Penetration Of Vehicles Research Articles

The impact of financial incentives on the total cost of ownership of electric light commercial vehicles in EU countries

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response

The high penetration of electric vehicles (EVs) poses both opportunities and challenges for power systems. EV retailers, playing a critical role in the demand response mechanism, face market risks caused by uncertainties in electricity consumption and prices. To address the operational issues of large-scale EVs and tap the potential of EV retailers, a temporal and spatial domain-based optimization strategy is proposed, which is implemented on bi-level (referring to transmission and distribution grid networks). First, a physical scheduling model of the grid is established, consisting of a novel hybrid demand response mechanism considering the incentives of EV retailers and the retail electricity price of EV users, and a new robust retail electricity pricing strategy handling the uncertainties of EV behavior and the electric market. Then, a bi-level optimization strategy is presented: at the upper level, in the transmission network, based on the robust pricing strategy, a unit commitment model that coordinates the hybrid demand response with other distributed energy resources is designed to optimize load periods of EVs in the time domain; at the lower level, in the distribution network, an optimal power flow model is proposed to spatially dispatch the location of EV loads. The impacts of retail price profile, EV penetration, hybrid demand response mechanism, and EV load location are analyzed in ten tests using the IEEE 33 distribution network. Simulation results indicate that the robust pricing strategy can effectively handle uncertainties, the integration of the hybrid demand response mechanism into scheduling can ensure the benefits of all participants, and the bi-level optimization strategy can accommodate distributed energy resources temporally and spatially.

Assessment of the carbon emissions reduction potential in Thailand’s power sector with high penetration of variable renewable energy sources and electric vehicles in the year 2030

Abstract The power sector has substantial carbon emissions reduction potential that could achieve the new nationally determined contribution target in 2030 by increasing low-carbon technologies, e.g. variable renewable energy sources and electric vehicles (EVs). Therefore, two approaches were suggested in this work. In the first approach, Thailand’s power sector was modelled by using PLEXOS software to find the impact of the high penetration of variable renewable energy (RE) and EV charging load for the projected year 2030. The second approach proposed a demand–response and energy storage system solution with carbon pricing in the model to assess the targets. As a result, the electricity demand from charging EVs will cause a new peak demand at night, while the high variable RE penetration will cause curtailment in the power system due to excess supply at noon. Therefore, Thailand’s power sector has the potential for carbon emissions reduction by 45% with clean energy technologies, which could increase to 68% with carbon pricing, easily achievable by the nationally determined contribution target in 2030. However, these benefits could only be derived if various entities involved in the energy regulatory, transport and power sectors coordinate to implement the required technological and financial policies.

EV Hosting Capacity Enhancement in a Community Microgrid Through Dynamic Price Optimization-Based Demand Response.

Community microgrids, as an emerging technology, offer resiliency in operation for smart grids. Microgrids are seeing an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can easily overwhelm such electric networks. In this work, we propose an efficient demand response (DR) scheme based on dynamic pricing to enhance the capacity of the microgrid to securely host a large number of EVs. A hierarchical two-level optimization framework is introduced to realize the DR scheme. At the upper level, the dynamic prices for the participating users in DR are optimized while at the lower level, each user optimizes its energy consumption based on the price signal from the upper level. An evolutionary algorithm and a mixed-integer linear programming model is employed to solve the upper and lower level problems, respectively. Energy scheduling problems of the users are solved in a distributed manner which adds to the scalability of the approach. The proposed DR scheme is tested on a microgrid system adopted from the IEEE European low-voltage distribution network. Numerical experiments confirm the effectiveness of the proposed DR scheme compared to the benchmark pricing policies from the literature.

Revolution on Two Wheelers: An In-depth Analysis of the Challenges Facing the Electric Scooter Industry

Overall Electric vehicle penetration in India, including two-wheelers, is projected to reach 3.2% by the end of CY2022.Two-wheeler riders in India encounter a number of obstacles, including range anxiety, inadequate infrastructure, a slow battery recharge, an absence of a rapid service network, and a subpar construction. This study collected the data of 100 respondents through questionnaire which is related how traditional and electric scooter buyers' preferences are shifting over time and also studied various challenges faced by electric scooter buyers. To resolve these problems, the market for electric scooters must prioritize improving battery life. The government must to make the most of this chance and get to work on finding solutions to the genuine issues that are brought up by the widespread use of electric scooters. Because of the limited size of the sample, it is not possible to extrapolate the results to the full population because there is a high probability that the findings will change if the sample size is raised.

Necessity of artificial intelligence techniques for power quality issues in electric vehicles

<p>World transportation is in an electrified mass transitional phase due to the predominant features such as a greener environment, fewer maintenance requirements, improved reliability and energy management efficiency compared to conventional transportation. Numerous difficulties have emerged since electric vehicle sales hit record highs, including how electrified transportation works with the grid. Emerging innovations and technological advances in the field of artificial intelligence have led to the development of enhanced controllers for electric vehicle penetration with the grid. This work includes information on the integration of artificial intelligence strategies used for the mitigation of power quality issues. By merging artificial intelligence techniques into the grid, users can achieve efficient energy use and beneficial power interactions between the grid and electric vehicles. To assess the network's practicality, this research develops a charging model that uses a Tasmanian Devil optimization (TDO) algorithm based on MATLAB software. According to research, TDO improves the waveform quality by reducing the THD with better performance and achieves efficient power transfer between the grid and EVs.</p>

Dynamic impedance model based two‐stage customized charging–navigation strategy for electric vehicles

AbstractWith the continuously increasing penetration of electric vehicles (EVs), the mutual match between the distribution of charging resources and the spatial–temporal distribution of EV charging demands is becoming increasingly important. To address this, this paper proposes a novel two‐stage customized EV charging–navigation strategy. Building on previous research on the real‐time information from dynamic traffic networks, a personalized dynamic road impedance (PDRI) model is built to transform three main criteria (distance, time, and finance) affecting charging–navigation into comprehensive road impedance. In the first navigation stage, fast‐charging stations (FCSs) with the lowest overall objective are selected. In the second navigation stage, an improved Floyd–Warshall algorithm is utilized to identify the routes with the lowest personalized weight to the selected FCS in the PDRI model. Notably, the personalized preferences of EV drivers for the three primary criteria are considered in both stages of the navigation process. Finally, simulation results demonstrate a significant improvement in the degree of matching between charging navigation plans and drivers' personalized requirements, and a more balanced spatial–temporal distribution of EV charging demands among FCSs, which verifies the effectiveness of the proposed strategy.

Optimal production cooperation strategies for automakers considering different sales channels under dual credit policy

The dual credit policy has significantly facilitated the market penetration of new energy vehicles. However, the best strategy for production collaboration, particularly when considering diverse sales channels, remains underexplored. In this study, a duopolistic market scenario is examined where a new energy vehicle automaker sells directly to end-users and a fuel vehicle automaker resells to consumers through a car dealer. We developed a game-theoretical model to analyze optimal decisions under three distinct strategies: pure competition (pc), commission sales (cs), and mixed production (mp). The findings are as follows: (1) The new energy vehicle credits rate and fuel consumption standards exhibit similar effects. While impacting the range effort levels minimally, these standards notably reduce the fuel-saving rate of fuel vehicles, questioning the efficiency of restrictive policies. (2) With the rise in credit trading prices, there is an increase in equilibrium decisions and profits for both the pc and mp strategies, suggesting that higher credit prices incentivize automakers to optimize their strategies, leading to better results. (3) The strategic choice of the automakers is heavily influenced by the credit trading price. The energy vehicle automaker tends to prefer the pc strategy, while the fuel vehicle automaker's preference alternates between mp and cs, depending on market conditions.

Coordinated pricing mechanism for parking clusters considering interval-guided uncertainty-aware strategies

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points.

Can financial subsidy increase electric vehicle (EV) penetration---evidence from a quasi-natural experiment

Electric vehicles (EVs) are considered a promising solution for reducing emissions in urban transportation and addressing energy crises. Several countries, including China, have implemented direct financial subsidies to encourage the adoption of EVs. However, there is a lack of comprehensive evaluation regarding the effectiveness of these subsidies and their interaction with non-financial incentive policies. Additionally, the design of subsidy policies can be endogenous, potentially leading to biased estimations of their effects on EV penetration. To address these issues, this study utilizes a “quasi-natural experiment" approach, taking advantage of the gradual reduction of nationwide EV subsidies by the Chinese government due to financial constraints, rather than market conditions, thus mitigating the endogeneity problem. The study collects panel data from 224 prefecture-level cities in China and employs a modified panel data fixed effects model to quantify the impact of financial subsidies on EV sales and market shares. The analysis focuses on subsidy variations during six periods. The main empirical findings are as follows: (1) 1 % increase in purchase subsidies leads to a 1.36 % increase in EV sales and a 2.31 % increase in EV market share. (2) Non-financial policies, such as parking benefits, promotional goals, and restrictions on the number of vehicles, also contribute to boosting EV sales. (3) The effects of purchase subsidies vary across cities with different socio-economic conditions. (4) Some non-financial policies are found to enhance the positive effects of subsidy policies.

Collaborative road pricing strategy for heterogeneous vehicles considering emission constraints

Urban transportation problems, primarily characterized by traffic congestion and exhaust emissions, continue to worsen, significantly impeding the economic and social development of cities. Road pricing strategies have emerged as an effective approach to tackle urban transportation problems, and their successful implementation in numerous cities have garnered significant attention. In this context, this study introduces a collaborative road pricing strategy tailored for mixed traffic flow comprising both conventional vehicles (CVs) and new energy vehicles (NEVs). CVs and NEVs are considered as two distinct types of cars with heterogeneous characteristics. The collaborative road pricing strategy consists of two components: congestion pricing and emission pricing. A bi-level programming model for the collaborative road pricing strategy is built. Exhaust emissions within the low-emission zone (LEZ) are used as constraints for the model, while the optimization objective of the model involves minimizing the sum of total travel time and total emissions of the road network. The viability of the proposed collaborative road pricing scheme is demonstrated through numerical tests within the second ring road in Beijing, China. The results imply that the service level of the road network can be improved by implementing the collaborative road pricing scheme. It also concludes that, increasing the penetration of new energy vehicles would help to decrease traffic congestion and emissions.

Impact of electric vechicles on power transmission grids

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by ▪ for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

EXPLORING CHARGING-DISPATCH APPROACHES AND VEHICLE-TO-GRID TECHNOLOGIES FOR ELECTRIC VEHICLES IN DISTRIBUTION NETWORKS

Various electric vehicle charging and discharging strategies (EVs) and V2G technologies are discussed in this article as their impacts on energy distribution networks. The V2G application that can be used on vehicles offers many benefits, as demonstrated. Features such as active power regulation, reactive power support, load balancing, and current harmonic filtering are incorporated into this technology.Although V2G technology has many benefits, there are also several challenges. These challenges include reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure. The article briefly discusses the effects of electric vehicle penetration levels,charging profiles, and various other aspects of controlled charging and discharging from a performance perspective. This includes over loading, deteriorating power quality, and power loss. A comprehensive analysis of controlled and uncontrolled charging–discharging methods, delayed charging discharging methods, indirect controlled discharging methods, bidirectional charging–discharging methods, and intelligent scheduling is presented in this study. Several challenges and issues regarding electric vehicle applications are discussed from an aggregator’s perspective. Analysis shows that Li-ion batteries can be recharged 2000–4000 times, and a mass-produced Li-ion battery costs $200–$500 per kWh.Degradation costs of batteries at 80% discharge depth are estimated to be $130 per MWh at 300 kWh investment cost. 10% of peak capacity could come from PEVs in the 20% range. Around 87.5% of PEVs are properly charged. Key Words: Electric vehicles(EV),penetration,overloading, Communication,Deteriorating,Degradation.

MSND: Modified Standard Normal Deviate Incident Detection Algorithm for Connected Autonomous and Human-Driven Vehicles in Mixed Traffic

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction.

Driving Behaivor Optimization Using Genetic Algorithm and Analysis of Traffic Safety for Non-Autonomous Vehicles by Autonomous Vehicle Penetration Rate

Driving Behaivor Optimization Using Genetic Algorithm and Analysis of Traffic Safety for Non-Autonomous Vehicles by Autonomous Vehicle Penetration Rate

Strategic planning of distribution network integrated with EV charging stations using fuzzy pareto optimality for performance improvement and grid-side emission reduction benefits

The increasing penetration of electric vehicles (EVs) in distribution systems requires efficient charging load management techniques for delivering high-quality power to consumers. In this paper, we proposed a strategic planning approach for distribution systems integrated with EV charging stations, employing fuzzy Pareto optimality to enhance system performance and minimize carbon emissions. Our approach involves a fuzzy Pareto heuristic network reconfiguration to minimize real power loss and improve voltage profiles. Additionally, Time of Use (ToU) pricing-based EV charging load scheduling is developed to minimize the annual energy cost. These objectives aim to improve minimum node voltage, reduce power losses, and maintain branch current constraints. To simulate the impact of EV charging loads, we aggregate residential, commercial, and industrial loads, along with EV charging loads at charging stations based on customer choice time and ToU pricing methods. The proposed reconfiguration approach is tested on 33 and 69-bus distribution systems with integrated EV charging stations, distributed generations (DGs), and shunt capacitors (SCs). The simulation results demonstrate the advantages of the reconfiguration algorithm in enhancing the performance of the distribution network in the presence of EV loads. Furthermore, we compare ToU pricing-based EV charging time zones with customer comfort-based EV charging, showcasing the advantages of the former in reducing emissions through real power loss reduction. Overall, our strategic planning approach not only improves distribution system performance but also offers economic benefits while minimizing carbon dioxide emissions.

Towards resilient energy communities: Evaluating the impact of economic and technical optimization

This paper assesses the difference of optimal operation of energy communities (ECs) with respect to economic and technical goals. ECs have emerged as a promising solution for accelerating the transition to more sustainable energy systems and therefore climate change mitigation. While cost optimization (economic goal) is most commonly used in ECs, optimizing their resilience (technical goal) can be an important part of operating a distribution grid with high photovoltaic (PV) and electrical vehicle (EV) penetration in the future. This paper presents a comparative analysis of the impact of those two objective functions on overall EC costs as well as individual member costs. The findings highlight the trade-off between the flexibility measures required for a resilient EC and the cost associated with them. This study helps quantify the subsidies that would be required to incentives EC to operate in a resilient matter as a form of grid service.

The 2022 report of synergetic roadmap on carbon neutrality and clean air for China: Accelerating transition in key sectors

China is now confronting the intertwined challenges of air pollution and climate change. Given the high synergies between air pollution abatement and climate change mitigation, the Chinese government is actively promoting synergetic control of these two issues. The Synergetic Roadmap project was launched in 2021 to track and analyze the progress of synergetic control in China by developing and monitoring key indicators. The Synergetic Roadmap 2022 report is the first annual update, featuring 20 indicators across five aspects: synergetic governance system and practices, progress in structural transition, air pollution and associated weather-climate interactions, sources, sinks, and mitigation pathway of atmospheric composition, and health impacts and benefits of coordinated control. Compared to the comprehensive review presented in the 2021 report, the Synergetic Roadmap 2022 report places particular emphasis on progress in 2021 with highlights on actions in key sectors and the relevant milestones. These milestones include the proportion of non-fossil power generation capacity surpassing coal-fired capacity for the first time, a decline in the production of crude steel and cement after years of growth, and the surging penetration of electric vehicles. Additionally, in 2022, China issued the first national policy that synergizes abatements of pollution and carbon emissions, marking a new era for China's pollution-carbon co-control. These changes highlight China's efforts to reshape its energy, economic, and transportation structures to meet the demand for synergetic control and sustainable development. Consequently, the country has witnessed a slowdown in carbon emission growth, improved air quality, and increased health benefits in recent years.

Assessing the cost-effectiveness of carbon neutrality for light-duty vehicle sector in China

China's progress in decarbonizing its transportation, particularly vehicle electrification, is notable. However, the economically effective pathways are underexplored. To find out how much cost is necessary for carbon neutrality for the light-duty vehicle (LDV) sector, this study examines twenty decarbonization pathways, combining the New Energy and Oil Consumption Credit model and the China-Fleet model. We find that the 2060 zero-greenhouse gas (GHG) emission goal for LDVs is achievable via electrification if the battery pack cost is under CNY483/kWh by 2050. However, an extra of CNY8.86 trillion internal subsidies is needed under pessimistic battery cost scenarios (CNY759/kWh in 2050) to eliminate 246 million tonnes of CO2-eq by 2050 ensuring over 80% market penetration of battery electric vehicles (BEVs) in 2050. Moreover, the promotion of fuel cell electric vehicles is synergy with BEVs to mitigate the carbon abatement difficulties, decreasing up to 34% of the maximum marginal abatement internal investment.

A Study On Electric Vehicles Charging System Utilizing With Solar Energy

Electric vehicles (EVs) are a viable solution for lowering greenhouse gas emissions. EVs not only reduce fossil fuel reliance, But they minimize ozone depleting compounds and facilitate large-scale renewable deployment. Despite extensive study on the qualities and properties of electric cars, as well as the nature of their charging infrastructure, electric vehicle manufacturing and network modeling continue to change and be restricted. This article addresses the numerous modeling approaches and optimization strategies used in studies of Electric Vehicle, Hybrid Electric Vehicle, Plug-in-Hybrid Electric Vehicle, and Battery Electric Vehicle penetration rates in the market