Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.

Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.

Dans le domaine de la chaîne logistique, nous constatons des besoins croissants d'information et d'interactions entre produits, processus et clients, et ceci durant le cycle de vie du produit. Cela entraîne le besoin, au niveau du produit, de nouvelles capacités de communication, de gestion de l'information, de perception et d'action avec son environnement physique. Ces besoins ont engendré le concept de produit ou objet communicant. Un produit avec ces nouvelles capacités pourra interagir avec d'autres entités physiques ou informationnelles dans son environnement et apporter des transformations significatives sur la gestion de la chaîne logistique. Le travail présenté dans cette thèse vise à analyser et contribuer à appliquer la notion d'objet communicant aux produits physiques dans le domaine de la chaîne logistique. L'approche proposée considère un produit comme un demandeur ou un fournisseur de services. La méthodologie proposée de gestion des produits communicants s'appuie sur la caractérisation d'une architecture de services ambiants devant permettre de gérer les services d'un produit d'une façon automatique et ubiquiste. Nous avons choisi l'architecture UPnP pour gérer les services des objets communicants. La communication directe avec le produit est supportée par les méthodes d'identification automatique RFID. Nous nous sommes appuyés sur le formalisme standard de modélisation UML afin de modéliser les interactions et les services associés à un produit physique. Comme résultat, nous avons élaboreé des démonstrateurs de laboratoire validant la faisabilité de notre proposition méthodologique de gestion de la chaîne logistique par les objets communicants.

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

Grouting is performed in order to decrease the permeability and increase the stiffness of the material, especially soil and rock. For tunnelling and underground constructions, permeation grouting is done where cement based materials are pumped inside drilled boreholes under a constant pressure, higher than the ground water pressure. The aim of permeation grouting is to reduce the water flow into tunnels and caverns and to limit the lowering of the surrounding groundwater table. Cement based materials are commonly used as grout due to their availability and lower costs. To obtain a proper water sealing and reduce the lowering of the ground water table, a desired spread of grout must be achieved and the rheology of the cement grout is the governing factor for estimating the required spread. Rheological properties of cement grout such as viscosity and yield stress are commonly measured off-line using laboratory instruments, and some simple tools are available to make field measurements. Although the rheological properties of the grout that is used play a fundamental role in design and execution, no method has yet been developed to measure these properties in-line in field work. In addition to the real time measurement, there is no standard method for determining the yield stress for grouting applications. Despite the common usage of Bingham model fitting to determine the yield stress, the range of shear rate is often not specified or is neglected.   In this work, an in-line rheometry method combining the Ultrasound Velocity Profiling (UVP) technique with Pressure Difference (PD) measurements, known as “UVP+PD”, was successfully tested for continuous in-line measurements of concentrated micro cement based grouts. A major obstacle of using the ultrasound based methodology was the transducers, which would be capable of emitting sufficient acoustic energy and can be used in field conditions. The transducer technology was developed in a parallel project and the Flow-Viz industrial rheometer was found to be capable of detail measurement of the velocity profiles of cement grout. The shape of the velocity profiles was visualized, and the change in the shape of the profiles with concentration and time was observed. The viscosity and yield stress of the grout were determined using rheological models, e.g. Bingham and Herschel-Bulkley. In addition, rheological properties were determined using the non-model approach (gradient method) and the tube viscometry concept and were compared with results obtained using the rheological models. The UVP+PD method was found to be capable of determining the rheological behavior of cement grout regardless of the rheological model. The yield stress of cement grout was investigated using off-line rheometry techniques and UVP+PD in-line measurements. Tests were performed applying different shear histories and it was found that two ranges of yield stress indeed exist. Therefore, the design value of yield stress should be chosen with respect to the prevailing shear rate at the grout front for the required spread of grout. In addition, an appropriate shear rate range should be used when a Bingham fitting is done to determine the yield stress. In order to estimate the shear rate, plug thickness and velocity for one dimensional and two dimensional geometry, a non- dimensional nomogram was developed. The advantage of using the nomogram is that it does not depend on the applied pressure and the rheological properties of the grout and can therefore, be used as a simple design tool. Analytical approaches were used for the estimation and good agreements were found with numerical calculations and experimental results. In conclusion, in this work, it was found that it is possible to continuously measure the velocity profiles and determine the change of the rheological properties of cement grout using the ultrasound based UVP+PD method under field conditions. The yield stress was also investigated and it was found that two range of yield stress exist depending on the prevailing shear rate of the grout, which should be used for designing the grouting time at different conditions. In order to decide the design value of yield stress for grouting applications, a non-dimensional nomogram was developed that can be used to estimate the plug thickness, shear rate and velocity of the grout. / <p>Funding for the project was provided by the Swedish Rock Engineering Research Foundation (BeFo), The Swedish Research Council (FORMAS) and The Development Fund of the Swedish Construction Industry (SBUF), who are gratefully acknowledged. QC 20151112</p>

This experimental study explores the heat transfer from heated bare and finned tubular surfaces to particulates in packed bed cross flow. The results from this experiment will be used to help select the type of particulates that will be used. Additionally, these results will assist in estimating heat transfer in prototype and commercial particle to fluid heat exchangers (PFHX). This research is part of larger effort in the use of particulates in concentrating solar power technology. These solid particles are heated by concentrated sunlight to very high temperatures at which they are a suitable heat source for various thermal power and thermochemical cycles. Furthermore, one of the advantages of this concept is the ability to store thermal energy in the solid particles at relatively low cost. However, an important feature of any Particle Heat Receiver (PHR) system is the PFHX, which is the interface between the solar energy system and the thermal power or chemical system. In order to create this system material data is needed for the design and optimization of this PFHX. The paper focuses on the heat transfer properties of particulates to solid surfaces under plug flow conditions. The particulates will be evaluated for three grain sizes of sand and two grain sizes of proppants. These two materials will be tested at one, five and ten millimeters per second in order to see how the various flow rates, which will be required for different loads, will affect the heat transfer coefficient. Finally the heat transfer coefficient will also be evaluated for both finned and non-finned heat exchangers to see the effect that changes in the surface geometry and surface area have on the heat transfer coefficient. The heat transfer coefficient will help determine the appropriate material that will be used in the PHR system.

The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

Cette thèse s'inscrit dans le cadre du projet européen PAPYRUS (7th FWP (Seventh Framework Program) et concerne le développement de modèles et d'outils permettant l'analyse d'un procédé industriel en interaction avec les indicateurs des performances du système. Ainsi que la synthèse d'algorithmes "Plug & Play" de diagnostic de défauts. Plus précisément, le premier objectif de la thèse est de proposer des modèles et des critères qui permettent, pour un procédé complexe de grande dimension, de savoir si des objectifs, exprimés en termes de performances (coût, de sûreté de fonctionnement, etc.), sont atteignables. Dans le cadre de la modélisation de causalité du système, une méthode, basée sur le transfert entropie, est proposée afin d'identifier le modèle de causalité du système à partir des données. On s'intéressera aussi à l'influence de divers défauts sur cette atteignabilité. Les outils utilisés sont principalement basés sur l'analyse par approche graphique (graphe de causalité) conjointement avec des outils statistiques. Le second objectif concerne la mise en oeuvre d'algorithmes de diagnostic de défauts. Une procédure hiérarchique de diagnostic de défauts s'appuyant sur les modèles de causalité du système est mise en oeuvre. Cette étape a aussi pour objectif de permettre l'évaluation des performances du système. La cible est le procédé d'application du projet PAPYRUS (papeterie Stora Enso d'IMATRA en Finlande).

This thesis presents novel algorithms and a revised modeling framework to evaluate residential aggregate electrical demand response performance under scenarios with limited device-state feedback. These algorithms permit the provision of balancing reserves, or the smoothing of variable renewable energy generation, via an externally supplied target trajectory. The responsive load populations utilized were home heat pumps and deferred electric vehicle charging. As fewer devices in a responsive population report their state information, the error of the demand response program increases moderately but remains below 8%. The associated error of the demand response program is minimized with responsive load populations of approximately 4500 devices; the available capacity of the demand response system scales proportionally with population size. The results indicate that demand response programs with limited device-state feedback may provide a viable option to reduce overall system costs and address privacy concerns of individuals wishing to participate in a demand response program. / Graduate

The purpose of study is to research the development of electric vehicle technology in the United States. This study describes the United States public policies towards electric vehicle technology and system of innovation approaches. The government roles with the help of national system of innovation have been also covered in this study. The point of departure was the study of available literature and U.S energy policy acts which illustrates that the break-through in electric vehicles still not only depended on better battery technology and infrastructure for charging stations but also on social, economic and political factors. The important actors involved in the process are both at local and international level are private firms, governmental departments, research and development (R&amp;D) institutes, nongovernment organizations (NGO’s) and environmental organizations etc. The arguments which are put forward in the background of development of such technologies are to reduce dependence on foreign oil and to reduce emissions of harmful gasses.