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Author: Yang Song Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Yang Song Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Ti Zhang Publisher: ISBN: Category : Languages : en Pages : 402
Book Description
The adoption of plug-in electric vehicles (PEV) requires research for models and algorithms tracing the vehicle assignment incorporating PEVs in the transportation network so that the traffic pattern can be more precisely and accurately predicted. To attain this goal, this dissertation is concerned with developing new formulations for modeling travelling behavior of electric vehicle drivers in a mixed flow traffic network environment. Much of the work in this dissertation is motivated by the special features of PEVs (such as range limitation, requirement of long electricity-recharging time, etc.), and the lack of tools of understanding PEV drivers traveling behavior and learning the impacts of charging infrastructure supply and policy on the network traffic pattern. The essential issues addressed in this dissertation are: (1) modeling the spatial choice behavior of electric vehicle drivers and analyzing the impacts from electricity-charging speed and price; (2) modeling the temporal and spatial choices behavior of electric vehicle drivers and analyzing the impacts of electric vehicle range and penetration rate; (3) and designing the optimal charging infrastructure investments and policy in the perspective of revenue management. Stochastic traffic assignment that can take into account for charging cost and charging time is first examined. Further, a quasi-dynamic stochastic user equilibrium model for combined choices of departure time, duration of stay and route, which integrates the nested-Logit discrete choice model, is formulated as a variational inequality problem. An extension from this equilibrium model is the network design model to determine an optimal charging infrastructure capacity and pricing. The objective is to maximize revenue subject to equilibrium constraints that explicitly consider the electric vehicle drivers' combined choices behavior. The proposed models and algorithms are tested on small to middle size transportation networks. Extensive numerical experiments are conducted to assess the performance of the models. The research results contain the author's initiative insights of network equilibrium models accounting for PEVs impacted by different scenarios of charging infrastructure supply, electric vehicles characteristics and penetration rates. The analytical tools developed in this dissertation, and the resulting insights obtained should offer an important first step to areas of travel demand modeling and policy making incorporating PEVs.
Author: Wenzhong Shi Publisher: Springer Nature ISBN: 9811589836 Category : Social Science Languages : en Pages : 941
Book Description
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Author: Nam P. Suh Publisher: Springer ISBN: 3319511831 Category : Technology & Engineering Languages : en Pages : 408
Book Description
This book details the design and technology of the on-line electric vehicle (OLEV) system and its enabling wireless power-transfer technology, the “shaped magnetic field in resonance” (SMFIR). The text shows how OLEV systems can achieve their three linked important goals: reduction of CO2 produced by ground transportation; improved energy efficiency of ground transportation; and contribution to the amelioration or prevention of climate change and global warming. SMFIR provides power to the OLEV by wireless transmission from underground cables using an alternating magnetic field and the reader learns how this is done. This cable network will in future be part of any local smart grid for energy supply and use thereby exploiting local and renewable energy generation to further its aims. In addition to the technical details involved with design and realization of a fleet of vehicles combined with extensive subsurface charging infrastructure, practical issues such as those involved with pedestrian safety are considered. Furthermore, the benefits of reductions in harmful emissions without recourse to large banks of batteries are made apparent. Importantly, the use of Professor Suh’s axiomatic design paradigm enables such a complicated transportation system to be developed at reasonable cost and delivered on time. The book covers both the detailed design and the relevant systems-engineering knowledge and draws on experience gained in the successful implementation of OLEV systems in four Korean cities. The introduction to axiomatic design and the in-depth discussion of system and technology development provided by The On-line Electric Vehicle is instructive to graduate students in electrical, mechanical and transportation engineering and will help engineers and designers to master the efficient, timely and to-cost implementation of large-scale networked systems. Managers responsible for the running of large transportation infrastructure projects and concerned with technology management more generally will also find much to interest them in this book.
Author: Matthew David Dean Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The climate crisis requires substantial shifts in the transportation and energy sectors. Greater use of intermittent renewable energy sources requires demand- and supply-side flexibility in electricity markets. Deployment of on-demand, shared, fully automated, and electric vehicle (SAEV) fleets offers natural synergies in meeting such challenges. Smart charging (and discharging) of electric vehicles (EVs) can shift loads away from peak demand to reduce, or at least delay, expensive infrastructure upgrades, while fleet managers lower emissions and power costs in real time. This dissertation explores (1) optimization-based idle-vehicle dispatch strategies to improve SAEV fleet operations in the Austin metro, (2) integration of power and transportation system (EV-use) modeling across the Chicago metro area, and (3) a case study of demand response participation and charging station siting in a region with multiple energy suppliers. Optimizing SAEV repositioning and charging dispatch strategies jointly lowered rider wait times by 39%, on average, and increased daily trips served per SAEV by 28% (up to 6.4 additional riders), compared to separate range-agnostic repositioning and heuristic charging strategies. Joint strategies may also decrease the SAEV fleet’s empty travel by 5.7 to 12.8 percentage points (depending on geofencing and charging station density). If fleets pay dynamic electricity prices and wish to internalize their upstream charging emissions damages, a new multi-stage charging problem is required. A day-ahead energy transaction problem provides targets for a within-day idle-vehicle dispatch strategy that balances charging, discharging, repositioning, and maintenance decisions. This strategy allowed the Austin SAEV fleet to lower daily power costs (by 15.5% or $0.79/day/SAEV, on average) while reducing health damages from generation-related pollution (2.8% or $0.43/day/SAEV, on average). Fleet managers obtained higher profits ($8 per SAEV per day) by serving more passengers per day than with simpler (price-agnostic) dispatch strategies. This dissertation also coupled an agent-based travel demand simulator (POLARIS) with an electricity grid model (A-LEAF) to evaluate charging impacts on the power grid across seasons, household-EV adoption levels, SAEV mode shares, and dynamic ride-sharing assumptions in 2035 for the Chicago, Illinois metro. At relatively low EV penetration levels (8% to 17%), an increase in electricity demand will require at most 1 GW of additional generation capacity. Illinois’ transition to intermittent variable renewable energy (VRE) and phase-out of coal-fired power plants will likely not noticeably increase wholesale power prices, even with unmanaged personal EV charging at peak hours. However, wholesale power prices will increase during peak winter hours (by +$100/MWh, or $0.10/kWh) and peak summer hours (+$300/MWh) due to higher energy fees and steep congestion fees on Illinois’ 2015-era transmission system. Although a smart-charging SAEV fleet uses wholesale prices to reduce electricity demand during peak hours, spreading charging demand in hours before and after the baseline peak creates new "ridges" in energy demand, which raise prices for all. These simulation results underscore the importance of investing in transmission system expansion and reducing barriers to upgrading or building new transmission infrastructure. If vehicles and chargers support bidirectional charging, SAEVs can improve grid reliability and resilience at critical times through demand response (DR) programs that allow load curtailment and vehicle-to-grid (V2G) power. Scenario testing of DR requests in Austin ranging from 1 MW to 12 MW between 4 and 5 PM reveals break-even compensation costs (to SAEV owners) that range from $86/kW to $4,160/kW (if the city imposes unoccupied travel fees), depending on vehicle locations and battery levels at the time of the DR request. Smaller requests can be met without V2G by reducing charging speeds, usually from 120 kW speed to Level 2 charging. Finally, an incremental charging station heuristic was designed to capture differences in land costs and electricity rate structures from different energy suppliers in the same region. The daily amortized costs over 10 years of hardware, installation, and land costs were estimated to be nearly $0.30/SAEV/day, compared to $0.38/SAEV/day with a baseline heuristic strategy ignoring land costs and marginal costs of expanding existing sites. SAEV charging costs showed no substantial difference between heuristic strategies, although combined daily energy fees were more expensive at $0.43/SAEV/day. Including land costs in charging station investment heuristics is necessary, and modelers should include spatially varying energy prices since the average daily per-vehicle energy costs are higher than the physical station costs. Taken together, this dissertation’s contributions offer hope for a decarbonizing world that provides affordable, clean, and convenient on-demand mobility
Author: Kenan Degirmenci Publisher: World Scientific ISBN: 1800611439 Category : Business & Economics Languages : en Pages : 296
Book Description
The electrification of shared fleets offers numerous benefits, including the reduction of local emissions of pollutants, which leads to ecological improvements such as the improvement of air quality. Electric Vehicles in Shared Fleets considers a holistic concept for a socio-technical system with a focus on three core areas: integrated mobility solutions, business models for economic viability, and information systems that support decision-making for the successful implementation and operation of electric vehicles in shared fleets.In this book, we examine different aspects within these areas including multimodal mobility, grid integration of electric vehicles, shared autonomous electric vehicle services, relocation strategies in shared fleets, and the challenge of battery life of electric vehicles. Insights into the future of transport are provided, which is predicted to be shared, autonomous, and electric. This will require the expansion of the charging infrastructure to provide adequate premises for the electrification of transportation and to create market demand.
Author: Siobhan Jocelyn Larissa Powell Publisher: ISBN: Category : Languages : en Pages :
Book Description
The electricity grid and transportation sector are undergoing simultaneous, rapid, and unprecedented transformations to reduce emissions. Coupled through electric vehicle charging, the two transformations can both hinder and support each other: the grid must provide electric vehicle drivers with reliable, affordable electricity and convenient access to charging stations; electric vehicle charging can impact the grid's transformation in turn by increasing demand, accelerating equipment aging or forcing upgrades, aligning or misaligning with renewable generation, or even providing grid services. This dissertation focuses on that coupling, by understanding what shapes electric vehicle charging demand and how it should be reshaped to improve the impacts on the electricity grid. Studying drivers' charging behaviour is the first step toward understanding charging demand. Electric vehicle charging behaviour is highly heterogeneous, shaped by individuals' travel patterns, access to charging infrastructure, and personal preferences. For example, charging data reveals that some drivers are risk averse and prefer to top-up at every opportunity while others prefer less frequent, higher energy sessions. We propose a novel methodology to include driver behaviour in a model of large-scale electric vehicle charging demand for applications in long-term planning. The methodology builds in knobs for future scenario design based on data-driven modeling of driver behaviour, clustering drivers and charging sessions. We calibrate the methodology using a large data set of nearly four million charging sessions from Northern California in 2019. Charging control is a powerful tool widely used to modify charging profiles. Studying the connections between charging control, electricity rate design, and drivers' charging behaviour is the second step toward understanding charging demand. We first investigate controlled charging at a small scale, studying the impact of workplace charging control for different electricity rate schedules on the aging of a distribution transformer. Then, we propose a novel methodology for representing such control in large-scale models of charging demand. The proposed methodology uses machine learning to directly model the mapping from uncontrolled to controlled aggregate demand. Finally, we apply this understanding of how drivers' charging behaviour, charging control, and access to charging infrastructure shape and reshape demand to study the future large-scale impacts on the electricity grid. We focus on the Western US, and model grid dispatch in 2035 under a range of charging scenarios to evaluate the effects of increasing or decreasing the deployment of home or workplace charging infrastructure and of the widespread deployment of charging control in response to different electricity rate schedules. An important contribution of this dissertation is its emphasis on open-source, highly scalable tools. Long-term planning for electric vehicles requires scenario analysis of the range of possible futures, and faster simulation run times allow planners to test assumptions or interact with new scenarios in near real-time. More than anything, the results of this dissertation urge policymakers to consider the coupling of grid and electric vehicle planning. Careful electricity rate design and better build-out of away-from-home charging infrastructure could yield meaningful improvements for both sectors' transformations.
Author: Sina Baghali Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Electric vehicles (EVs) are one of the eminent alternatives to decarbonize the transportation sector. However, large-scale EV adoption brings new challenges and opportunities to both transportation and power systems (TPSs). The challenges include the lack of understanding of EV driving behaviors and the associated charging demand (CD) distribution, the complex interaction of the decentralized decision-makers from TPSs, and the insufficient infrastructure from TPSs to accommodate the growing CD of EVs. On the other hand, the opportunities include benefiting the power systems by leveraging vehicle-to-grid (V2G) technologies and improving transportation mobility by incorporating strategic infrastructure planning. The goal of this dissertation is to address the challenges and leverage opportunities associated with large-scale EV adoption from planning and operational perspectives in TPSs. We have the following objectives: 1. Better understanding the impacts of driving patterns on the spatio-temporal distribution of EV CD. 2. Investigate the value of EVs on the coupled TPSs. 3. Plan the supporting power and transportation infrastructure for the growing CD of EVs. More specifically, we first utilized machine learning approaches to model and forecast CD of EVs based on their driving behavior. Secondly, we propose a multi-agent model that captures the decentralized interactions between key stakeholders in TPSs to investigate the value of EVs in distribution system support. Thirdly, we modeled infrastructure planning for EV adoption from two perspectives: 1) We study the multi-stage DG and CS planning problem considering decentralized investors in a multi-agent optimization framework to understand the system evolvement. 2) We study the centralized CS planning problem in a bi-level programming framework to optimize transportation mobility by strategically placing CSs. To overcome the computational difficulties, we have proposed effective computational algorithms based on exact convex reformulation and value-decomposition algorithms. Our numerical examples demonstrate that the proposed models can identify the equilibrium investment patterns of DGs and CSs, as well as determine the optimal locations of CSs from a centralized entity's perspective. Additionally, our operational framework shows how EVs can provide system support for load pickup with endogenously determined incentives and energy prices. These modeling and computational strategies can provide foundations for future investigation, planning, and market design with large-scale EVs in coupled TPSs.
Author: Nan Jiang (Ph. D.) Publisher: ISBN: Category : Languages : en Pages : 342
Book Description
In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socio-economic system and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to change the current energy consumption structure and diminish greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply-demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environment and energy policy enactment. Driving PEVs inevitably changes individual's travel and activity behaviors and calls for fundamental changes to the existing transportation network and travel demand modeling paradigms to accommodate changing cost structures, technological restrictions, and supply infrastructures. A prominent phenomenon is that all PEV drivers face a distance constraint on their driving range, given the unsatisfactory battery-charging efficiency and scarce battery-charging infrastructures in a long period of the foreseeable future. Incorporating this distance constraint and the resulting behavioral changes into transportation network equilibrium and travel demand models (static and/or dynamic) raises a series of important research questions. This dissertation focuses on analyzing the impact of a massive adoption of BEVs on urban transportation network flows. BEVs are entirely dependent on electricity and cannot go further once the battery is depleted. As a modeling requirement in its simplest form, a distance constraint should be imposed when analyzing and modeling individual behaviors and network congestions. With adding this simple constraint, this research work conceptualizes, formulates and solves mathematical programming models for a set of new BEV-based network routing and equilibrium problems. It is anticipated that the developed models and methods can be extensively used in a systematic way to analyze and evaluate a variety of system planning and policy scenarios in decision-making circumstances of BEV-related technology adoption and infrastructure development.
Author: Harprinderjot Singh Publisher: ISBN: Category : Electronic dissertations Languages : en Pages : 0
Book Description
Autonomous vehicles (AVs) and electric vehicles (EVs) will improve safety, mobility, roadway capacity and provide efficient driving, efficient use of travel time, and reduced emissions. However, these technologies affect vehicle miles traveled (VMT), travel time, ownership cost, and electric grid network. Shared mobility systems can ameliorate the high price of these technologies. However, the shared mobility system poses additional problems such as users' waiting time, inconvenience, and increased VMT. Further, the impact of these emerging technologies varies on different groups of users (different values of travel time (VOTT). Another hurdle to the adoption of EVs is the limited range and scarcity of charging infrastructure. A well-established network of charging infrastructure, especially the direct current fast chargers (DCFC), can alleviate this challenge. However, the widespread adoption of EVs and the growing network of DCFC stations will increase the electric energy demand affecting the electric grid stability, demand-supply imbalance, overloading, and degradation of the electric grid components. Distributed energy resources (DER) such as solar panels and energy storage systems (ESS) can support the EV demand and reduce the load on the electric grid. This study develops modeling frameworks for the optimal adoption of AVs and EVs, considering their effect on transportation systems, the environment, and the electric grid network. Further, it suggests different scenarios that would promote the adoption of these technologies and provide a sustainable and resilient system. This study proposes a multi-objective mathematical model to estimate the optimal fleet configuration in a system of private manual-driven vehicles (PMVs), private AVs (PAVs), and shared AVs (SAVs) while minimizing the purchase and operating costs, time (travel and waiting time), and emission production. SAVs can be the optimal solution with the efficient use of travel time or the purchase price below a certain relative threshold. PAVs can be the optimal solution only if the onboard amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and adopted by people with high VOTT. The framework is extended to consider different combinations of EVs, AVs, and conventional human-driven vehicles in a private and shared mobility system. The metaheuristics based on genetic and simulated annealing algorithms are developed to solve the large-scale NP-hard nonlinear optimization problem. The model is implemented for the network of Ann Arbor, Michigan. The results suggest that EVs are optimal for the system due to low operating costs and zero tailpipe emissions. Shared autonomous electric vehicles (SAEVs) are the best option for users with low VOTT. Private autonomous electric vehicles (PAEVs) would favor the system if the travel time savings are at least 20% or the price of AV technology is less than one-third of the vehicle price. The study then investigates the optimum investment technology to support the rising energy demand at the DCFC stations and reduce the load on the electric grid network. The different investments include purchasing and installing various ESS (new batteries (NB), second-life batteries (SLB), flywheels), solar panels, electric grid upgrades, and the cost of buying/selling electricity from/to the electric grid. The model is implemented for the DCFC stations supporting the future needs of EV charging demand for urban trips in the major cities of Michigan in 2030. The combination of SLBs and solar panels provides maximum benefits. The total annual and electricity savings are $25,000-$165,000 and $40,000-$300,000 per city.
Author: Yang Song
Author: Yang Song
Author: Ti Zhang
Author: Wenzhong Shi
Author: Nam P. Suh
Author: Matthew David Dean
Author: Kenan Degirmenci
Author: Siobhan Jocelyn Larissa Powell
Author: Sina Baghali
Author: Nan Jiang (Ph. D.)
Author: Harprinderjot Singh