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In the US, over 400 state and local incentives have been issued to increase the adoption of plug-in electric vehicles (PEVs) since 2008. This article quantifies the influence of key incentives and enabling factors like charging infrastructure and receptive demographics on PEV adoption. The study focuses on three central questions. First, do consumers respond to certain types of state level vehicle purchase incentives? Second, does the density of public charging infrastructure increase PEV purchases? Finally, does the impact of various factors differ for plug-in hybrid electric vehicles (PHEV), battery electric vehicles (BEV) and vehicle attributes within each category? Based on a regression of vehicle purchase data from 2008-2016, we found that tax incentives and charging infrastructure significantly influence per capita PEV purchases. Within tax incentives, rebates are generally more effective than tax credits. BEV purchases are more affected by tax incentives than PHEVs. The correlation of public charging and vehicle purchases increases with the battery-only driving range of a PHEV, while decreasing with increasing driving range of BEVs. Results indicate that early investments in charging infrastructure, particularly along highways; tax incentives targeting affordable BEVs and PHEVs with higher battery only range, and better reflection of the environmental cost of owning gasoline vehicles are likely to increase PEV adoption in the US.

The transition to low-emission road transport in Europe depends not only on the growth of plug-in electric vehicle (PEV) uptake, but also on the timely expansion of publicly accessible charging infrastructure. This article provides a descriptive and diagnostic assessment of the relationship between electrification dynamics and public charging infrastructure development in Europe. The analysis combines a long-run descriptive window (2015–2024, with 2025 treated separately as a scenario observation) and a core diagnostic window (2020–2024) for which a consistent proxy of potential infrastructure strain—plug-in vehicles per public recharging point (VPP)—is available. The results show a strong increase in PEV share in new registrations, from 1.0% in 2015 to 20.92% in 2024, while the number of public recharging points rose from 67,064 to 900,000 over the same period. In the core sample, VPP declined from 15.24 in 2020 to 13.92 in 2024, which is consistent with a catch-up phase in infrastructure deployment after 2021. At the same time, the short-window relationship between PEV share, infrastructure scale and average CO2 emissions of newly registered cars remains weak and unstable, indicating the role of additional structural factors. The article contributes a transparent, replicable indicator-based framework for describing infrastructure strain in aggregate European data. In policy terms, the findings support a shift from simple point-count targets toward functionally and spatially differentiated infrastructure planning, including interoperability, power structure, and accessibility in underserved areas.

Plug-in electric vehicles (PEVs) have become a key factor driving towards smart cities, which allow for higher energy efficiency and lower environmental impact across urban sectors. Industry vision for future PEV includes the ability to recharge a vehicle at a speed comparable to traditional gas refuelling, i.e. <3 min. per vehicle. Such a technology, referred to as ultra-fast charging (UFC), has drawn much interest from research and industry. However, UFC poses unprecedented challenges to existing electricity supply infrastructure due to its large power density, impulsive, and stochastic load characteristics. Planning the locations and electric capacities of UFC stations is critical to preventing detrimental impacts. In particular, efforts must be made of mitigate grid asset depreciation, grid instabilities, and deteriorated power quality. The authors first review planning methods for conventional charging stations. Next, they discuss outlooks for UFC planning solutions by drawing an analogy with renewable energy (RE) source planning. This analogy is based on the similar power density and stochastic characteristics of RE and UFC. While this study mainly focuses on UFC planning from the power grid perspective, other urban aspects, including traffic flow and end-user behaviour, are examined for feasible UFC integration within smart cities.

Electric vehicles (EVs) have proved capable of solving many of the environment’s problems such as reducing harmful pollutants’ emission along with having greater motor efficiency than gasoline vehicles. This study presents a bibliometric analysis of 10,426 publications from the year 1989 to 2020, obtained from Web of Science™ (WoS) core collection (CC). An initial citation analysis was done using Histcite to identity the leading nations, institutes, authors, and journals performing research related to EVs. Following this, a co-citation analysis was performed using VOSviewer, which generates clusters that are further analyzed to identify the key domains in EV research. A research overview in EVs over the last three decades is presented that can serve various stakeholders in this field of study. The results of this study will highlight the critical research areas in the field of EVs. Additionally, it will also provide various insights that may help the policymakers, practitioners and associations to accelerate EV adoption by the end-users.

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization.

This paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62$, 85636.87 $ , and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day. Dynamic economic emission dispatch with the plug‐in electric vehicles and home distributed photovoltaic generation.

Summary Industrial parks are forming industrial microgrids (IMGs) with factories, distributed energy resources, electric loads, heat loads, and combined heat and power systems as well as renewable distributed energy resources and plug‐in electric vehicles (PEVs). Generation scheduling (GS) in IMGs is affected by the stochastic behavior of electric and heat loads due to outages of production processes or production lines and the uncertainties in solar irradiance and combined heat and power systems. This paper presents a stochastic scenario‐based GS framework to consider uncertainties in an IMG coordinated with PEV charging. Although the scenario‐based methods are usually very time consuming, this paper shows that their applications in IMGs will not significantly increase the calculation time. The proposed formulation guaranties that occurrence of each condition of uncertainty will not affect the PEV activities. An IMG with 12 factories, photovoltaic generations, and 6 types of electric vehicles with different battery sizes is considered and simulated. The main contributions are (1) a new stochastic GS problem formulation to minimize the cost of IMGs while fully charging all PEVs within their requested periods considering the network security, factories, and PEV constraints; (2) changing the nonlinear constraints to linear forms suitable for scenario‐based optimization; and (3) considering the stochastic behavior of electric loads without requiring any data about their internal process in each factory.

Demand‐side management in conjunction with plug‐in electric vehicles, along with renewable energy sources, has been explored in relation to dynamic optimal power flow (DOPF). The renewable energy sources considered here are solar PV plants (SPVPs) along with hydropower plants (HPPs). All of the bottlenose dolphin optimizer (BDO), gray wolf optimization (GWO), and the self‐organizing Hierarchical particle swarm optimizer with time‐varying acceleration coefficients (HPSO‐TVAC) are the techniques utilized to solve DOPF. 57‐bus, 118‐bus, as well as IEEE 30‐bus systems are employed for authentication. The cost acquired with DSM is about 4.35%, 12.98%, and 5.25% less than the cost obtained without DSM in the case of the IEEE 30‐bus, 57‐bus, and 118‐bus systems, respectively. The cost derived by BDO is less than that of the other two methods. Demand‐side management in conjunction with plug‐in electric vehicles along renewable energy sources has been explored in relation to DOPF (dynamic optimal power flow). The renewable energy sources considered here are SPVPs (solar PV plants) along with HPPs (hydropower plants). BDO (Bottlenose Dolphin Optimizer), GWO (Gray Wolf Optimization), and the HPSO‐TVAC (self‐organizing Hierarchical particle swarm optimizer with time‐varying acceleration coefficients) are the techniques utilized to solve DOPF. 57‐bus, 118‐bus, as well as IEEE 30‐bus systems are employed for authentication. The cost acquired with DSM is about 4.35%, 12.98%, and 5.25% less than the cost obtained without DSM in the case of IEEE 30‐bus, 57‐bus, and 118‐bus systems correspondingly. The cost derived by BDO is less than those of the other two methods.

Fast charging is a promising way for plug-in electric vehicles (PEVs) to get recharged quickly and reduce the impacts of long-lasting charging process on PEV owners’ daily life. Decreasing time during charging PEVs also makes the decision of PEV owners choosing where to charge affected more by the time length of driving towards and waiting in charging stations, raising new requirements for charging facilities planning. In this study, a cost minimisation planning method of PEV fast-charging stations taking influences of queuing and driving time into consideration is proposed and solved by the genetic algorithm-based methodology. An iterative algorithm obtaining the equilibrium of the user's decision of place to charge is proposed to consider the impacts of waiting and driving time at different charging stations on PEV owners. The effectiveness of the proposed strategy is then verified through the case analysis based on trajectory data of taxis in Beijing, which shows that the proposed methodology has good performances in computation. Weight of time costs and investment restrictions such as the number of charging stations would also influence the planning result.

This study proposes a novel multi-agent method for electric vehicle (EV) owners who will take part in the electricity market. Each EV is considered as an agent, and all the EVs have vehicle-to-grid capability. These agents aim to minimise the charging cost and to increase the privacy of EV owners due to omitting the aggregator role in the system. Each agent has two independent decision cores for buying and selling energy. These cores are developed based on a reinforcement learning (RL) algorithm, i.e. Q-learning algorithm, due to its high efficiency and appropriate performance in multi-agent methods. Based on the proposed method, agents can buy and sell energy with the cost minimisation goal, while they should always have enough energy for the trip, considering the uncertain behaviours of EV owners. Numeric simulations on an illustrative example with one agent and a testing system with 500 agents demonstrate the effectiveness of the proposed method.