Modeling Us Light Duty Demand For Ev Charging Infrastructure In 2030

With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 50% of light-duty sales are electric (including plug-in hybrids), resulting in an on-road stock of 33 million vehicles. We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a cumulative capital investment of $82 billion in public and private charging infrastructure will be necessary in our baseline scenario (approximately 3x greater than our estimate of planned investments to date). This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.

With the support of DOE's Vehicle Technologies Office and the DOE/DOT Joint Office (JO), NREL has applied the EVI-X modeling suite to conduct a National Electric Vehicle Infrastructure Needs Assessment. This report considers a 2030 scenario in which 50% of light-duty sales are electric (including plug-in hybrids), resulting in an on-road stock of 33 million vehicles. We consider the needs of vehicles used for typical daily driving, drivers without access to residential charging, corridor charging supporting long-distance travel, and ride-hailing electrification. We find that a cumulative capital investment of $82 billion in public and private charging infrastructure will be necessary in our baseline scenario (approximately 3x greater than our estimate of planned investments to date). This result is framed as a conservative estimate as the assumed costs include charging equipment and installation but exclude the cost of grid upgrades and distributed energy resources.

Ambitious federal clean goals, along with historic investment in American manufacturing, have put the United States on track to see 30-42 million light-duty electric vehicles (EVs) on the road by 2030. Now, a groundbreaking study from the National Renewable Energy Laboratory (NREL) has estimated the EV charging infrastructure needed nationwide to support a sweeping transition to electrified transportation. The study, titled "The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure," estimates the number, type, and location of the chargers needed to create a comprehensive network of EV charging infrastructure. Its use of proprietary NREL software tools and sophisticated analysis have resulted in a nationwide infrastructure needs assessment with a never-before-seen level of detail - one that takes into account the different ways Americans travel, from running errands to taking road trips, and can adjust to changing circumstances as EV adoption rates change over time.

For a century, almost all light-duty vehicles (LDVs) have been powered by internal combustion engines operating on petroleum fuels. Energy security concerns about petroleum imports and the effect of greenhouse gas (GHG) emissions on global climate are driving interest in alternatives. Transitions to Alternative Vehicles and Fuels assesses the potential for reducing petroleum consumption and GHG emissions by 80 percent across the U.S. LDV fleet by 2050, relative to 2005. This report examines the current capability and estimated future performance and costs for each vehicle type and non-petroleum-based fuel technology as options that could significantly contribute to these goals. By analyzing scenarios that combine various fuel and vehicle pathways, the report also identifies barriers to implementation of these technologies and suggests policies to achieve the desired reductions. Several scenarios are promising, but strong, and effective policies such as research and development, subsidies, energy taxes, or regulations will be necessary to overcome barriers, such as cost and consumer choice.

Front Cover -- About Island Press -- Subscribe -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgments -- 1. Will the Transportation Revolutions Improve Our Lives-- or Make Them Worse? -- 2. Electric Vehicles: Approaching the Tipping Point -- 3. Shared Mobility: The Potential of Ridehailing and Pooling -- 4. Vehicle Automation: Our Best Shot at a Transportation Do-Over? -- 5. Upgrading Transit for the Twenty-First Century -- 6. Bridging the Gap between Mobility Haves and Have-Nots -- 7. Remaking the Auto Industry -- 8. The Dark Horse: Will China Win the Electric, Automated, Shared Mobility Race? -- Epilogue -- Notes -- About the Contributors -- Index -- IP Board of Directors

Electric vehicles (EVs) are becoming a more prominent portion of Texas’s light duty vehicle (LDV) fleet as they become more attractive to consumers and as relevant governing bodies work to incentivize further adoption rates in an effort to reduce emissions within the transportation industry. Meanwhile, the Electric Reliability Council of Texas (ERCOT), the electric grid that services about 90% of the state’s residents, has been seeing increases in power demand. This has been due to factors such as a growing population, increased air conditioning use, and pushes for electrification across other industries, all factors that are expected to continue contributing to power demand increases within the foreseeable future. As more vehicles in the state are electrified, they will add further power demand increases on top of the existing contributing factors. This work focuses on evaluating different EV adoption, charging management, and policy scenarios in order to then evaluate how they may be expected to impact ERCOT, particularly regarding peak demand increase within two time horizons: one into 2030 and one into 2050. Peak demand is an important consideration because as it increases, it presents challenges for maintaining the electrical grid’s reliability when it exceeds generation capacity. After constructing and refining models which predict EV presence at the county level based on socio-economic and infrastructure related feature variables, the anticipated impacts of a growing EV fleet are quantified using historical data from ERCOT, planned installations and reserve margins, EV charging patterns, and travel patterns. Additionally, this work includes results from a collaboration which culminated in applying relevant EV fleet growth predictions to a DC-OPF simulation for Austin Energy, serving as a case study for the future of EV fleet impacts on relatively small-scale utilities. The results of this study showcase the fact that it is possible to accurately predict EV presence at the county level with 6 publicly available feature variables. In examining adoption pathways, it importantly finds that current incentives will very likely be insufficient for the achievement of the Biden Administration’s 50% market share goal by 2030. Should this market share goal come to fruition, however, it is expected to be manageable at the state level and within the Austin Energy case study regarding electricity supply in 2030. Sustained growth from this scenario, however, will necessitate ambitious charging management strategies in order to limit the potentially heavy impact of a growing EV fleet on peak demand looking forward into and beyond 2050

The transportation sector is a largest emitter of greenhouse gases in the U.S., accounting for 28.6% of all 2016 emissions, the majority of which come from the passenger vehicle fleet [1,2]. One major technology that is being investigated by researchers, planners, and policy makers to help lower the emissions from the transportation sector is the plug-in electric vehicle (PEV). The focus of this work is to investigate and model the impacts of increased levels of PEVs on the regional electric power grid and on the net change in CO2 emissions due to the decrease tailpipe emissions and the increase in electricity generation under current emissions caps. The study scope includes all of New England and New York state, modeled as one system of electricity supply and demand, which includes the estimated 2030 baseline demand and the current generation capacity plus increased renewable capacity to meet state Renewable Portfolio Standard targets for 2030. The models presented here include fully electric vehicles and plug-in hybrids, public charging infrastructure scenarios, hourly charging demand, solar and wind generation and capacity factors, and real-world travel derived from the 2016-2017 National Household Travel Survey. We make certain assumptions, informed by the literature, with the goal of creating a modeling methodology to improve the estimation of hourly PEV charging demand for input into regional electric sector dispatch models. The methodology included novel stochastic processes, considered seasonal and weekday versus weekend differences in travel, and did not force the PEV battery state-of-charge to be full at any specific time of day. The results support the need for public charging infrastructure, specifically at workplaces, with the "work" infrastructure scenario shifting more of the unmanaged charging demand to daylight hours when solar generation could be utilized. Workplace charging accounted for 40% of all non-home charging demand in the scenario where charging infrastructure was "universally" available. Under the increased renewable fuel portfolio, the reduction in average CO2 emissions ranged from 90 to 92% for the vehicles converted from ICEV to PEV. The total emissions reduced for 15% PEV penetration and universally available charging infrastructure was 5.85 million metric tons, 5.27% of system-wide emissions. The results support the premise of plug-in electric vehicles being an important strategy for the reduction of CO2 emissions in our study region. Future investigation into the extent of reductions possible with both the optimization of charging schedules through pricing or other mechanisms and the modeling of grid level energy storage is warranted. Additional model development should include a sensitivity analysis of the PEV charging demand model parameters, and better data on the charging behavior of PEV owners as they continue to penetrate the market at higher rates.

Electric vehicles (EVs) are widely considered a sustainable substitution to conventional vehicles to mitigate fossil fuel dependence and reduce tail-pipe emissions. However, limited ranges, long charging times, and lack of charging infrastructure have hindered EV's market acceptance. This calls for more investments in building charging stations and advancing battery and charging technologies to obviate issues associated with EVs and increase their market share and improve sustainability. This study introduces modeling frameworks to optimize fast-charging infrastructure locations at the network level to address the challenges associated with EVs. Furthermore, it investigates the required charging investments for the current and future EV market shares, technology advancements, and seasonal demand variations. First, this study seeks an optimal configuration for plug-in electric vehicle charging infrastructure that supports their long-distance intercity trips at the network level. A mathematical optimization model is proposed which minimizes the total system cost and considers the range anxiety, multiple refueling, maximum capacity, charging delay, and detour time. This study considers the impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles considering a user equilibrium framework. This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations in intercity networks. Then, the study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations, as well as different battery sizes and charger technologies, the main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. This study incorporates the developed intercity model to suggest the optimal locations of EV fast chargers to be implemented in Michigan.Next, this study introduces an integrated framework for urban fast-charging infrastructure to address the range anxiety issue in urban networks. Unlike intercity trips that start with fully charged batteries, urban trips might start with any state of charge because of home/work chargers' unavailability, being part of a trip chain, and forgetting to charge overnight. A mesoscopic simulation tool is incorporated to generate trip trajectories, and a state-of-the-art tool is developed to simulate charging behavior based on various trip attributes for these trajectories. The resulting temporal charging demand is the key element in finding the optimum charging infrastructure. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Finally, this study generates forecasting models to estimate the number of chargers and charging stations to support the EV charging demand for urban areas. These models provide macro-level estimates of the required infrastructure investment in urban areas, which can be easily implemented by policy-makers and city planners. This study incorporates data obtained from applying a disaggregate optimization-based charger placement model, for multiple case studies to generate the required data to calibrate the macro-level models, in the state of Michigan.

The global shift towards electric vehicles (EVs) holds immense promise for mitigating greenhouse gas (GHG) emissions and advancing sustainable development goals. Nonetheless, the limited market penetration of EVs persists, primarily due to challenges in meeting the demand for replenishment compared to conventional internal combustion engine vehicles (ICEVs). The overarching goal of this dissertation is to develop mathematical models and a management framework for EV drivers' travel behaviors in a demand-supply-coupled transportation system, with the ultimate aim of facilitating the widespread adoption of EVs. By gaining deeper insights into and effectively managing various aspects of EV driver behaviors, such as charging preferences and route choices, the following benefits can be achieved: meeting the charging demands of EV drivers, optimizing the utilization of charging facility supply, and promoting the adoption of EVs as a preferred mode of travel. Firstly, the charging behavior of EV drivers is modeled based on the given charging facility supply. The existing research efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To fill the gap, an ensemble learning model based on gradient boosting is developed. A total of 18 features are defined and extracted from the multisource data, which cover information on drivers, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5 years of charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with an R square value of 0.54 and root mean square error of 0.14, both better than the two benchmark models, the multiple linear regression model and the random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users, or that are further from residential areas. Secondly, the provision of charging infrastructure is formulated as a demand management mechanism accounting for the underlying demand-supply coupled relationship. The existing studies treat each charging station as an independent entity and naively select the candidate locations with the highest individual usage rates. To address this issue, a two-stage learning-based demand-supply-coupled optimization model for the charging station location problem (CSLP) is proposed, aiming to incorporate the concept of EV charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station (CS) based on 15 defined features. Next, in stage two, a demand-supply-coupled CSLP model is developed with the objective of maximizing the total charging usage rates of both existing and newly selected charging stations. The proposed model is solved using a gradient-based stochastic spatial search algorithm. A case study using the same data as the first chapter is performed to test the effectiveness of the proposed model and algorithm. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches, namely the Greedy-Based Method and Neighbor-Swap-Based Method. Lastly, the routing behavior, as another aspect of EV driver travel behaviors, is modeled in a community charging setting. The existing research focuses on the EV traffic assignment under the scenario of corridor charging in a small-scale road network, ignoring the link interactions in community charging and path deviations in large-scale road networks. To tackle these challenges, an EV traffic assignment model for large-scale road networks with link interaction in community charging and with path deviations is proposed. First, the mathematical formulation for the EV traffic assignment model considering the interaction among road links connecting to the same CS is proposed, which is further proven to be equivalent to the user equilibrium (UE) condition. Then, a column-generation-based solution algorithm is developed to solve the model, facilitating the complex EV path deviations in a large-scale road network. The result of numerical examples shows that the proposed algorithm could converge in 0.025, 1.71, 4.73 and 91 seconds with a relative gap of no more than 0.0008 on the four testing networks, being the most accurate and fastest compared with the three benchmark algorithms, Frank-Wolfe algorithm, Interaction-Ignored algorithm, and Commercial-Solver-Based algorithm. The sensitivity analysis results show that the total travel cost and the total system dwelling time exhibit a negative correlation with charging supply while displaying a positive correlation with charging demand.

The increase in air pollution and vehicular emissions has led to the development of the renewable energy-based generation and electrification of transportation. Further, the electrification shift faces an enormous challenge due to limited driving range, long charging time, and high initial cost of deployment. Firstly, there has been a discussion on renewable energy such as how wind power and solar power can be generated by wind turbines and photovoltaics, respectively, while these are intermittent in nature. The combination of these renewable energy resources with available power generation system will make electric vehicle (EV) charging sustainable and viable after the payback period. Recently, there has also been a significant discussion focused on various EV charging types and the level of power for charging to minimize the charging time. By focusing on both sustainable and renewable energy, as well as charging infrastructures and technologies, the future for EV can be explored. Developing Charging Infrastructure and Technologies for Electric Vehicles reviews and discusses the state of the art in electric vehicle charging technologies, their applications, economic, environmental, and social impact, and integration with renewable energy. This book captures the state of the art in electric vehicle charging infrastructure deployment, their applications, architectures, and relevant technologies. In addition, this book identifies potential research directions and technologies that facilitate insights on EV charging in various charging places such as smart home charging, parking EV charging, and charging stations. This book will be essential for power system architects, mechanics, electrical engineers, practitioners, developers, practitioners, researchers, academicians, and students interested in the problems and solutions to the state-of-the-art status of electric vehicles.