As electric vehicles (EVs) become more popular, the utility companies are forced to increase power generations in the grid. However, these EVs are capable of providing power to the grid to deliver different grid ancillary services in a concept known as vehicle-to-grid (V2G) and grid-to-vehicle (G2V), in which the EV can serve as a load or source at the same time. These services can provide more benefits when they are integrated with Photovoltaic (PV) generation. The proper modeling, design and control for the power conversion systems that provide the optimum integration among the EVs, PV generations and grid are investigated in this thesis. The coupling between the PV generation and integration bus is accomplished through a unidirectional converter. Precise dynamic and small-signal models for the grid-connected PV power system are developed and utilized to predict the system’s performance during the different operating conditions. An advanced intelligent maximum power point tracker based on fuzzy logic control is developed and designed using a mix between the analytical model and genetic algorithm optimization. The EV is connected to the integration bus through a bidirectional inductive wireless power transfer system (BIWPTS), which allows the EV to be charged and discharged wirelessly during the long-term parking, transient stops and movement. Accurate analytical and physics-based models for the BIWPTS are developed and utilized to forecast its performance, and novel practical limitations for the active and reactive power-flow during G2V and V2G operations are stated. A comparative and assessment analysis for the different compensation topologies in the symmetrical BIWPTS was performed based on analytical, simulation and experimental data. Also, a magnetic design optimization for the double-D power pad based on finite-element analysis is achieved. The nonlinearities in the BIWPTS due to the magnetic material and the high-frequency components are investigated rely on a physics-based co-simulation platform. Also, a novel two-layer predictive power-flow controller that manages the bidirectional power-flow between the EV and grid is developed, implemented and tested. In addition, the feasibility of deploying the quasi-dynamic wireless power transfer technology on the road to charge the EV during the transient stops at the traffic signals is proven.

The world faces the unprecedented challenge of the need change to a new energy era. The introduction of distributed renewable energy and storage together with transportation electrification and deployment of electric and hybrid vehicles, allows traditional consumers to not only consume, but also to produce, or store energy. The active participation of these so called "prosumers", and their interactions may have a significant impact on the operations of the emerging smart grid. However, how these capabilities should be integrated with the overall system operation is unclear. Intelligent energy management systems give users the insight they need to make informed decisions about energy consumption. Properly implemented, intelligent energy management systems can help cut energy use, spending, and emissions. This thesis aims to develop a consumer point of view, user-friendly, intelligent energy management system that enables vehicle drivers to plan their trips, manage their battery pack and under specific circumstances, inject electricity from their plug-in vehicles to power the grid, contributing to frequency regulation.

With the emerging nationwide availability of plug-in electric vehicles (PEVs) at prices attainable for many consumers, electric utilities, system operators, and researchers have been investigating the impact of this new source of electricity demand. The presence of PEVs on the electric grid might offer benefits equivalent to dedicated utility-scale energy storage systems by leveraging vehicles' grid-connected energy storage through vehicle-to-grid (V2G) enabled infrastructure. Existing research, however, has not effectively examined the interactions between PEVs and the electric grid in a V2G system. To address these shortcomings in the literature, longitudinal vehicle travel data are first used to identify patterns in vehicle use. This analysis showed that vehicle use patterns are distinctly different between weekends and weekdays, seasonal interactions between vehicle charging, electric load, and wind generation might be important, and that vehicle charging might increase already high peak summer electric load in Texas. Subsequent simulations of PEV charging were performed, which revealed that unscheduled charging would increase summer peak load in Texas by approximately 1\%, and that uncertainty that arises from unscheduled charging would require only limited increases in frequency regulation procurements. To assess the market potential for the implementation of a V2G system that provides frequency regulation ancillary services, and might be able to provide financial incentives to participating PEV owners, a two-stage stochastic programming formulation of a V2G system operator was created. In addition to assessing the market potential for a V2G system, the model was also designed to determine the effect of the market power of the V2G system operator on prices for frequency regulation, the effect of uncertainty in real-time vehicle availability and state-of-charge on the aggregator's ability to provide regulation services, and the effect of different vehicle characteristics on revenues. Results from this model showed that the V2G system operator could generate revenue from participation in the frequency regulation market in Texas, even when subject to the uncertainty in real-time vehicle use. The model also showed that the V2G system operator would have a significant impact on prices, and thus as the number of PEVs participating in a V2G program in a given region increased, per-vehicle revenues, and thus compensation provided to vehicle owners, would decline dramatically. From these estimated payments to PEV owners, the decision to participate in a V2G program was analyzed. The balance between the estimated payments to PEV owners for participating in a V2G program and the increased probability of being left with a depleted battery as a result of V2G operations indicate that an owner of a range-limited battery electric vehicle (BEV) would probably not be a viable candidate for joining a V2G program, while a plug-in hybrid electric vehicle (PHEV) owner might find a V2G program worthwhile. Even for a PHEV owner, however, compensation for participating in a V2G program will provide limited incentive to join. / text

Transportation and energy play an essential role in modern society. Since the Industrial Revolution, fossil fuels have enabled great advancements in human society. Within this process, Internal Combustion Engines Vehicles (ICEVs) played a significant role in guaranteeing reliable and affordable long-distance transportation. However, the subsequent increase of the Motorized Private Transport resulted in undesired effects such as pollution. One instrument in reducing the Greenhouse Gas (GHG) emissions of the transport sector is to shift from the conventional ICEVs toward zero local emission vehicles. Electric Vehicles (EVs) are being promoted worldwide as a suitable powertrain technology that could replace the ICEVs. However, unless combined with electricity from renewable generation technologies the EVs will not effectively reduce GHG emissions. Through the simulation of future transport and energy sector scenarios in Germany, the GHG emission reductions have been analyzed. Techno-economic and environmental characteristics for several powertrain technologies under several vehicles charging strategies are evaluated. The thesis explores the impact of charging EVs on the electrical grid. The result show that EVs using smart charging strategies that support Vehicle-to-grid (V2G) are capable of fulfilling mobility needs of users while providing substantial flexibility to the electrical grid. Such flexibility can facilitate the future expansion of non-dispatchable Renewable Energy Sources (RES).

The effects of businesses all over the globe on social issues like climate change have caused an increasing demand for those businesses to take responsibility for their actions. While corporate social responsibility has been concerned with such topics for a while, the more recent concept of ‘creating shared value’ aims to have a more justified approach in a way that it provides economic value for the implementing company while also targeting social issues simultaneously. Still, specific tools helping companies to implement initiatives that create shared value are missing.Multinational large-fleet operators, such as logistics companies or car rental services, are considered to contribute a significant share to the earlier mentioned social issue of climate change. With the rising adoption of electric vehicles by such large-fleet operators, the concept of Vehicle-to-Grid is identified as a way for multinational large-fleet operators to create shared value. Vehicle-to-Grid is a technology that promises to help increase the utilisation of renewable energy sources, thereby helping to tackle climate change. Since the concepts of creating shared value and Vehicle-to-Grid have not been combined so far, a research gap was identified. Therefore, this research aims to answer the questions of how Vehicle-to-Grid can create shared value for multinational large-fleet operators and how expected results of that implementation can be measured for the implementing company, society and other considered stakeholders.Empirical data is collected by qualitatively interviewing organisations that have been involved in Vehicle-to-Grid related projects and is analysed with the help of a conceptual framework that the authors developed. The conclusion of this study closes the identified research gap and contributes to the theory of how shared value initiatives can be implemented. The research suggests that for multinational large-fleet operators, shared value creation by implementing Vehicle-to-Grid could be achieved by redefining productivity in the value chain and enable local cluster development. Additionally, the research gives implications on how progress for all considered parties can be measured and suggests managerial and policy implications that would help to define Vehicle-to-Grid business cases in the future.

Indiana University-Purdue University Indianapolis (IUPUI) / With the rapid growth of technology, scientists are trying to find ways to make the world a more efficient and eco-friendly place. The research and development of electric vehicles suddenly boomed since natural resource are becoming very scarce. The significance of an electric vehicle goes beyond using free energy, it is environ- mental friendly. The objective of this thesis is to understand what Vehicle to Grid Communication (V2G) for an electric vehicle is, and to implement a model of this highly efficient system into a Hybrid Petri Net. This thesis proposes a Hybrid Petri net modeling of Vehicle to Grid (V2G) Communication topology. Initially, discrete, continuous, and hybrid Petri net's are defined, familiarized, and exemplified. Secondly, the Vehicle and Grid side of the V2G communication system is introduced in detail. The modeling of individual Petri nets, as well as their combination is discussed thoroughly. Thirdly, in order to prove these systems, simulation and programming is used to validate the theoretical studies. A Matlab embedded simulation program known as SimHPN is used to simulate specific scenario's in the system, which uses Depth-first Search (DFS) Algorithm. In addition to SimHPN simulation program, Matlab program is made to output four levels of the reachability tree as well as specifying duplicate and terminate nodes. This code incorporates a technique known as Breadth-first Search (BFS) Algorithm.

The functionality to both charge and discharge energy from and to the power grid to a Battery Electric Vehicle (BEV) is referred to as Vehicle-to-Grid (V2G). This allows the customer to buy energy when the spot price is low and sell energy when the price is high to make a profit, called energy arbitrage. However, when the battery is charging, discharging, or idling for storage, battery degradation occurs due to chemical properties and reactions. This thesis developed a mathematical optimisation model in Python, using the modelling language Pyomo. Mathematical equations are used to integrate energy arbitrage and degradation data to reduce the total cost in terms of degradation and energy by finding an optimised charge and discharge pattern. The model allows different user scenarios to be analysed by changing inputs such as charger power, battery cost or daily driving distance. When using V2G technology, the State-of-Charge (SoC) level of BEVs battery packs can be adjusted to find SoC levels which minimise the battery degradation, while allowing the user to make a profit from energy arbitrage. The result shows that the V2G charging protocol, compared to protocols without a bidirectional charger could be beneficial for the simulated time periods, by both reducing degradation and the total energy cost. The results also indicate that the degradation cost of the battery is often the determining factor in the decision of when to charge or discharge, i.e., the substantial cost-saving strategy is to control the storage and cycle degradation to reduce the total degradation, rather than controlling the energy arbitrage. The model and the result of this thesis can be used by car manufacturers to learn more about how battery cell types behave in V2G mode and influence further work on V2G control.

This dissertation studies a framework in support electric vehicle (EV) charging station expansion and management decisions. In the first part of the dissertation, we present mathematical model for designing and managing electric vehicle charging stations, considering both long-term planning decisions and short-term hourly operational decisions (e.g., number of batteries charged, discharged through Battery-to-Grid (B2G), stored, Vehicle-to-Grid (V2G), renewable, grid power usage) over a pre-specified planning horizon and under stochastic power demand. The model captures the non-linear load congestion effect that increases exponentially as the electricity consumed by plugged-in EVs approaches the capacity of the charging station and linearizes it. The study proposes a hybrid decomposition algorithm that utilizes a Sample Average Approximation and an enhanced Progressive Hedging algorithm (PHA) inside a Constraint Generation algorithmic framework to efficiently solve the proposed optimization model. A case study based on a road network of Washington, D.C. is presented to visualize and validate the modeling results. Computational experiments demonstrate the effectiveness of the proposed algorithm in solving the problem in a practical amount of time. Finding of the study include that incorporating the load congestion factor encourages the opening of large-sized charging stations, increases the number of stored batteries, and that higher congestion costs call for a decrease in the opening of new charging stations. The second part of the dissertation is dedicated to investigate the performance of a collaborative decision model to optimize electricity flow among commercial buildings, electric vehicle charging stations, and power grid under power demand uncertainty. A two-stage stochastic programming model is proposed to incorporate energy sharing and collaborative decisions among network entities with the aim of overall energy network cost minimization. We use San Francisco, California as a testing ground to visualize and validate the modeling results. Computational experiments draw managerial insights into how different key input parameters (e.g., grid power unavailability, power collaboration restriction) affect the overall energy network design and cost. Finally, a novel disruption prevention model is proposed for designing and managing EV charging stations with respect to both long-term planning and short-term operational decisions, over a pre-determined planning horizon and under a stochastic power demand. Long-term planning decisions determine the type, location, and time of established charging stations, while short-term operational decisions manage power resource utilization. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under stochastic exogenous factors such as outside temperature and air velocity. Since the re- search problem is NP-hard, a Sample Average Approximation method enhanced with a Scenario Decomposition algorithm on the basis of Lagrangian Decomposition scheme is proposed to obtain a good-quality solution within a reasonable computational time. As a testing ground, the road network of Washington, D.C. is considered to visualize and validate the modeling results. The results of the analysis provide a number of managerial insights to help decision makers achieving a more reliable and cost-effective electricity supply network.

The recent increase of electric cars adoption will inuence the electricity demand in the distributionnetworks which risks to be higher than the maximum power available in the grid, if not well planned. Forthis reason, it is on the DSOs and TSOs's interest to plan carefully coordinated charging of a bulk of EVsas well as assess the possibility of EVs acting as energy storages with the Vehicle-to-Grid (V2G) or Vehicleto-Building (V2B) capability. When parked and plugged into the electric grid, EVs will absorb energy andstore it, being also able to deliver electricity back to the grid/building (V2G/B system).This can be anoptimized process, performed by an aggregator, gathering multiple EVs that discharge the battery into thegrid at peak time and charge when there is low demand i.e. overnight and o-peak hours.Numerous studies have investigated the possibility of aggregating multiple EVs and optimizing theircharging and discharging schedules for peak load reduction or energy arbitrage with participation in theelectricity market. However, no study was found for optimizing a shared eet of EVs with daily reservationsfor dierent users trying to perform V2B. In this study an optimization modelling algorithm (mixed integerlinear problem - MILP) that manages the possible reservations of the shared eet of EVs, coordinates thecharging and discharging schedules, and provides V2B (Vehicle-to-Building), with the objective of minimizingenergy costs and accounting with battery ageing has been developed. A case study with real data for abuilding is carried out modelling dierent number of EVs for two dierent days in year 2017, one in Marchand other in June.Results show that the prots are higher for all cases when introducing V2B as compared to a no optimizationscenario: V2B with battery degradation (50 ore/kWh) has decreased daily variable electricity costsbetween 54 and 59% in March and 60 and 63% for June when compared without smart charging. Integrationof battery degradation cost in V2B applications is necessary and inuences signicantly the chargingand discharging strategies adopted by EV and nally the total daily costs: The total daily cost increaseby maximal 10% for the day in March and 13% for the day in June when comparing the scenario that hasstationary battery and uses only-charging model for EVs with the scenario applying V2B mode consideringa degradation cost of 80 ore/kWh. / Ö kningen av antalet elbilar kommer att påverka lasten i elnätet som riskerar att bli högre än kapacitetom det inte är väl planerat. Därför är det i elnätsföretags intresse att samordna laddningen av de flesta elbilarna samt att utvärdera möjligheterna att använda elbilar som energilager gentemot elnätet (Vehicleto-Grid,V2G) eller byggnader (Vehicle-to-Building, V2B). Vid parkering och anslutning till elnätet kommer elbilar att ladda energi och lagra den, samtidigt de kan leverera el tillbaka till elnätet eller byggnaden (V2G/V2B). Detta kan vara en optimerad process som utförs av en aggregator genom att ladda flera elbilar i låglasttimmar och ladda ur dem under höglasttimmar.Många studier har undersökt möjligheten att aggregera flera elbilar och optimera laddningsoch urladdningsplaner för topplastreduktion eller energiarbitrage på elmarknaden. Ingen studie har dock hittats för att optimera en gemensam flotta av elbilar med dagliga reservationer för olika användare som försöker utföra V2B. Denna studie har utvecklat en optimeringsmodell (blandad heltalsprogrammering MILP) som hanterar möjliga reservationer av en flotta av elbilar, koordinerar laddning och urladdning planering, och utför V2B för att minimera energikostnader med hänsyn till batteriets åldrande. En fallstudie för en byggnad genomfördes modellering av olika antal elbilar för två dagar 2017, en i mars och andra i juni.Resultaten visar att vinsten är högre i samtliga fall då man introducerar V2B jämfört med scenario utan optimering: V2B med batteriladdningskostnad 50 öre/kWh minskade dagliga rörliga elkostnader mellan 54% och 59% i mars och mellan 60% och 63% i juni jämfört med utan smart laddning. Att inkludera batteriladdningskostnaden i V2B-applikationer är nödvändigt och har en signifikant inverkan på laddningsstrategierna och de totala kostnaderna: De totala dagliga kostnaderna ökar med upp till 10% i mars och upp till 13% i juni då man jämför scenariot att bara ladda elbilar och ha stationärt batteri med scenariot V2B med hänsyntill batteriladdningskostnad 80 öre/kWh.

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