nos modos Grid-to-Vehicle (G2V) e Vehicle-to-Grid (V2G). No lado primário tem-se um conversor boost trifásico intercalado (interleaving) com modulação PWM senoidal para conexão com a rede elétrica, e na saída um barramento CC com ponto médio. O lado secundário é composto por uma topologia em ponte completa com modulação PWM convencional de razão cíclica de 50%. A transferência de potência do lado primário ao secundário é realizada por meio da técnica de defasamento angular (phase-shift), a mesma utilizada em conversores Dual-Active Bridge (DAB). Devido a defasagem natural do sistema trifásico, é possível obter transferência de potência constante, de reduzida ondulação, por meio do controle do defasamento angular também constante. Os princípios de operação do conversor são apresentados e demonstrados por análises qualitativas e quantitativas. Resultados experimentais de um protótipo de potência de 3 kW, tensão de entrada da rede elétrica de 127 V/60 Hz, tensão de saída de 300 V e frequência de comutação de 50 kHz são apresentados. Em conformidade com as análises, o protótipo doconversor apresentou fator de potência de 0,997, potência transferida entre primário e secundário constante e de baixa ondulação e dinâmica satisfatória e rápida durante ensaios de inversão do fluxo de potência.

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. We provide an innovative Energy Management Strategy (EMS) for MG with grid support in this academic publication. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. The period of 24 h is divided into 96 intervals (t) and storage requirements (kWh/t) are estimated based on the estimated load and RES together with the necessary storage volume. We employ three approaches that are frequently used for communication channel power allocation optimization to accomplish OAES. With two objectives: minimum network power loss plus voltage fluctuations, the Multi-Objective Optimization Problem (MOOP) is solved for each 't' based on OAES to provide the Optimal Power Flow (OPF). The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG.

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]

The storage capacity of Electric Vehicles (EVs) can be used to improve energy management in low-voltage distribution networks. The research throughout the previous decade has concentrated on several control strategies for grid auxiliary assistance via Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V). This article focuses on energy management in distribution networks using EVs, solar photovoltaic (PV), and diesel generators (DG). To begin with, Water Filling Algorithm (WFA) is utilized to disperse EV storage optimally in each zone of energy needed for load flattening. The goal here is to decrease grid reliance while minimizing overall energy costs. After that, a win–win plan is formed between the EV owner and the grid operator to lower EV charging costs and increase EV storage usage for grid support. A multi-Objective Optimization problem is formulated with two objectives: load flattening and voltage regulation. This is solved by Multi-Objective Genetic Algorithm (MOGA) where the decision variable is EV power transaction, Optimal Power Transaction (OPT). ANFIS-based EV ranking technique has been designed to achieve these two aims concurrently. The influence of Optimal Energy Distribution (OED) has been studied in different scenarios. ANFIS prioritization is investigated in several scenarios, as well as the impact on overall EV power availability and Cost of Charging (CoC). [ABSTRACT FROM AUTHOR]

This report makes an initial investigation into the potential for combining very high penetration levels of electric vehicles with similarly very high penetration of variable renewable electricity (VRE) in California. A literature review is performed regarding the potential for high levels of EV sales and VRE penetration at both the U.S. and California level. Such scenarios have been developed by a number of researchers, such as U.S. national laboratories for the White House (under the Obama Administration), and by Energy and Environmental Economics, Inc. (E3) for the California Energy Commission. Such studies indicate that both of these “extreme” futures are entirely plausible and have the potential to coexist. However, none of the reviewed studies has undertaken detailed analysis of how large numbers of EVs could interact with and support a VRE-dominated system, and how these might interact in a useful way. This could include grid-to-vehicle (G2V) and vehicle- to-grid (V2G) movement of electricity, with vehicle batteries providing large scale electricity storage.We undertake our own preliminary simulation for a 2030 and 2050 scenario for California, using an 8760 hours (full year) electricity demand profile and VRE generation example. We assume a ramp- up of VRE to 60% of all electricity generation by 2030 and 100% by 2050, with a similar increase in the EV share of new LDV sales, creating a significant stock (about 7 million) by 2030 and nearly complete transition (to over 20 million vehicles) by 2050. Using an “averages, peaks and valleys” analysis on the electric side, and a typical spare battery storage potential on the vehicle side, our simulation shows that by 2030 a large share of excess VRE electricity generation could be stored, and a large share of electricity shortfall from VRE could be provided, by electric vehicle batteries throughout the year, though there would be many cases where they cannot provide full coverage of these situations. However by 2050, if nearly 100% of the fleet were EVs, only about half of their available, spare capacity is needed to store the excess electricity from a full VRE system on the highest generation day and only about 40% would be needed to store and supply the shortage from lack of VRE generation on the highest shortfall day.While these results are encouraging, a deeper simulation is needed to provide a true hour-by-hour assessment of battery use and the incidence of storage need compared to driving need. Management of charging times that could not be assessed here may also play a critical role. In addition, our initial assessment only covers a single day shortfall. Shortfalls could occur for longer periods, particularly if the VRE electricity system were sized to take better advantage of seasonal storage options. Vehicle batteries are best suited to very short duration storage and may not be adequate to keep the electricity reliable for many consecutive days of shortfall. Hydrogen (H2) has the potential to be a longer-term energy storage option and could be stored in fuel cell vehicle tanks (and the H2 system associated with generating, storing and distributing H2 to those tanks). The next stage of our research will involve running a full simulation using our (ITS-Davis) California ZEV power model (“CALZEV”), a version of the larger Message model, applied to consider both electricity and hydrogen (with large numbers of both of these types of vehicles) in order to: 1) gauge the relative storage potential and cost over a range of time frames and VRE scenarios, and 2) estimate the relative value and possible synergies in a system with both types of vehicles and fuels.

An adaptable infrastructure for dynamic power control (AIDPC) of battery chargers for electric vehicles has been proposed in this work. The battery power is dynamically adjusted by utilizing flexible active load management when the vehicle is plugged in. The battery charging and discharging prototype model is developed for storing the surplus power during the off-peak period and delivering the power during the shortfall of the grid. The development and adoption of two charging strategies, namely the constant current (CC) charging and the proposed dynamic constant current (DCC) charging, has been elaborated. When the Grid to Vehicle (G2V) operation mode is active in the CC charging method, the batteries are charged from the power grid with a constant amplitude sinusoidal current and unity power factor. In the proposed method of DCC, the batteries are charged from the grid with a decreased amplitude in sinusoidal current and unity power factor, and the battery charges from the dynamic dc link voltage. The battery charging and discharging depends upon the rate of change of dc link voltage according to the load variation of the grid. The simulation and experimental results support the rapid time response of the (AIDPC) load profile of the electrical system experiences a new peak due to the CC charging method. On the other hand, the vehicle scheduling algorithm successfully draws less power from the grid when the DCC charging method is used. [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.

Widespread recharging of electric vehicle (EV) batteries could lead to frequent overloads, excessive power loss, and severe voltage fluctuations, especially at the distribution-system level. These challenges can be mitigated with smart charging initiatives, in which the system operator regulates EV charging with certain technical or economic objectives, provided that the EV owners are prepared to relinquish the charging control of their vehicles. Amidst concerns regarding the potential hike in electricity bills due to domestic EV charging, cost-minimizing objectives have been identified as compelling motivation for EV owners to participate in centralized charging programs. This paper presents a dynamic strategy for smart charging that can account for the uncertainties associated with vehicle mobility. The charging scheme aims to minimize energy costs with respect to a real-time pricing tariff while fulfilling the charge requirement of all EV users. The benefits of smart charging under both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes are analyzed. Furthermore, the impact of smart charging on the distribution system is assessed in terms of system demand, distribution efficiency, and load voltage. Results indicate that the proposed technique can reduce the consumers' energy bill by roughly one third. Although the smart V2G method leads to maximum saving, the inclusion of battery degradation cost tips the balance in smart G2V's favor. Moreover, the pair of smart charging solutions improves distribution-system operation, with all the monitored metrics of power distribution showing significant improvement. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle's battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem. [ABSTRACT FROM AUTHOR]

This article introduces a unique dynamic balance strategy (DBS) that can overcome many disadvantages such as voltage and frequency deviations, overloading, high infrastructure costs that may occur in the network integration of electric vehicles (EVs). The proposed method is also aimed at keeping the comfort and satisfaction of the EV users at a high level without the obligation of the EV owners to inform the operator of the entry and charge planning. DBS introduces a real-time energy-sharing method that uses both the grid-to-vehicle (G2V) and vehicle-to-vehicle (V2V) topologies simultaneously without knowing the power demand profile in advance. However, from the perspective of smart grid, it is also possible to activate the vehicle-to-grid (V2G) topology in case of frequency or voltage fluctuations in the network with the proposed DBS. The proposed DBS was simulated in real time and phasor analysis for different penetration levels in MATLAB/Simulink program. Considering that EV driver behavior is highly variable and will vary regionally, it is shown that the proposed DBS gives dynamic results against all behavioral situations, varying penetration levels, and all the negativities that may occur in the penetration of EVs are overcome by keeping the satisfaction of EV drivers at a high levels. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are experiencing substantial investment and widespread acceptance. However, successful penetration of the global market is contingent upon the development of a strategic plan for the efficient allocation of EVs to optimal charging stations (CSs). This study combines several optimization models to systematically assign EVs to the optimal charging stations, with the goal of maximizing trading energy while simultaneously minimizing total response time. Factors taken into consideration include traveling distance, charging (V2G), and discharging (G2V) energy trading, total response time, and energy prices. The efficacy of the combined models is validated using GAMS and BARON solvers, with a focus on EV satisfaction factor, updated energy and response time, number of served EVs, and alleviation of range anxiety. The proposed models demonstrate 85% satisfaction factor for the majority of charging requests, reaching almost 99% for discharging requests. These results surpass those of contemporary models, underscoring the heightened effectiveness of the proposed approach. [Display omitted] • Optimal EV allocation balances energy trading and response time. • Comprehensive model considers mobility, V2G, G2V, response, and prices. • GAMS simulations show consistently high EV satisfaction levels. • Superior outcomes compared to COP and EVaaS models in satisfaction. • Future work targets an emergency model for EVs with low energy levels. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle’s battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem.

La integración de sistemas estacionarios y de los vehículos eléctricos como almacenamiento electroquímico en la red eléctrica, traerán consigo una serie de acciones en los sistemas eléctricos de potencia a fin de robustecer la red existente, diversificar las mallas energéticas actuales y lograr una contribución significativa ante los requerimientos energéticos de la región y los problemas asociados debido al cambio climático. El presente artículo expone las principales oportunidades y desafíos de su integración, muestra la evolución y situación actual de la matriz eléctrica de Centroamérica. La región presenta condiciones favorables para permitir una penetración de diversas tecnologías de almacenamiento en combinación con generación de electricidad intermitente lo cual representa una nueva consideración en el análisis. Diversas tecnologías de almacenamiento son expuestas que, a pesar de ser antiguas en su descubrimiento y uso, hoy se presentan como una oportunidad para la producción masiva de energía eléctrica renovable. Por último, se analizarán las formas en que dichas tecnologías pueden interactuar con la red eléctrica como unidades de almacenamiento y carga. Finalmente, se identificarán las principales oportunidades y desafíos de la integración de sistemas de almacenamiento electroquímico en la red eléctrica.

In existing electric vehicle (EV) charger topology, due to the presence of line frequency transformer and bidirectional DC-DC/AC converters, the circuit complexity in the structure gets increased in EV charger; thereby, it results bigger in size for high capacity. Moreover, due to rectification mode, it consumes the power with poor power quality. To overcome these drawbacks, this paper proposes a single input-dual port (SIDP) isolated bidirectional DC-DC converter (IBDC) to achieve ultra-fast EV charging of the battery. Also, to improve the power quality, a novel DC-link-fed PFC control strategy is proposed in this paper. Moreover, enabling grid to vehicle (G2V) and vehicle to grid (V2G) operation at bidirectional way depends on the situation such as state of charge (SOC) levels, off-peak and peak hours and user-defined data, which have been implemented using optimal algorithmic approach (OAA). The closed-loop control strategies are implemented to transfer the power at an accurate range using PWM plus phase-shifting approach. Moreover, it has the advantages of smaller in size due to the presence of medium frequency transformer and power quality gets improved without any additional converter stage. Finally, to validate the proposed operation, the results are observed under various case studies and conditional input from the customer and presented in this paper. Furthermore, the experimental results have been presented to validate the operation. [ABSTRACT FROM AUTHOR]

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. We provide an innovative Energy Management Strategy (EMS) for MG with grid support in this academic publication. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. The period of 24 h is divided into 96 intervals (t) and storage requirements (kWh/t) are estimated based on the estimated load and RES together with the necessary storage volume. We employ three approaches that are frequently used for communication channel power allocation optimization to accomplish OAES. With two objectives: minimum network power loss plus voltage fluctuations, the Multi-Objective Optimization Problem (MOOP) is solved for each 't' based on OAES to provide the Optimal Power Flow (OPF). The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG. • In this scholarly article present a pioneering Energy Management Strategy (EMS) for MG with grid support. • To reduce grid dependency by utilizing RES and EV storage. The EMS comprises three stages of execution: Optimal Allocation EV Storage (OAES), Optimal Power Allocation (OPA) for Fleet, and Ranking for EV Recommendation (RER) • The key objectives in this stage are: min, the battery's degradation, and maximize its utilization for MG support. [ABSTRACT FROM AUTHOR]

Vehicle‐for‐grid (VfG) is introduced as a mobile energy storage system (ESS) in this study and its applications are investigated. Herein, VfG is referred to a specific electric vehicle merely utilised by the system operator to provide vehicle‐to‐grid (V2G) and grid‐to‐vehicle (G2V) services. The advantages of VfGs over the ESSs and plug‐in electric vehicles (PEVs) include mobility of the VfGs across the distribution system and their complete availability for the system operator, respectively. In this study, VfGs are utilised by the distribution company (DISCO) to minimise the daily operation cost of the distribution system by providing the V2G and G2V services in optimal buses of the feeders. In addition, VfGs are applied by the generation company (GENCO) to minimise the daily operation cost of the generation system by providing the V2G and G2V services at optimal time periods. It is demonstrated that optimal application of VfGs has a considerable potential for cost reduction for both DISCO and GENCO. In fact, the DISCO and GENCO are benefitted because of the minimisation of feeders' power loss and deferring the expensive generation units, respectively. Additionally, it is proven that cooperation of GENCO and DISCOs in utilisation of the VfGs has more benefit for them. [ABSTRACT FROM AUTHOR]

The usage of electric vehicle batteries to assist the main electric grid for the storage of energy provided by intermittent sources should become an essential tool to increase the penetration of green energies. However, this service induces additional usage on the cells and, therefore, could degrade them further. Since degradation is path-dependent, it is of paramount importance to test the impact of all the different grid applications on the batteries. In this work, we tested the additional usage induced by using electric vehicle batteries for frequency regulation at moderate rates during rest or charge and found no detrimental effect after around 2000 cycles on the cells.

Copyright of Scientific Papers of Electrical & Control Engineering Faculty Technical University of Gdansk / Zeszyty Naukowe Wydzialu Elektrotechniki & Automatyki Politechniki Gdanskiej is the property of Gdansk University of Technology, Faculty of Electrical & Control Engineering 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.)

• Charging infrastructure sharing concept is introduced for electric vehicles (EV). • A methodology is proposed to assess the optimal cost and savings for EV deployment. • The best combination of heavy duty and light duty EV deployment is recommended. • Heavy duty EV is more economical with fixed travel schedules to save energy cost. • Net metering reduces the payback time. The proliferation of electric vehicles (EVs) all around the world offers both challenges and opportunities to build a sustainable city and transportation system. Bidirectional charging capabilities at workplace charging facilities (e.g., as part of a microgrid) have made the overall economic optimization more attractive on one hand, but also more complex on the other hand. This paper investigates the cost optimization problem for bidirectional charging at a workplace microgrid connected to two different buildings to determine the optimal framework for a combination of both heavy-duty and light-duty electric vehicles (HDEV and LDEV). A deep learning-based model has been developed to forecast the 15-minute solar generation and building power consumption. Real-time travel profile data has been used to represent the temporal uncertainty of electric vehicle charging. The cost optimization problem is formulated as a Mixed Integer Programming (MIP) model which also addresses battery life degradation. Furthermore, a comprehensive economic analysis has been carried out to analyze the payback period, peak reduction, and cost savings for two different buildings at the same workplace with both on-board and off-board charger configurations. It has been found that HDEV is a better cost-effective solution in comparison to LDEV in terms of energy cost reduction and payback periods. Net metering capability leads to higher energy savings and peak reductions in most cases. [ABSTRACT FROM AUTHOR]

The battery is a key component in Plug-in Electric Vehicles (PEVs) whose degradation should be considered in vehicle modeling and if the battery pack is to be used in a Vehicle to Grid (V2G) smart grid studies. Several researchers have proposed different methodologies for PEV batteries degradation modeling from various aspects. Most of the battery degradation literature consists of empirical-based studies with results extracted from experimental tests in laboratories. As such, the results have been presented in non-formulated forms and are of less effectiveness for smart grid researchers. Furthermore, the impact of battery degradation in V2G smart grid have not been examined in smart grid studies. This paper reviews and compares different battery technologies focusing on Lithium-ion batteries which dominant in today and future vehicle applications. After that the most prominent degradation models are assessed, the effects of degradation factors on battery performance are examined. The literature shows that the degradation causes can be categorized into two groups namely calendar ageing and cycling ageing. Generally, the calendar ageing is influenced by temperature, time, and state of charge, while the cycling ageing is influenced by cycle number, charge rate and depth of discharge. Finally, in this work a conceptual framework for battery degradation modeling is proposed that can be easily used in smart grid studies, without necessarily requiring a detailed understanding of fundamental electrochemical processes. The proposed framework considers not only the battery degradation modeling, but also that of other related components in a smart grid. [ABSTRACT FROM AUTHOR]

Upgrading the internal combustion engine (ICE) driven cars to the electric vehicles (EVs) offers the opportunity to reduce the fossil fuel consumption, emission rates and total driving costs. However, the large scale utilization of the EVs introduces a stochastic load demand to the power grid. The effect of EVs charging demand on the distribution network operation should be investigated properly. This paper proposed a novel model to study the effects of power exchange between the grid and EVs on the power system demand profile, the operation stability index, and the reliability indices. To this end, the operation instability indices are introduced by the range and standard deviation of the load factor of the network components to evaluate the system stability. Further, the CAIFI, 1 1 Customer Average Interruption Frequency Index. SAIDI, 2 2 System Average Interruption Duration Index. SAIFI, 3 3 System Average Interruption Frequency Index. and ASAI 4 4 Average Service Availability Index. reliability indices are calculated for various vehicle-to-grid (V2G) and grid-to-vehicle (G2V) power level to estimate the impact of different level of power exchange on system reliability. We introduced an EV charging scheduling approach which considers the specification of Li-Ion battery and the limitations for increasing battery life. The power exchange profile for V2G is also calculated using the constant power method to discharge the energy at different levels for times which cars are parked at the workplace. Due to the stochastic nature of EVs, the minimal path method is used to compute the stability and reliability parameters and the backward–forward algorithm is used to load flow analysis. The proposed model is evaluated via modified IEEE 33-bus test system. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) technology represents an unprecedented opportunity to transfer the energy industry into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. On the other hand, the emergence of electric vehicles (EVs) promises to yield multiple benefits to both power and transportation industry sectors, but it is also likely to affect the SG reliability, by consuming massive energy. Nevertheless, the plug-in of EVs at public supply stations must be controlled and scheduled in order to reduce the peak load. This paper considers the problem of plug-in EVs at public supply stations (EVPSS). A new communication architecture for SG and cloud services is introduced. Scheduling algorithms are proposed in order to attribute priority levels and optimize the waiting time to plug-in at each EVPSS. To the best of our knowledge, this is one of the first papers investigating the aforementioned issues using new network architecture for SG based on cloud computing. We evaluate our approach via extensive simulations and compare it with two other recently proposed works, based on real supply energy scenario in Toronto. Simulation results demonstrate the effectiveness of the proposed approach when considering real EVs charging–discharging loads at peak-hours period. [ABSTRACT FROM PUBLISHER]

Vehicle-to-grid and Grid-to-vehicle strategies are often cited as promising to mitigate the intermittency of renewable energy on electric power grids. However, their impact on the vehicle battery degradation has not been investigated in detail. The aim of this work is to understand the impact of bidirectional charging on commercial Li-ion cells used in electric vehicles today. Results show that additional cycling to discharge vehicle batteries to the power grid, even at constant power, is detrimental to cell performance. This additional use of the battery packs could shorten the lifetime for vehicle use to less than five years. By contrast, the impact of delaying the charge in order to reduce the impact on the power grid is found to be negligible at room temperature, but could be significant in warmer climates. [ABSTRACT FROM AUTHOR]

The expected increase in the penetration of electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) will produce unbalanced conditions, reactive power consumption and current harmonics drawn by the battery charging equipment, causing a great impact on the power quality of the future smart grid. A single-phase semi-fast electric vehicle battery charger is proposed in this paper. This ac on-board charging equipment can operate in grid-to-vehicle (G2V) mode, and also in vehicle-to-grid (V2G) mode, transferring the battery energy to the grid when the vehicle is parked. The charger is controlled with a Perfect Harmonic Cancellation (PHC) strategy, contributing to improve the grid power quality, since the current demanded or injected has no harmonic content and a high power factor. Hall-effect current and voltage transducers have been used in the sensor stage to carry out this control strategy. Experimental results with a laboratory prototype are presented.

This paper presents the establishment and operation control of a dc microgrid incorporating with electric vehicle (EV) as movable energy storage. The developed microgrid consists of a wind permanent-magnet synchronous generator ac source and a dc source, a battery/supercapacitor (SC)/flywheel hybrid energy storage system, and a grid-connected bidirectional single-phase three-wire (1P3W) load inverter. Various harvested ac and dc sources can be plugged into the microgrid via the developed interfaced power converters. In addition to good microgrid operation performance, the microgrid-to-grid and grid-to-microgrid operations can be successfully conducted with good power quality. As to the developed EV interior permanent-magnet synchronous motor drive, the SC auxiliary storage is equipped to assist the main battery source in intermittent energy conversion. Satisfactory acceleration/deceleration/reversible and regenerative braking operation characteristics are obtained via the developed power stages and control schemes. Moreover, the successful microgrid-to-vehicle and vehicle-to-microgrid interconnected operations of EV and microgrid are achieved. In addition, the vehicle-to-grid/grid-to-vehicle operations can be also performed via the developed 1P3W inverter. [ABSTRACT FROM AUTHOR]

This paper develops an electric vehicle switched-reluctance motor (SRM) drive powered by a battery/supercapacitor having grid-to-vehicle (G2V) and vehicle-to-home (V2H)/vehicle-to-grid (V2G) functions. The power circuit of the motor drive is formed by a bidirectional two-quadrant front-end dc/dc converter and an SRM asymmetric bridge converter. Through proper control and setting of key parameters, good acceleration/deceleration, reversible driving, and braking characteristics are obtained. In idle condition, the proposed motor drive schematic can be rearranged to construct the integrated power converter to perform the following functions: 1) G2V charging mode: a single-phase two-stage switch-mode rectifier based charger is formed with power factor correction capability; 2) autonomous V2H discharging mode: the 60-Hz 220-V/110-V ac sources are generated by the developed single-phase three-wire inverter to power home appliances. Through the developed differential mode and common mode control schemes, well-regulated output voltages are achieved; 3) grid-connected V2G discharging mode: the programmed real power can be sent back to the utility grid. [ABSTRACT FROM PUBLISHER]