The role of electric vehicles (EVs) in energy systems will be crucial over the upcoming years due to their environmental-friendly nature and ability to mitigate/absorb excess power from renewable ...energy sources. Currently, a significant focus is given to EV smart charging (EVSC) solutions by researchers and industries around the globe to suitably meet the EVs' charging demand while overcoming their negative impacts on the power grid. Therefore, effective EVSC strategies and technologies are required to address such challenges. This review paper outlines the benefits and challenges of the EVSC procedure from different points of view. The role of EV aggregator in EVSC, charging methods and objectives, and required infrastructure for implementing EVSC are discussed. The study also deals with ancillary services provided by EVSC and EVs' load forecasting approaches. Moreover, the EVSC integrated energy systems, including homes, buildings, integrated energy systems, etc., are reviewed, followed by the smart green charging solutions to enhance the environmental benefit of EVs. The literature review shows the efficiency of EVSC in reducing charging costs by 30 %, grid operational costs by 10 %, and renewable curtailment by 40 %. The study gives key findings and recommendations which can be helpful for researchers and policymakers.
•To present a comprehensive review on EVSC together with their benefits and challenges,•To review the charging methods for EVs and related characteristics and impediments,•To enumerate the ancillary services that a smart charging mechanism can provide•To discuss the challenges of integrating EVs into energy systems.•Charging costs and grid operational costs can be reduced by 30 % and 10 % via EVSC
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Smart charging of battery electric vehicles (BEVs) can contribute to flexibility in power grids and help integrate renewable electricity. Tapping into this potential requires high user acceptance for ...smart charging and corresponding tariffs. In this paper, we analyze the preferences of current BEV users, representing the potential near-term adopters of smart charging, for different smart charging tariff design elements by conducting a discrete choice experiment with 689 participants in Germany. In doing so, we (1) provide an overview of current BEV users' preferences, (2) identify and characterize BEV user groups with substantial differences in their preferences, and (3) identify barriers for smart charging implementation from the perspective of current BEV users. More specifically, we find that potential cost savings along with the pricing scheme and charging mode are the most important tariff elements, whereof a pre-defined price corridor with an emergency price for grid bottlenecks and charging a safety buffer before applying smart charging are most preferred. We identify three user groups, with a large share of innovative adopters. Moreover, driving range or reluctance regarding data sharing can represent barriers for smart charging adoption. Based on our findings, we derive implications for decision-makers in policy and industry.
•We investigate smart charging preferences in a choice experiment with 689 BEV users.•Financial benefits drive the preferences of all three user clusters we identified.•However, they differ in importance of intervention means and secure pricing schemes.•Lower grid expansion costs and integrating renewables are important motivators.•Implications for several actors in designing a user-friendly tariff system are outlined.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In the last two years there has been a significant increase in the global adoption of electric vehicles (EVs). Particularly in Denmark, 40% of new vehicle registrations in 2023 were electric, marking ...a notable shift towards e-mobility. As a result, EV loading begins to constitute a large part of residential electricity demand. The concurrent increase in the interest for reduction in charging costs via demand response leads to a growing need for better understanding of charging behavior and its impact on consumption profiles. This works addresses the lack of up-to-date large-scale empirical studies on residential EV charging behavior. It presents a thorough analysis of all important charging characteristics based on a large set of 5534 Danish residential chargers between 2021 and 2023, providing unique insight in the effect of user-induced controlled charging. Our analysis shows the difference between actual charging profiles and those commonly used by distribution system operators in Denmark, and reveals the significant change over the span of one year with the doubling of peak demand per charger from 1.25 kW to 2.5 kW.
•Up to date (2021–2023) analysis of residential EV charging data in Denmark.•Data from 5534 residential EV chargers.•Behavioral analysis of real smart charging usage.•Analysis of all main charging characteristics and their evolution.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•The impact of EV charging on distribution networks is likely to vary geographically.•The GB transmission network can support 100% domestic charging, but not under N-1.•Smart charging can reduce the ...networks requiring intervention from 28% to 9%.•Optimising load at the distribution level increases transmission level peak demand.
A rapid increase in the number of electric vehicles is expected in coming years, driven by government incentives and falling battery prices. Charging these vehicles will add significant load to the electricity network, and it is important to understand the impact this will have on both the transmission and distribution level systems, and how smart charging can alleviate it. Here we analyse the effects that charging a large electric vehicle fleet would have on the power network, taking into account the spatial heterogeneity of vehicle use, electricity demand, and network structure. A conditional probability based method is used to model uncontrolled charging demand, and convex optimisation is used to model smart charging. Stochasticity is captured using Monte Carlo simulations. It is shown that for Great Britain’s power system, smart charging can simultaneously eliminate the need for additional generation infrastructure required with 100% electric vehicle adoption, while also reducing the percentage of distribution networks which would require reinforcement from 28% to 9%. Discussion is included as to how far these results can be extended to other power systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
A significant transformation occurs globally as transportation switches from fossil fuel-powered to zero and ultra-low tailpipe emissions vehicles. The transition to the electric vehicle requires an ...infrastructure of charging stations (CSs) with information technology, ingenious, distributed energy generation units, and favorable government policies. This paper discusses the key factors when planning electric vehicle charging infrastructure. This paper provides information about planning and technological developments that can be used to improve the design and implementation of charging station infrastructure. A comprehensive review of the current electric vehicle scenario, the impact of EVs on grid integration, and Electric Vehicle optimal allocation provisioning are presented. In particular, this paper analyzes research and developments related to charging station infrastructure, challenges, and efforts to standardize the infrastructure to enhance future research work. In addition, the optimal placement of rapid charging stations is based on economic benefits and grid impacts. It also describes the challenges of adoption. On the other hand, future trends in the field, such as energy procurement from renewable sources and cars’ benefits to grid technology, are also presented and discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
This paper prescribed the design of controller for electrical vehicle to Grid power, by using this controller improve the power requirement of grid and reactive power compensation ...capability. Bidirectional converter is very helpful during on peak load demand. During off peak load demand grid will supply the power to the battery and charge the battery. During on peak load demand excess power of battery will supply to the grid. The concept aggregator is depicted in the figure 2. (Aggregator collects the power from all electrical vehicle first then it supply to the grid). This modern electrical vehicle technology proposed the distribution generation Methodology. All the control strategies of modern electrical vehicle to grid is proposed like smart charging or discharging of batteries during off peak load demand and On peak load demand respectively. V2G controller allow the active power it act as an ancillary services to grid. Electrical vehicle controller has ability to exchange the active or reactive power capability. Simulation of bidirectional AC/DC and DC/DC controller and their control circuit are analyzed by using matlab Simulink software.
•California’s decarbonization strategy will lead to 1 billion tons of CO2 saved in the light-duty transportation sector.•Grid decarbonization can further decrease emissions by over 100 million tons ...of CO2 in California.•The majority of emissions savings in the technology transition comes from vehicle electrification rather than grid decarbonization.•Smart charging can potentially decrease grid costs in a full renewable transition by about 5%
California has a many activities targeting specific sectors to mitigate climate change. This study models several scenarios of future electric vehicle emissions in the state and explores untapped policy opportunities for interactions between sectors, specifically between the transportation and electricity grid. As electric vehicles become more prevalent, their impact on the electricity grid is directly related to the aggregate patterns of vehicle charging—even without vehicle-to-grid services, shifting of charging patterns can be a potentially important resource to alleviate issues such as renewable intermittency. This study involved the creation of a model to predict the potential emissions benefits of managed vs. unmanaged charging. The study finds that the lion’s share of emissions reduction in the light-duty transportation sector in California comes from electrification, with a cumulative 1 billion tons of CO2 reduction through 2045. This figure represents a decrease of about 4 tons CO2/capita/year from the average operation of Californian passenger vehicles in 2020 to about 40 kg CO2/capita/year in 2045. Decarbonization of the current grid leads to an additional savings of 125 million tons of CO2 over the same time-period. As the state moves towards these objectives through existing (and potential future) policies, additional policies to exploit synergies between transportation electrification and grid decarbonization could reduce cumulative emissions by another 10 million tons of CO2.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Distribution system operators face a challenging environment marked by increased decentralization, digitalization, and the decarbonization of transport and heating sectors. In particular, the ...integration of large numbers of electric vehicles (EVs) will pose challenges for distribution grid operation and planning. However, EVs also open the opportunity to offer flexibility services to different actors in the electricity system using smart charging and vehicle-to-grid (V2G) technology. This work reviewed the scientific literature and key European demonstrator projects on the proactive integration of EVs into distribution grids. The main technical, economic, regulatory, and user-related aspects were analyzed and the associated barriers identified. There is a broad scientific literature on the technical feasibility of EV flexibility provision and coordination schemes, which has as well been proved in demonstrator projects, even though the required technologies for V2G (bidirectional chargers, communication protocols) are not yet widespread. On the other hand, main barriers are economic and institutional, largely due to a lack of regulatory frameworks to value flexibility at distribution level and thus uncertainty on the value of these flexibility services. In particular, this work analyzed four possible value frameworks (grid codes, connection agreements, tariffs and market platforms) to use flexibility at the distribution level, and their implementations with EV fleets in demonstrator projects.
•Holistic review of scientific literature and EV demonstrator projects.•Identification of flexibility services that EVs can provide throughout the value chain.•Identification of technical, economic and regulatory barriers for active EV integration.•Analysis of possible frameworks to use EV-driven flexibility at distribution level.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A system perspective on cost and emission tradeoff of electric vehicle (EV) charging.•A multi-objective optimization method of EV smart charging and vehicle-to-grid (V2G).•Costs and CO2 emissions of ...EV smart charging and V2G can be reduced simultaneously.•Smart charging can mitigate grid congestion problems in case of high EV penetration.•Cost and emissions of grid reinforcement outweigh benefits of increased flexibility.
With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Governments are currently subsidizing growth in the electric car market and the associated infrastructure in order to accelerate the transition to more sustainable mobility. To avoid the grid ...overload that results from simultaneously charging too many electric vehicles, there is a need for smart charging coordination systems. In this paper, we propose a charging coordination system based on Reinforcement Learning using an artificial neural network as a function approximator. Taking into account the baseload present in the power grid, a central agent creates forward-looking, coordinated charging schedules for an electric vehicle fleet of any size. In contrast to optimization-based charging strategies, system dynamics such as future arrivals, departures, and energy consumption do not have to be known beforehand. We implement and compare a range of parameter variants that differ in terms of the reward function and prioritized experience. Subsequently, we use a case study to compare our Reinforcement Learning algorithm with several other charging strategies. The Reinforcement Learning-based charging coordination system is shown to perform very well. All electric vehicles have enough energy for their next trip on departure and charging is carried out almost exclusively during the load valleys at night. Compared with an uncontrolled charging strategy, the Reinforcement Learning algorithm reduces the variance of the total load by 65%. The performance of our Reinforcement Learning concept comes close to that of an optimization-based charging strategy. However, an optimization algorithm needs to know certain information beforehand, such as the vehicle’s departure time and its energy requirement on arriving at the charging station. Our novel Reinforcement Learning-based charging coordination system therefore offers a flexible, easily adaptable, and scalable approach for an electric vehicle fleet under realistic operating conditions.
•Development of a flexible and scalable smart charging strategy for electric vehicles.•Creation of 24h charging schedules for individual electric vehicles.•Possible reduction of grid congestion with smart charging decisions.•Increased user comfort with Reinforcement Learning-based charging algorithm.•Comparison to optimization-based and uncontrolled charging of electric vehicles.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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