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In the first part of this thesis, GPS data for a year's worth of travel by 255 Seattle households is used to illuminate how plug-in electric vehicles (PEVs) can match household needs. Data from all vehicles in each of these households were analyzed at a disaggregate level primarily to determine whether each household would be able to adopt various types of PEVs without significant issues in meeting travel needs. The results suggest that a battery-electric vehicle (BEV) with 100 miles of all-electric range (AER) should meet the needs of 50% of Seattle's one-vehicle households and the needs of 80% of the multiple-vehicle households, when households charge just once a day and rely on another vehicle or mode just 4 days a year. Moreover, the average one-vehicle Seattle household uses each vehicle 23 miles per day and should be able to electrify close to 80% of its miles, while meeting all its travel needs, using a plug-in hybrid electric vehicle with 40-mile all-electric-range (PHEV40). Households owning two or more vehicles can electrify 50 to 70% of their total household miles using a PHEV40, depending on how they assign the vehicle across drivers each day. Cost comparisons between the average single-vehicle household owning a Chevrolet Cruze versus a Volt PHEV suggest that, when gas prices are $3.50 per gallon and electricity rates are 11.2 ct per kWh, the Volt will save the household $535 per year in energy/fuel costs. Similarly, the Toyota Prius PHEV will provide an annual savings of $538 per year over the Corolla. The results developed in this research provide valuable insights into the role of AER on PEV adoption feasibility and operating cost differences. The second part of this thesis uses detailed travel data from the Seattle metropolitan area to evaluate the effects of built-environment variables on the use of non-motorized (bike + walk) modes of transport. Several model specifications are used to understand and explain non-motorized travel behavior in terms of household, person and built-environment variables. Land-use measures like land-use mix, density, and accessibility indices were also created and incorporated as covariates to appreciate their marginal effects. The models include a count model for household vehicle ownership levels, a binary choice model for the decision to stay within versus departing one's origin zone (i.e., intra- versus inter-zonal trip-making), discrete choice models for destination choices and mode choices, and a zero-inflated negative binomial model for non-motorized trip counts per household. The mode and destination choice models were estimated separately for interzonal and intrazonal trips and for each of three different trip types (home-based work, home-based non-work, and non-home-based), to recognize the distinct behaviors at play when making shorter versus longer trips and different types of trips. This comprehensive set of models highlights how built-environment variables -- like the number and type of intersections present around one's origin and destination, the number of bus stops available within a certain radius, household and jobs densities, parking prices, land use mixing, and walk-based accessibility -- can significantly shape the pattern of one's non-motorized movement. The results underscore the importance of street connectivity (quantified as the number of 3-way and 4-way intersections in a half-mile radius), higher bus stop density, and greater non-motorized access in promoting lower vehicle ownership levels (after controlling for household size, income, neighborhood density and so forth), higher rates of non-motorized trip generation (per day), and higher likelihoods of non-motorized mode choices. Destination choices are also important for mode choices, and local trips lend themselves to more non-motorized options than more distance trips. Intrazonal trip likelihoods rose with higher street connectivity, transit availability, and land use mixing. For example, the results suggest that an increase in the land-use mix index by 10% would increase the probability of choosing to travel within the zone by 12%. As expected destinations with greater population and job numbers (attraction), located closer (to a trip's origin), offering lower parking prices and greater transit availability, were more popular. Interestingly, those with more dead ends (or cul de sacs) attracted fewer trips. Among all built environment variables tested, street structure offered the greatest predictive benefits, alongside jobs and population (densities and counts). For example, a 1-percent increase in the average number of 4-way intersections within a quarter-mile radius of the sampled households is estimated to increase the average household's non-motorized trip generation by 0.36%. A one-standard-deviation increase in the (mean) number of 4-way intersections at the average trip origin is estimated to increase the probabilities of bike and walk modes for interzonal home-based-work trips by 57% and 30%, respectively. In contrast, increasing the number of dead-ends at the origin by one standard deviation is estimated to decrease the probability of biking for both home-based-work and non-work trips by ~30%. These results underscore the importance of network density and connectivity for promoting non-motorized activity. The regional non-motorized travel (NMT) accessibility index ( derived from the logsum of a destination choice model) also offers strong predictive value, with NMT counts rising by by 7% following a 1% increase in this variable -- if the drive alone accessibility index is held constant (along with all other variables, evaluated at their means). Similarly, household vehicle ownership is expected to fall by 0.36% with each percentage point increase in the NMT accessibility index, and walk probabilities rise by 26.9% following a one standard deviation increase in this index at the destination zone. A traveler's socio-economic attributes also have important impacts on NMT choices, with demographics typically serving as much stronger predictors of NMT choices than the built environment. For example, the elasticity of NMT trip generation with respect to a household's vehicle ownership count is estimated to be -0.52. Males and tose with drivers licenses are estimated to have 17% and 39% lower probabilities, respectively, of staying within their origin zone, relative to women and unlicensed adults (ceteris paribus). Non-motorized model choices also exhibit strong sensitivity to age and gender settings. Several of the regional variables developed in this work, and then used in the predictive models, are highly correlated. For example, bus stop and intersection densities are very high in job- and population-dense areas. For example, the correlation co-efficients between the bus stop density and 4-way intersection density is 0.805, between NMT and SOV AIs is 0.830 and between 4-way intersection density and NMT AI is 0.627. As a result, many variables are proxying for and/or competing with each other, as is common in models with many land use covariates, and it is difficult to quantify the exact impact of each of these variables. Nonetheless the models developed here provide valuable insight into the role of several new variables on non-motorized travel choices. Some final case study applications, moving all households to the downtown area (that has high accessibility indices and density), illustrate to what extent these revealed-data-based models will predict shifts toward and away from non-motorized trip-making. It appears that average household vehicle ownership level reduces to 0.57 from 1.89 (a 70% reduction) and average two-day NMT trip generation increases to 5.92 from 0.83 (an increase of more than 6 times). Such ranges are valuable to have in mind, when communities seek to reduce reliance on motorized travel by defining new built-environment contexts. / text

This dissertation formulates and develops models and solution approaches for plug-in electric vehicle (PEV) charging station installation. The models are formulated in the form of bilevel programming and stochastic programming problems, while a meta-heuristic method, genetic algorithm, and Monte Carlo bounding techniques are used to solve the problems. Demand for PEVs is increasing with the growing concerns about environment pollution, energy resources, and the economy. However, battery capacity in PEVs is still limited and represents one of the key barriers to a more widespread adoption of PEVs. It is expected that drivers who have long-distance commutes hesitate to replace their internal combustion engine vehicles with PEVs due to range anxiety. To address this concern, PEV infrastructure can be developed to provide re-fully status when they are needed. This dissertation is primarily focused on the development of mathematical models that can be used to support decisions regarding a charging station location and installation problem. The major parts of developing the models included identification of the problem, development of mathematical models in the form of bilevel and stochastic programming problems, and development of a solution approach using a meta-heuristic method. PEV parking building problem was formulated as a bilevel programming problem in order to consider interaction between transportation flow and a manager decisions, while the charging station installation problem was formulated as a stochastic programming problem in order to consider uncertainty in parameters. In order to find the best-quality solution, a genetic algorithm method was used because the formulation problems are NP-hard. In addition, the Monte Carlo bounding method was used to solve the stochastic program with continuous distributions. Managerial implications and recommendations for PEV parking building developers and managers were suggested in terms of sensitivity analysis. First, in the planning stage, the developer of the PEV parking building should consider long-term changes in future traffic flow and locate a PEV parking building closer to the node with the highest destination trip rate. Second, to attract more parking users, the operator needs to consider the walkability of walking links.

L'augmentation des densités de puissance et d'énergie des SSE (système de stockage électrique) des véhicules électriques/véhicules hybrides rechargeable (VEs/VHRs), tout en conservant des coûts raisonnables pour l'utilisateur, et le développement de convertisseurs d'énergie électrique à haute densité de puissance volumique, et de plus en plus performant vont favoriser la production en masse de véhicules électrifiés. Une partie de ces véhicules électriques (VEs/VHRs) nécessitent une connexion au réseau pour la recharge des batteries. L’insertion de ces nouvelles charges dans le réseau présentera alors plusieurs enjeux et impacts significatifs pour les réseaux électriques puisqu’ils doivent répondre localement à des demandes de puissance non négligeables. Ce projet de thèse vise à étudier et réduire les impacts des VEs/VHRs sur les réseaux de distribution grâce à la technologie Vehicle-to-Grid (V2G). Le véhicule électrique alimente le réseau en fonction des besoins du système électrique (modèle bidirectionnel) et lui offre un service de flexibilité. Ces travaux de recherche ont pour but d'approfondir les concepts dans lequel l’alimentation des véhicules électriques (VE) et/ou hybrides de type P-VEH est intégrée à la gestion du réseau de distribution et des « hubs énergétiques » du futur. L’objectif de la thèse est d’abord étudier les service systèmes possible à fournir grâce à V2G, ensuite de concevoir un système de supervision qui assurera une gestion énergétique de ces nouvelles charges en choisissant le mode de recharge et/ou décharge adéquat et en prenant également en considération la demande de consommation locale et la présence de production de type renouvelable (photovoltaïque, éolien) dans le réseau de distribution. Cette supervision se fera dans un premier temps « en hors ligne » et par la suite « en ligne ». On aura recours à l’utilisation de méthodes d’intelligence artificielle comme l’apprentissage automatique (Machine Learning) et la logique floue, la commande prédictive ainsi que des méthodes d’optimisation hybrides (stochastiques et déterministes). / The power and energy density increment of the electrical storage system (ESS) of electric vehicles/Plug-in hybrid electric vehicles (EVs/PHEVs), while maintaining reasonable costs for the user, and the development of converters of electrical energy to high power density and more and more powerful, will encourage the mass production of electrified vehicles. Beyond, electric vehicles (EVs/PHEVs) require a connection to the grid for the charging of the batteries. The insertion of these new loads in the grid will then present several issues and significant impacts for electrical networks since they must respond locally to non-negligible power requests. This PhD thesis aims to study and reduce the impacts of the EVs/PHEVs on the distribution grid thanks to the vehicle-to-Grid (V2G) technology. The electric vehicle supplies the grid depending on the needs of the electrical system (bi-directional model) and offers a flexible service. These works of research have aimed to deepen the concepts in which the supply of electric vehicles (EV) and/or hybrids of type PHEV is integrated with the management of the distribution network and the future "energy hubs". The objective of the thesis is at first to examine the possible ancillary services provided by V2G, then to design a system of supervision which will ensure an energy management of these new loads by choosing the adequate mode of charge/discharge and also taking into consideration the request of local consumption and the presence of renewable production of type photovoltaic and wind in the distribution grid. This supervision will be in a first step "offline" and subsequently "online". The methods which are used in this thesis are as follows; artificial intelligence such as machine learning and fuzzy logic, the predictive control as well as the methods of hybrids optimization (stochastic and deterministic).

Renewable energy is key to a sustainable future. However, the intermittency of most renewable sources and lack of sufficient storage in the current power grid means that reliable integration of significantly more renewables will be a challenging task. Moreover, increased integration of renewables not only increases uncertainty, but also reduces the fraction of traditional controllable generation capacity that is available to cope with supply-demand imbalances and uncertainties. Less traditional generation also means less rotating mass that provides very short term, yet very important, kinetic energy storage to the system and enables mitigation of the frequency drop subsequent to major contingencies but before controllable generation can increase production. Demand, on the other side, has been largely regarded as non-controllable and inelastic in the current setting. However, there is strong evidence that a considerable portion of the current and future demand, such as electric vehicle load, is flexible. That is, the instantaneous power delivered to it needs not to be bound to a specific trajectory. In this thesis, we focus on harnessing demand flexibility as a key to enabling more renewable integration and cost reduction. We start with a data driven analysis of the potential of flexible demands, particularly plug-in electric vehicle (PEV) load. We first show that, if left unmanaged, these loads can jeopardize grid reliability by exacerbating the peaks in the load profile and increasing the negative correlation of demand with wind energy production. Then, we propose a simple local policy with very limited information and minimal coordination that besides avoiding undesired effects, has the positive side-effect of substantially increasing the correlation of flexible demand with wind energy production. Such local policies could be readily implemented as modifications to existing "grid friendly" charging modes of plug-in electric vehicles. We then propose improved localized charging policies that counter balance intermittency by autonomously responding to frequency deviations from the nominal frequency and show that PEV load can offer a substantial amount of such ancillary services. Next, we consider the case where real-time prices are employed to provide incentives for demand response. We consider a flexible load under such a pricing scheme and obtain the optimal policy for responding to stochastic price signals to minimize the expected cost of energy. We show that this optimal policy follows a multi-threshold form and propose a recursive method to obtain these thresholds. We then extend our results to obtain optimal policies for simultaneous energy consumption and ancillary service provision by flexible loads as well as optimal policies for operation of storage assets under similar real-time stochastic prices. We prove that the optimal policy in all these cases admits a computationally efficient form. Moreover, we show that while optimal response to prices reduces energy costs, it will result in increased volatility in the aggregate demand which is undesirable. We then discuss how aggregation of flexible loads can take us a step further by transforming the loads to controllable assets that help maintain grid reliability by counterbalancing the intermittency due to renewables. We explore the value of load flexibility in the context of a restructured electricity market. To this end, we introduce a model that economically incentivizes the load to reveal its flexibility and provides cost-comfort trade-offs to the consumers. We establish the performance of our proposed model through evaluation of the price reductions that can be provided to the users compared to uncontrolled and uncoordinated consumption. We show that a key advantage of aggregation and coordination is provision of "regulation" to the system by load, which can account for a considerable price reduction. The proposed scheme is also capable of preventing distribution network overloads. Finally, we extend our flexible load coordination problem to a multi-settlement market setup and propose a stochastic programming approach in obtaining day-ahead market energy purchases and ancillary service sales. Our work demonstrates the potential of flexible loads in harnessing renewables by affecting the load patterns and providing mechanisms to mitigate the inherent intermittency of renewables in an economically efficient manner. / text

When the low voltage electric grid is dimensioned electric loads are predicted by analyzing the area by certain factors such as geographical data, customer type, heating method etc. So far, the charging of Plugin Electric Vehicles (PEVs) is not considered as one of these factors. Approximately 30% of the distribution grid in Sweden is projected to need reinforcements due to the increased loads from PEVs during winters if the charging isn’t controlled. In addition to this Stockholm face the problem of capacity shortage from the transmission grid, limiting the flow of electricity into the city. This research is therefore conducted to analyze the impact that the increase of PEVs will have on the distribution grid in the future. This thesis simulates the electric grid for three substations located in Hammarby Sjöstad by using power flow analysis and electric grid data from 2016. To approach this problem a method to disaggregate the total power consumption per substation into power consumption responding to each building was developed. In addition to this the number of PEVs in the future was projected. Nine different scenarios were used to compare different outcomes for the future, namely the years of 2025 and 2040. In order to simulate the worst possible case for the electric grid all the PEVs were assumed to be charged at the same time, directly when arriving home on the Sunday when the power demand peaks in 2016. The results indicate that PEVs can have a considerable impact on the components of the low voltage distribution network and controlled charging should be implemented. By examining the impact on the simulated electric grid from the different scenarios the limit of PEV penetration is found. In the area of Hammarby this limit seems to be around 30 % of the total cars if there is no controlled charging. Without any controlled charging the peak power demand increases by 30% with a 30% share of PEVs, which is projected to happen in 2025. In 2040 when share of PEVs is projected to be about 95% the peak power is instead increased by more than 100% which shows the impact that PEVs can exert on the electric grid. Utilizing a simple method of controlled charging where the PEVs are instead charged during the night when the power demand is low, the peak power is not increased at all. This also results in the small cost benefit for PEV owners since the electricity is cheaper during the night and controlled charging can therefore save about 15% of the electricity charging cost. However, the main savings are for the grid owners since the need to reinforce the grid is heavily reduced. In addition to this the power losses are reduced heavily from about 14% down to 5% in the electric grid that is simulated. / När dimensioneringen av distributionsnätet utförs analyseras området genom att räkna med elektriska laster som till exempel kan bero på geografiska data, typ av konsument, uppvärmningsmetod etcetera. Än så länge har laddningen av elbilar (PEVs) inte varit en av dessa faktorer trots den förväntade tillväxten av elbilar. Ungefär 30% av Sveriges distributionsnät förväntas behöva förstärkningar på grund av den ökade elkonsumtionen från elbilar under vintrarna om laddningen inte kontrolleras. Utöver detta står Stockholm inför problemet med effektbrist från elöverföringsnätet. Denna uppsats genomförs således för att analysera påverkan från elbilar på fördelningsnätet i framtiden. Denna masteruppsats simulerar det elektriska nätet för tre nätstationer i Hammarby Sjöstad genom en analys av effektflödet. En metod för att disaggregera elkonsumtionen per nätstation ned till elkonsumtionen per byggnad utvecklades och antalet elbilar i framtiden uppskattades. För att utvärdera elbilars påverkan skapades nio olika scenarion för framtiden genom att undersöka hur det kommer att se ut år 2025 och år 2040. Genom att anta att laddningen av alla elbilar i området sker samtidigt, samma tid som den maximala förbrukningen av el sker under en söndag 2016, analyseras det värsta möjliga scenario för det elektriska nätet. Resultaten visar att elbilar kan ha enorm påverkan på de maximala lasterna för ett lågspänningsnät och därför kommer kontroll av laddningen behövas. Genom att undersöka elnätets påverkan i de olika scenariona uppskattades gränsen för hur många elbilar det modellerade elnätet klarar av. I Hammarby Sjöstad ligger denna gräns på ungefär 30% elbilar. Utan kontrollerad laddning ökar maxlasten med 30% år 2025 då antalet elbilar förväntas vara 30% av alla bilar i Hammarby Sjöstad. År 2040 då antalet elbilar uppnår ungefär 95 % av alla bilar ökar maxlasterna med mer än 100% vilket visar den enorma påverkan elbilar kan ha på elnätet. Genom att använda en simpel modell av kontrollerad laddning som består av att flytta laddningen från eftermiddagen till natten, då förbrukningen av elektricitet är låg, ökar inte maxlasten för dygnet alls jämfört med scenariot utan elbilar. Detta resulterar också i besparingen av elektricitetskostnad för elbilsägaren med cirka 15% eftersom elektriciteten ofta är billigare under natten jämfört med kvällens elpriser. Detta är dock små summor jämfört med besparingar elnätsägarna kan göra då elnätet inte behöver förstärkas lika mycket som skulle behövas utan kontroll av laddningen. Utöver detta så sänks även förlusterna av elektricitet i det simulerade nätet från 14% ned till 5% genom att utnyttja denna modell av kontrollerad laddning.