Due to recent concerns regarding energy conservation and dependence on fossil fuels, the efficient integration of electric vehicle populations’ in the future smart grid has become a significant area of research. Despite the heavy penetration of smart meters throughout North America and Europe a lack of research exists utilizing real consumption data. Making use of smart meter data, a model of a prototypical residential feeder was created to observe the effects of increased electric vehicle penetration on the selected feeder. Simulations of the current operation of the feeder were performed along with modeled uncoordinated, coordinated, and coordinated vehicle to grid charging of electric vehicles. As expected, it was found that the normal operation of the feeder is subject to several inefficiencies which are made worse by uncoordinated charging of electric vehicles. Significant improvements in the considered operational parameters were found as a result of the coordinated charging of vehicles using a quadratic programming based control algorithm. Further it was found, that the use of coordinated vehicle to grid connections only produces marginal benefits over standard connections. Leading to the conclusion that development of charging controls is more significant to the mitigation of electric vehicle charging effects than the integration of vehicle to grid connections. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2014-01-31 16:02:37.201

This thesis seeks to understand the relationship between government influence and market forces pertaining to the introduction of new technologies in the market. The thesis will do this by utilizing electric vehicle supply equipment (EVSE) in California and Virginia as a historical analysis case study to determine the historical catalysts for change in the public EVSE market since its introduction in 1995. Comparing the rate of change to historical timelines for both states, "market tendencies" and "government involvement" played the greatest role in EVSE growth, with there being a distinct shift from "market tendencies" to "government involvement" over time. Results show that California has fully embraced the interventionist role, with state and local actors playing a part. Virginia, on the other hand, has just begun to allow state intervention, so much of the change in the state has come from economic or business events. Data shows, however, that this could be changing, and that Virginia could be on the verge of allowing for market intervention based on equitable development and future economic opportunity. / Master of Arts / The 21st century is ripe with innovative technologies and ideas that influence the future of the world, but not all these ideas are fully embraced in the private market. This thesis looks to understand the different roles that the government can play in assisting with the development of markets by analyzing the introduction of electric vehicle supply equipment (EVSE) for public use in California and Virginia. Using a historical-analytic approach, I gathered data on the rate of increase in EVSE and compared that to the historical timelines to determine the variables with the most influence. After identifying four "pivotal moments" in the timeline, I discovered that the major catalysts for change were "market tendencies" and "government involvement." Looking at the progression, I determined that there is a distinct trend shifting from market tendencies, at the beginning of the timeline, to government involvement in modern changes. Evidence shows that not only is this trend embraced in California, with many state and local bodies working on the issue, but it also shows that Virginia, the laggard of the two states, could be on the verge of straying from its ideals of "free markets" to embrace change.

A “smarter grid” is expected to be more flexible and more reliable than traditional electric power grids. Among technologies required for the “smarter grid” deployment, this dissertation presents a sustainable microgrid and a spatial and temporal model of plug-in electric vehicle charging demand for the “smarter grid”. First, this dissertation proposes the dynamic modeling technique and operational strategies for a sustainable microgrid primarily powered by wind and solar energy resources. Multiple-input dc-dc converters are used to interface the renewable energy sources to the main dc bus. The intended application for such a microgrid is an area in which there is interest in achieving a sustainable energy solution, such as a telecommunication site or a residential area. Wind energy variations and rapidly changing solar irradiance are considered in order to explore the effect of such environmental variations to the intended microgrid. The proposed microgrid can be operated in an islanded mode in which it can continue to generate power during natural disasters or grid outages, thus improving disaster resiliency of the “smarter grid”. In addition, this dissertation presents the spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. The first letter M of M/M/s indicates that discharged vehicles arrive at a charging station with the Poisson distribution. The second letter M denotes that the time to charge each EV is exponentially distributed, and the third letter s means that there are s identical charging pumps at a charging station. This mathematical model of charging demand may allow grid’s distribution planners to anticipate charging demand at a specific charging station. / text

abstract: Transmission line capacity is an obstacle for the utilities because there is a load increment annually, and new power plants are being connected, which requires an update. Energy router (ER) is a device that provides an additional degree of freedom to the utilities by controlling the reactive power. The ER reactive power injection is demonstrated by changing the line's reactance value to increase its capacity and give the utility a deferral time for the project upgrade date. Changing the reactance manually and attaching Smart Wire's device to the branches have effectively solved the overload in three locations of a local utility in Arizona (LUA) system. Furthermore, electric vehicle charging stations (EVCSs) have been increasing to meet EV needs, which calls for an optimal planning model to maximize the profits. The model must consider both the transportation and power systems to avoid damages and costly operation. Instead of coupling the transportation and power systems, EVCS records have been analyzed to fill the gap of EV demand. For example, by accessing charging station records, the moment knowledge of EV demand, especially in the lower order, can be found. Theoretically, the obtained low-order moment knowledge of EV demand is equivalent to a second-order cone constraint, which is proved. Based on such characteristics, a chance-constrained (CC) stochastic integer program for the planning problem is formulated. For planning EV charging stations with ER, this method develops a simple ER model to investigate the interaction between the mobile placement of power flow controller and the daily pattern of EV power demand. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Technological advancements in battery electric vehicles (BEVs) have developed alongside increases in vehicle size and the introduction of vehicle styling more similar to internal combustion engine vehicles (ICEVs). Increases in the distance a BEV can travel on a single charge have been accompanied by the ability to recharge the vehicle much faster than the BEV models available just 10 years ago. The Environmental Protection Agency (EPA) reports for model year 2021 include 40 BEV models and many manufacturers have signaled plans to increase the number of battery electric vehicle models offered. As more consumers consider purchasing a battery electric vehicle the question of how well that vehicle can meet all their needs is asked more frequently. This research examines the current DC-Fast charging infrastructure to evaluate how the current distribution of chargers impacts consumer convenience for non-commute routes. No study has evaluated the impact that the current DC-Fast charging infrastructure has on the consumer driving experience and we fill this research need because it will allow consumers to understand more accurately how a (BEV) may meet their needs while also allowing BEV manufacturers to better understand the impacts of potential investments in charging infrastructure. The authors examine over 30,000 pairs of simulated BEV and ICEV routes and compare the distance and duration variations for each pair. Due to our effort to consider the suitability for long distance trips, we have ensured that more than 50% of the simulated routes have a minimum travel distance of 500 miles and over 15% of the routes exceed 1000 miles. Working from this data, 99.7% of the locations in a sample of 360 places in the contiguous U.S. can be reached without relying on the ability to charge a BEV overnight. We further identify a median increase in BEV trip duration of 13.1% and a median increase in distance of 0.06%. The differences in median travel time, particularly when trips exceed 400 miles suggests that long trips made with a BEV may result in longer total travel time, however, differences in route length between BEVs and ICEVs were minimal. These findings serve as the foundation to discuss challenges and solutions related to widespread non-commuter adoption of BEVs in a variety of geographic locations, including how and where the consumer experience may vary. The results from this work will support consumer awareness about the ability of a BEV to meet their needs as well to aid in the evaluation of infrastructure investment as it relates to improving the consumer experience. The methods employed serve as a foundation for future work to investigate the relationship between vehicle type and consumer experience as well as to advance algorithms capable of evaluating routes that require a selection to be made from a set of optional stops. / Master of Science / Technological advancements in battery electric vehicles have developed alongside increases in vehicle size and the introduction of vehicle styling more similar to the gasoline powered internal combustion engine vehicles that many people currently own. Increases in the distance a vehicle can travel on a single charge have been accompanied by the ability to recharge the vehicle much faster than the battery electric vehicle models available just 10 years ago. The Environmental Protection Agency reports that there are 40 battery electric vehicle models available for model year 2021 and many manufacturers have signaled plans to increase the number of battery electric vehicle models offered. As more consumers consider purchasing a battery electric vehicle the question of how well that vehicle can meet all their needs is asked more frequently. This study examines one of the factors that impact the answer to that question: how does the driving experience vairy between gasoline powered vehicles and battery electric vehicles when long trips must be made. The distance and total time to complete the trips were compared across more than 30,000 pairs of routes within the lower 48 states of the United States and the District of Columbia. Battery electric vehicle routes were modeled based on the capabilities of Tesla vehicles due to the well-developed charging infrastructure that supports them. More than 50% of the routes examined exceed 500 miles, emphasizing the focus on long distance travel. Many routes with a total length of less than 400 miles were found to have little or no difference in total travel time or travel distance. However, when trips with a length of 500 miles or more are included the median difference in travel time is 13.1% accompanied by a minimal difference in travel distance of 0.06%. Due to the rapidly increasing travel range of battery electric vehicles and the speed at which they can recharge combined with the frequent installment of new charging locations throughout the United States it is expected that these differences would be smaller today than at the time this study was conducted. The results of this study can be used by consumers to establish realistic expectations regarding how the experience of traveling long distances in a battery electric vehicle may compare with the gasoline powered vehicle they are already familiar with. Battery electric vehicle manufacturers and others considering investments in charging infrastructure may also apply the findings discussed in this study to better communicate the long-distance performance of their vehicles with consumers and identify locations where improvements in the charging infrastructure would be most beneficial to the consumer experience. Future work is needed to explore how the long-range travel experience has continued to improve. The framework of this study provides a foundation for further evaluation of the impact that vehicle and infrastructure developments may have on the consumer experience.

"In this thesis, based on the existing researches on power factor correction technology, I analyze, design and study the Boost type power factor correction technology, which is applied in the in-board two-stage battery charger. First I analyzed the basic working principle of the active power factor corrector. By comparing several different topologies of PFC converter main circuit and control methods, I specified the research object to be the average current control (ACM) boost power factor corrector. Then I calculated and designed the PFC circuit and the ACM controller applied in the first level charging of EVs. And I run the design in Simulink and study the important features like power factor, the input current waveform and the output DC voltage and the THD and odd harmonic magnitude."

As global climate change concerns, technological advancements, and economic shifts increase the adoption of electric vehicles, it is vital to study how best to integrate these into our existing energy systems. Electric vehicles (EVs) are on track to quickly become a large factor in the energy grid. If left uncoordinated, the charging of EVs will become a burden on the grid by increasing peak demand and overloading transformers. However, with proper charging control strategies, the problems can be mitigated without the need for expensive capital investments. Distributed control methods are a powerful tool to coordinate the charging, but it will be important to assess the trade-offs between performance, information privacy, and computational speed between different control strategies. This work presents a comprehensive comparison between four distributed control algorithms simulating two case studies constrained by dynamic transformer temperature and current limits. The transformer temperature dynamics are inherently nonlinear and this implementation is contrasted with a piece-wise linear convex relaxation. The more commonly distributed control methods of Dual Decomposition and Alternating Direction Method of Multipliers (ADMM) are compared against a relatively new algorithm, Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN), as well as against a low-information packetized energy management control scheme (PEM). These algorithms are implemented with a receding horizon in two distinct case studies: a local neighborhood scenario with EVs at each network node and a hub scenario where each node represents a collection of EVs. Finally, these simulation results are compared and analyzed to assess the methods’ performance, privacy, and processing metrics for each case study as no algorithm is found to be optimal for all applications.

The paradigm of personal transportation is changing. Electric vehicles are here. The arrival of the Tesla Roadster, Nissan Leaf, and Chevy Volt has changed the way in which we have to think about the energy that fuels our transportation needs. As PEVs find their way into garages this year and especially in the coming years, the neighborhood, city, state, and regional electric infrastructure will take on a new importance for many people as their interactions with it become significantly more complex and intimate as a result of regular electric vehicle charging.

Electric Vehicles represent a small niche market today, but is predicted to grow rapidly over the next years. In order to prepare for this upcoming trend it is the networks of Electric Vehicle Charging Stations (EVCS) must expand, leading to an increasing demand for EVCSs. The EVCS market is thus becoming increasingly more popular to companies, and therefore this study’s purpose is to investigate this market and its competitive situation. The method used in this study includes a brief market analysis and a competitor analysis. The market analysis includes identification of the EVCS markets together assessing the future of the markets, and identification of EVCS market drivers and restraints. The competitor analysis includes competitor identification, classification and analysis. The top ten competitors are analyzed by the use of document content analysis, the analysis involves understanding the competitors’ target customers, how they do business and how their marketing material is structured. The three most promising EVCS markets, both currently and in the future, are the Asia Pacific, Europe and the North America markets. Most of the top competitors are active within these three markets. Regional developments, and market drivers and restraints of these markets have been identified. The opportunities in the EVCS markets are many as they are relatively unexploited markets without any actual market leaders, and also that all markets are predicted to grow at a very high rate over the coming decade in parallel with the projected mass adoption if Electric Vehicles (EVs). / Idag utgör elfordon endast en liten nischmarknad i transportmarknaden, men denna förväntas växa snabbt under de närmaste åren. För att kunna hantera marknadsetableringen av elfordon måste elfordonsladdningsinfrastrukturen byggas ut, vilket leder till en ökad efterfrågan på elfordonsladdningsstationer. Elfordonsladdningsmarknaden förespås således bli allt mer intressant för företag. Detta examensarbete genomförs på grund av detta växande intresse, då studiens syfte är att undersöka elfordonsladdstationsmarknaden och dess konkurrenssituation. Metoden som används i denna studie inbegriper en kort marknadsanalys och en konkurrensanalys. Marknadsanalysen innehåller identifiering av elfordonsladdningsmarknaderna, vad som driver och hindrar marknaderna, och en bedömning av hur framtiden ser ut för marknaderna. I konkurrensanalysen ingår identifiering, klassificering och analys av de olika konkurrenterna. De tio mest konkurrenskraftiga konkurrenterna analyseras med hjälp av dokumentinnehållsanalys, syftet med analysen är att förstå konkurrenternas målgrupper, hur de gör affärer och hur deras marknadsföringsmaterial är strukturerad. De tre mest lovande elfordonsladdningsmarknaderna, både nu och i framtiden, är marknaderna i Asien och Stillahavsområdet, Europa och Nordamerika. De flesta av de analyserade konkurrenterna är verksamma inom dessa tre marknader. Den regionala utvecklingen, och vad som driver och begränsar marknaderna har identifierats för de tre mest lovande marknaderna. Eftersom dessa marknader är relativt oexploaterade i samband med att de förväntas växa med väldigt hög takt det kommande decenniet parallellt med massanvändningen av elfordon är möjligheterna många för de företag som inriktar sig mot elbilsladdning.

We consider a facility location problem with uncertainty flow customers' demands, which we refer to as stochastic flow capturing location allocation problem (SFCLAP). Potential applications include siting farmers' market, emergency shelters, convenience stores, advertising boards and so on. For this dissertation, electric vehicle charging stations siting with maximum accessibility at lowest cost would be studied. We start with placing charging stations under the assumptions of pre-determined demands and uniform candidate facilities. After this model fails to deal with different scenarios of customers' demands, a two stage flow capturing location allocation programming framework is constructed to incorporate demand uncertainty as SFCLAP. Several extensions are built for various situations, such as secondary coverage and viewing facility's capacity as variables. And then, more capacitated stochastic programming models are considered as systems optimal and user oriented optimal cases. Systems optimal models are introduced with variations which include outsourcing the overflow and alliance within the system. User oriented optimal models incorporate users' choices with system's objectives. After the introduction of various models, an approximation method for the boundary of the problem and also the exact solution method, the L-Shaped method, are presented. As the computation time in the user oriented case surges with the expansion of the network, scenario reduction method is introduced to get similar optimal results within a reasonable time. And then, several cases including testing with different number of scenarios and different sample generating methods are operated for model validation. In the last part, simulation method is operated on the authentic network of the state of Arizona to evaluate the performance of this proposed framework.