Dissertations / Theses: 'Vehicle energy demand'

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PILLA, SIRISH. "DEMAND V/S SUPPLY OF ENERGY FOR ELECTRIC VEHICLE." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2826.

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The ongoing ascent in electric vehicle (EV) selection is commonly observed as certain, as EVs conceivably give a more clean option in contrast to conventional vehicles. However for EVs to understand this potential, singular purchasers don't just need to embrace EVs, they likewise need to utilize the advancements and foundation in a feasible manner EVs are a spotless method of transport when accused of energy from sustainable sources, for example, wind energy and photovoltaic (PV) sun oriented energy. As of now, EVs are commonly charged in the early night, when power interest of families is high and inexhaustible energy creation is low .EV clients should along these lines be urged to act in a more economical manner, by effectively or latently shifting charging request or to participate in brilliant charging or vehicle-to-framework plans worked by gatherings, for example, aggregators.

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Whitehead, Jake. "Energy Efficient Vehicle Policy: Lessons Learnt : An analysis of the effects of incentive policies on the demand, usage and pricing of energy efficient vehicles." Doctoral thesis, KTH, Transport- och lokaliseringsanalys, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185933.

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Encouraging the uptake of energy efficient vehicles (EEVs) is an aspiration of critical importance in a day and age in which we are confronted with the increasingly dire consequences of human behaviour on our planet, and on the planet for generations to come. The transport sector is one of the highest contributors of anthropogenic greenhouse gas emissions, whilst pollution from this sector is responsible for a large proportion of human deaths each and every year. Given the severity of these issues, it is more important than ever for policy-makers, and researchers alike, to encourage a transition within the community towards more sustainable lifestyles. Transportation is key to this change. As a service that every human being uses, almost every day of his or her life, the transport sector presents a unique opportunity for behavioural change. Through efficient and targeted policies, consumers can be incentivised to make more sustainable transport choices and to consider the consequences of their own actions. Foremost amongst these initiatives is that of encouraging a transition towards energy efficient vehicles. This thesis has been produced in order to shed further light on issues affecting this transition. In particular for policy-makers, this document includes a series of recommendations based on prevailing findings in the current literature, in addition to the novel and significant findings of this research effort. These include the various lessons learnt from government policies that have already been implemented in regions around the globe. As a thesis by publication, this document consists of four research articles that investigate factors affecting the EEV market, specifically in terms of: consumer demand, vehicle usage and product pricing. A number of other demographic and economic factors have also been examined, including the role of economies-of-scale.
Att uppmuntra ökad användningen av energieffektiva fordon (EEVs) är en strävan av avgörande betydelse i en tid då vi konfronteras med de allt mer ödesdigra konsekvenserna av människors påverkan på vår planet, i dag och för kommande generationer. Transportsektorn är en av de sektorer som bidrar mest till utsläppen av antropogena växthusgaser. Utsläpp från transportsektorn bidrar även till ett stort antal dödsfall varje år. Med tanke på vikten av dessa frågor är det viktigare än någonsin för beslutsfattare och forskare att bidra till en samhällsövergång mot mer hållbara livsstilar. Transporter är avgörande i denna omvandling. Eftersom transporter är en tjänst som alla människor utnyttjar i stort sett varje dag, erbjuder transportsektorn en unik möjlighet till beteendeförändringar. Genom effektiva och målinriktade åtgärder kan konsumenter ges incitament att göra mer hållbara transportval och överväga konsekvenserna av sina handlingar. Främst bland dessa initiativ är en uppmuntran till en övergång mot mer energieffektiva fordon. Denna avhandling har tagits fram i syfte att belysa frågeställningar som berör denna övergång. För framför allt beslutsfattare innehåller avhandlingen en rad rekommendationer baserade på såväl rådande forskningsresultat från aktuell forskningslitteratur som nya resultat från denna forskningsinsats. Dessa inkluderar erfarenheter från redan implementerade politiska åtgärder från regioner runt om i världen. Denna sammanläggningsavhandling består av fyra forskningsartiklar som undersöker faktorer som påverkar EEV-marknaden vad gäller konsumentefterfrågan, fordonsanvändning och produktprissättning. Utöver dessa har även ett antal andra demografiska och ekonomiska faktorer, inklusive betydelsen av stordriftsfördelar, undersökts.

QC 20160503

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Hsu, Edward Hsuan-Wei. "ELECTRIFICATION OF THE SWEDISH VEHICLE FLEET: CHARGING DEMAND AND THE POWER SYSTEM." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448286.

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With the transport sector switching to electric energy to reduce greenhouse gas emission, the supply and demand in the energy system are impacted by this transition. Meanwhile, there are not a lot of studies focus on the electrification of the vehicle fleet in Sweden. To fill up the knowledge gap, the paper aims to identify the total required electrical energy and power for the electrification of the vehicle fleet in Sweden. This includes switching passenger vehicles, light and heavy trucks, and buses to battery electric vehicles. An Electric Vehicle Power Demand Model is designed to answer the research question. It is a simplified model that can calculate energy consumption and power demand from an electric vehicle fleet. To simulate the charging schedule, four scenarios are created with differences in charge speed and the use of smart or unregulated charging. Based on the model, the electric vehicle fleet consumes 20.4 TWh of electricity per year, accounting for 14.7% of total demand in Sweden. Combing the vehicle fleet with other energy services, an average hourly peak load of 16.2 GW in summer and 24.3 in winter can be seen, while the available capacity in Sweden is around 27.1. The result indicates that the current Swedish energy system is capable of handling demand from charging the electric vehicle fleet in terms of power capacity for most times. However, undersupply may happen in some extreme condition during the winter due to higher consumption from other energy services. Furthermore, with the increasing share of renewable power in the system, the availability of these power plants can have a direct impact on the supply. This requires smart charging to shift the charging events to prevent peak hours, which can potentially decrease the peak loads up to 2 GW in EV charging demand during peak hours. However, the actual effect of it still requires more study. Lastly, the model created for the research can be used as a research or decision-making tool to estimate the impact of a group of electric vehicles in the future, therefore, contribute to the development of the sustainable energy transition.

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Howerter, Sarah E. "Modeling Electric Vehicle Energy Demand and Regional Electricity Generation Dispatch for New England and New York." ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1133.

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The transportation sector is a largest emitter of greenhouse gases in the U.S., accounting for 28.6% of all 2016 emissions, the majority of which come from the passenger vehicle fleet [1,2]. One major technology that is being investigated by researchers, planners, and policy makers to help lower the emissions from the transportation sector is the plug-in electric vehicle (PEV). The focus of this work is to investigate and model the impacts of increased levels of PEVs on the regional electric power grid and on the net change in CO2 emissions due to the decrease tailpipe emissions and the increase in electricity generation under current emissions caps. The study scope includes all of New England and New York state, modeled as one system of electricity supply and demand, which includes the estimated 2030 baseline demand and the cur- rent generation capacity plus increased renewable capacity to meet state Renewable Portfolio Standard targets for 2030. The models presented here include fully electric vehicles and plug-in hybrids, public charging infrastructure scenarios, hourly charging demand, solar and wind generation and capacity factors, and real-world travel derived from the 2016-2017 National Household Travel Survey. We make certain assumptions, informed by the literature, with the goal of creating a modeling methodology to improve the estimation of hourly PEV charging demand for input into regional electric sector dispatch models. The methodology included novel stochastic processes, considered seasonal and weekday versus weekend differences in travel, and did not force the PEV battery state-of-charge to be full at any specific time of day. The results support the need for public charging infrastructure, specifically at workplaces, with the “work” infrastructure scenario shifting more of the unmanaged charging demand to daylight hours when solar generation could be utilized. Workplace charging accounted for 40% of all non-home charging demand in the scenario where charging infrastructure was “universally” available. Under the increased renewable fuel portfolio, the reduction in average CO2 emissions ranged from 90 to 92% for the vehicles converted from ICEV to PEV. The total emissions reduced for 15% PEV penetration and universally available charging infrastructure was 5.85 million metric tons, 5.27% of system-wide emissions. The results support the premise of plug-in electric vehicles being an important strategy for the reduction of CO2 emissions in our study region. Future investigation into the extent of reductions possible with both the optimization of charging schedules through pricing or other mechanisms and the modeling of grid level energy storage is warranted. Additional model development should include a sensitivity analysis of the PEV charging demand model parameters, and better data on the charging behavior of PEV owners as they continue to penetrate the market at higher rates.

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Rey, Diana. "A Gasoline Demand Model for the United States Light Vehicle Fleet." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2351.

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The United States is the world's largest oil consumer demanding about twenty five percent of the total world oil production. Whenever there are difficulties to supply the increasing quantities of oil demanded by the market, the price of oil escalates leading to what is known as oil price spikes or oil price shocks. The last oil price shock which was the longest sustained oil price run up in history, began its course in year 2004, and ended in 2008. This last oil price shock initiated recognizable changes in transportation dynamics: transit operators realized that commuters switched to transit as a way to save gasoline costs, consumers began to search the market for more efficient vehicles leading car manufactures to close 'gas guzzlers' plants, and the government enacted a new law entitled the Energy Independence Act of 2007, which called for the progressive improvement of the fuel efficiency indicator of the light vehicle fleet up to 35 miles per gallon in year 2020. The past trend of gasoline consumption will probably change; so in the context of the problem a gasoline consumption model was developed in this thesis to ascertain how some of the changes will impact future gasoline demand. Gasoline demand was expressed in oil equivalent million barrels per day, in a two steps Ordinary Least Square (OLS) explanatory variable model. In the first step, vehicle miles traveled expressed in trillion vehicle miles was regressed on the independent variables: vehicles expressed in million vehicles, and price of oil expressed in dollars per barrel. In the second step, the fuel consumption in million barrels per day was regressed on vehicle miles traveled, and on the fuel efficiency indicator expressed in miles per gallon. The explanatory model was run in EVIEWS that allows checking for normality, heteroskedasticty, and serial correlation. Serial correlation was addressed by inclusion of autoregressive or moving average error correction terms. Multicollinearity was solved by first differencing. The 36 year sample series set (1970-2006) was divided into a 30 years sub-period for calibration and a 6 year "hold-out" sub-period for validation. The Root Mean Square Error or RMSE criterion was adopted to select the "best model" among other possible choices, although other criteria were also recorded. Three scenarios for the size of the light vehicle fleet in a forecasting period up to 2020 were created. These scenarios were equivalent to growth rates of 2.1, 1.28, and about 1 per cent per year. The last or more optimistic vehicle growth scenario, from the gasoline consumption perspective, appeared consistent with the theory of vehicle saturation. One scenario for the average miles per gallon indicator was created for each one of the size of fleet indicators by distributing the fleet every year assuming a 7 percent replacement rate. Three scenarios for the price of oil were also created: the first one used the average price of oil in the sample since 1970, the second was obtained by extending the price trend by exponential smoothing, and the third one used a longtime forecast supplied by the Energy Information Administration. The three scenarios created for the price of oil covered a range between a low of about 42 dollars per barrel to highs in the low 100's. The 1970-2006 gasoline consumption trend was extended to year 2020 by ARIMA Box-Jenkins time series analysis, leading to a gasoline consumption value of about 10 millions barrels per day in year 2020. This trend line was taken as the reference or baseline of gasoline consumption. The savings that resulted by application of the explanatory variable OLS model were measured against such a baseline of gasoline consumption. Even on the most pessimistic scenario the savings obtained by the progressive improvement of the fuel efficiency indicator seem enough to offset the increase in consumption that otherwise would have occurred by extension of the trend, leaving consumption at the 2006 levels or about 9 million barrels per day. The most optimistic scenario led to savings up to about 2 million barrels per day below the 2006 level or about 3 millions barrels per day below the baseline in 2020. The "expected" or average consumption in 2020 is about 8 million barrels per day, 2 million barrels below the baseline or 1 million below the 2006 consumption level. More savings are possible if technologies such as plug-in hybrids that have been already implemented in other countries take over soon, are efficiently promoted, or are given incentives or subsidies such as tax credits. The savings in gasoline consumption may in the future contribute to stabilize the price of oil as worldwide demand is tamed by oil saving policy changes implemented in the United States.
M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering MS

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Whitehead, Jake Elliott. "The expected and unexpected consequences of implementing energy efficient vehicle incentives." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/84928/12/84928%20Jake%20Whitehead%20Thesis.pdf.

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Completed as part of a Joint PhD program between Queensland University of Technology and the Royal Institute of Technology in Stockholm, Sweden, this thesis examines the effects of different government incentive policies on the demand, usage and pricing of energy efficient vehicles. This study outlines recommendations for policy makers aiming to increase the uptake of energy efficient vehicles. The study finds that whilst many government incentives have been successful in encouraging the uptake of energy efficient vehicles, policy makers need to both recognise and attempt to minimise the potential unintended consequences of such initiatives.

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Han, Xue. "Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks." Thesis, KTH, Industriella informations- och styrsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98484.

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There has been a large body of statements claiming that the large scale deployment of Distributed Energy Resources (DERs) will eventually reshape the future distribution grid operation in numerous ways. However, there is a lack of evidence specifying to what extent the power system operation will be alternated. In this project, quantitative results in terms of how the future distribution grid will be changed by the deployment of distributed generation, active demand and electric vehicles, are presented. The quantitative analysis is based on the conditions for both a radial and a meshed distribution network. The input parameters are on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. The simulation results indicate that the deployment of DERs can significantly reduce the power losses and voltage drops by compensating power from the local energy resources, and limiting the power transmitted from the external grid. However, it is notable that the opposite results (e.g., severe voltage uctuations, larger power losses) can be obtained due to the intermittent characteristics of DERs and the irrational management of different types of DERs in the DNs. Subsequently, this will lead to challenges for the Distribution System Operator (DSO).

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Dini, Alina L. "Influence of new car buyers' purchase experience on plug-in electric vehicle demand." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/116541/1/Alina_Dini_Thesis.pdf.

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Plug-in electric vehicles (PEVs) are one new technology which offers promise for transport sustainability and improving energy efficiency. Global enthusiasm for PEVs has spurred broad-reaching interest, but for jurisdictions where PEV policies are absent, as in Australia, consumer adoption continues to be low. Research into the barriers of adoption for PEVs often identifies cost and lack of infrastructure as key barriers, but consumer's purchase experience plays a pivotal role in technology adoption. This research will help the PEV industry and governments to understand how critical the consumer purchase experience is to overall market success.

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Sehar, Fakeha. "An Approach to Mitigate Electric Vehicle Penetration Challenges through Demand Response, Solar Photovoltaics and Energy Storage Applications in Commercial Buildings." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/86654.

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Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses.
Ph. D.

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Rue, Timothy James. "Modular Vehicle Design Concept." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51219.

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Outlined herein is the Modular Vehicle [MODV] concept as a cost effective, utilitarian, and highly functional vehicle concept for the changing demands placed on a MAGTF [Marine Air-Ground Task Force] or SP-MAGTF [Special Purpose Marine Air-Ground Task Force] in the 21st century. A large focus is put on the importance of modularity and cost effectiveness of having a 24 hour configurable vehicle to a specific mission and area of operation. Off-road vehicle progression through history is presented and successful design features are noted in order to develop underlying goals for the modular vehicle. The thesis emphasizes recent technology advancements that can shift the foundations of vehicle design including wheel hub motors, high capacity batteries, solid oxide fuel cells, autonomy, structural health monitoring, energy harvesting shock absorbers, non-pneumatic tires, and drive-by-wire options. Predictions on the outlook for the technology progressions is discussed to give insight into the viability of basing a vehicle concept on these technologies. Finally, physical design bounds are presented to provide a foundation for the future design of such a vehicle.
Master of Science

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Magnusson, Mimmi. "Energy systems studied of biogas : Generation aspects of renewable vehicle fuels in the transport system." Doctoral thesis, KTH, Energiprocesser, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105120.

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The transport sector is seen as particularly problematic when concerns about climate change and dependency on fossil energy are discussed. Because of this, bioenergy is strongly promoted for use in the transport sector, both on a European level and nationally in Sweden. Even though bioenergy is considered one of the key solutions, it is generally agreed that both supply- and demand-side measures will be needed to achieve a change to a more sustainable transport system. One of the reasons for this is the limited availability of biomass, especially agricultural feedstocks competing with food or feed production. Woody biomass, however more abundant, is also exposed to tough competition from other sectors. In this thesis, the role of biogas as a vehicle fuel in a future sustainable transport system is discussed together with the prerequisites needed to realise such a transport system. Biogas is a biofuel that could be produced in several different ways: by anaerobic digestion, which is a first-generation production route, by gasification, which is a second-generation process, and by catalytic reduction of carbon dioxide, a third-generation technology. The main focus in this thesis is on biogas produced by anaerobic digestion and the results show that there is a significant potential for an increase compared to today’s production. Biogas from anaerobic digestion, however, will only be able to cover a minor part of the demand in the Swedish transport sector. Considering biogas of the second and third generations, the potential for production is more uncertain in a mid-term future, mainly due to competition for feedstock, the possibility to produce other fuels by these processes, and the present immaturity of the technology. The limited potential for replacing fossil vehicle fuels, either by biogas or other renewable fuels, clearly shows the need for demand-side measures in the transport system as well. This thesis shows the importance of technical and non-technical means to decrease the demand for transport and to make the transport as efficient as possible. The results show that both energy-efficient vehicles and behavioural and infrastructural changes will be required. Policies and economic incentives set by governments and decision-making bodies have a prominent role to play, in order to bring about a shift to a more sustainable transport system, however, measures taken on individual level will also have a great impact to contribute to a more sustainable transport system.

QC 20121116

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Nygren, Johan. "On the impact of noise and energy demand from traffic : An assessment using microscopic modelling." Licentiate thesis, KTH, Marcus Wallenberg Laboratoriet MWL, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292360.

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Noise emissions from transportation remain one of the greatest environmental issues of modern day. Inhabitants in urban environments are especially exposed, with almost 80 million people in the European Union exposed to noise levels exceeding the recommended limits set by the World Health Organization (WHO). While the health-related effects from exposure of traffic noise are problematic and of utmost importance to reduce, availability to efficient transport is also an essential necessity. These conflicting requirements on transportation calls for a more holistic approach to traffic analysis, and to understand the relation between these effects from the traffic. This work investigates properties of traffic, such as the exposure of noise emissions, the vehicle-specific energy demand and duration, to analyse the sustainability of transport. The traffic simulation software SUMO is used to provide a discrete traffic model with individual vehicles, combined with the European vehicle noise source model IMAGINE used to model discrete sound sources that allow for directivity in the sound field and is speed- as well as acceleration-dependent. The resulting cost related to the exposure of noise is then evaluated at several measurement points in the network using a willingness-to-pay (WTP) model. This allows for an analysis of the relation between noise exposure cost and energy efficiency through the estimation of the vehicle-specific energy demand. A time-varying traffic demand is added to analyse the effects of a varying traffic density and congestion to the relation between the different properties. Additionally, the concept of allocating the noise exposure cost down to individual vehicles by means of contributed acoustic energy is expanded to take the main contributing vehicles and time-segments into consideration, and to allow for a non-linear weighting factor. These allocation strategies also allow for a bias to assign a higher cost to noisier vehicles, as vehicles that contribute more to the overall noise exposure than others may be more easily identified. Lastly, the relation between the traffic properties are analysed by means of correlation. Initial studies indicate that the correlation is dependent on the traffic density and the amount of vehicle interaction.
Trafikbuller är en av de största miljöproblemen idag. Invånare i stadsmiljöer är särskilt utsatta, där nära 80 miljoner personer i Europeiska Unionen är utsatta för bullernivåer som överskrider Världshälsoorganisationens (WHO) angivna gränsvärden. Medan de hälso-relaterade effekterna från exponering av buller är på en ohållbar nivå och bör reduceras, så är även tillgänglighet till effektiv transport en nödvändighet. Dessa motstridiga krav på transportnätverket kräver en mer holistisk syn på trafikanalys, för att förstå relationen mellan dessa effekter från trafiken. Detta arbete undersöker de effekter som uppstår från trafiken, såsom bullerexponering, det fordons-specifika energibehovet och tiden i trafiken, för att analysera hållbarhetsaspekter för transporter. I detta arbete används trafiksimuleringsprogrammet SUMO för att erhålla en diskret trafikmodell med individuella fordon, i kombination med den europeiska fordonsbullermodellen IMAGINE som används för att modellera diskreta bullerkällor som tar hänsyn till direktivitet i ljudfältet samt fart- och accelerationsberoende. Den resulterande kostnaden för bullerexponeringen beräknas därefter för ett stort antal mätpunkter i nätverket genom en modell för betalningsvilja (WTP). Detta tillåter en analys av förhållandet mellan kostnad från bullerexponering och energieffektivitet baserat på det fordonsspecifika energibehovet. Ett tidsvarierande trafikflöde läggs på för att analysera effekterna från en varierande trafiktäthet och trängsel på förhållandet mellan de olika egenskaperna. Dessutom expanderas konceptet att allokera den buller-relaterade kostnaden ner till enskilda fordon baserat på deras enskilda bullerbidrag. Detta för att potentiellt kunna allokera en större del av den totala kostnaden till fordon som bidrar särskilt mycket till den totala bullernivån eller till särskilda tidsegment med höga bullernivåer, samt att tillåta en ickelinjär viktfunktion. Dessa allokeringsstrategier ger också möjligheten att allokera en högre kostnad till bullriga fordon, då fordon som bidrar mer till den totala bullernivån kan lättare identifieras. Slutligen analyseras förhållandet mellan trafikegenskaperna utifrån korrelation. Inledande studier visar att korrelationen beror på trafiktätheten och mängden fordonsinteraktion i trafiken.

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Zaini. "Control strategies for blended braking in road vehicles : a study of control strategies for blended friction and regenerative braking in road vehicles based on maximising energy recovery while always meeting the driver demand." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/7341.

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In HEV and EV, higher fuel economy is achieved by operating the ICE and electric motor in the most efficient region and by using regenerative braking. Such a braking system converts, transfers, stores and reuses kinetic energy which would otherwise be dissipated as heat through friction brakes to the environment. This research investigates the control of braking for a mixed-mode braking system in a these vehicles based on the proportion of braking energy that can be stored. Achieving mixed-mode braking requires the ‘blending’ of the two systems (regenerative and friction), and in brake blending, the electric motor/generator (M/G) and the hydraulic actuation pressure are controlled together to meet the driver’s braking demand. The research presented here has established a new robust dynamic modelling procedure for the design of combined regenerative and hydraulic braking systems. Direct torque control and pressure control were selected as the control criteria in both brakes. Two simulation models have been developed in Matlab/Simulink to generate analysis the performance of the control strategy in the blended braking system. Integration of the regenerative braking system with ABS has also been completed, based on two conditions, with and without the deactivation of the regenerative braking. Verification of the models is presented, based on experimental work on two EVs manufactured by TATA Motors; the ACE light commercial vehicle and the VISTA small passenger car. It is concluded that braking demand and vehicle speed determine the operating point of the motor/generator and hence the regenerative braking ratio.

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Parry, Emily. "Electricity load estimation and management for plug-in vehicle recharging on a national scale prior to the development of third party monitoring and control mechanisms." Thesis, University of Bath, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.606668.

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In accordance with the main aim of the study, a widely accessible, modifiable tool was created for parties interested in maintaining the national electricity supply network and parties interested in informing policy on plug-in vehicle adoption schemes and recharging behaviour control. The Parry Tool enables the user to incorporate present limits to plug-in vehicle recharging demand scheduling as imposed by the state of present technology (no third party mechanism for monitoring and control of recharging), present human travel behaviour needs and existing patterns in electricity usage; into the investigation of the impacts of recharging demand impacts and the design of mitigation measures for deflecting (parrying) worst case scenarios. The second aim of the project was to demonstrate the application of the Parry Tool. The multidisciplinary/interdisciplinary information gathered by the Parry Tool was used to produce national demand profiles for plug-in vehicle recharging demand, calculated using socioeconomic and travel behaviour-estimated population sizes for plug-in eligible vehicles and vehicle usage patterns, which were added to existing national electricity demand for a chosen test week – this was the first scenario subsequently tested. The information gathered by the Parry Tool was then used to inform the design of two demand management methods for plug-in vehicle recharging: Recharging Regimes and weekly recharging load-shifting – these were the second and third scenarios subsequently tested. Unmitigated simultaneous recharging demand in scenario 1 (all vehicles assumed to recharge at home upon arrival home every day) severely exacerbated peak demand, raising it by 20% above the highest peak in existing demand for the year 2009 over half an hour from 58,554 MW to 70,012 MW – a challenge to the generation sector. This increased the difference between daily demand minima and maxima and made the new total demand have sharper peaks – a challenge for grid regulators. Recharging Regimes in scenario 2 split the estimated national plug-in vehicle populations into groups of different sizes that started recharging at different times of the day, with the word ‘regime’ being applied because the spread of start times changed over the course of the test week from workdays to weekend. This avoided exacerbation of the peak and reduced the difference between daily demand minima and maxima by raising minima, providing a load-levelling service. Scenario 3 embellished the Recharging Regimes with workday-to-weekend recharging load-shifting that therefore took better advantage of the often overlooked weekly pattern in existing demand (demand being higher on workdays than weekends), by allowing partial recharging of a segment of the plug-in vehicle population. Limited consideration of the impact of changing vehicle energy usage (for which distance travelled was assumed to proxy in this study) showed that the more vehicles used their batteries during the day, the better the levelling effect offered by Recharging Regimes. Greater utilisation of battery capacity each day, however, can also be assumed to lessen the potential for workday-to-weekend load levelling, because load-shifting depends upon vehicles being able to partially recharge or defer recharging to later days and still meet their travel needs plus keep a reserve State Of Charge (SOC) for emergency and other unplanned travel. Whilst altering vehicle energy usage did not change the finding that unmitigated simultaneous recharging exacerbated existing peak demand, it was noted that when limited mileage variation was considered this sharpened the profile of total demand – the rise and fall of the new peak far steeper than that of the original peak in existing demand. The Parry Tool combines a series of integrated methods, several of which are new contributions to the field that use UK data archives but may potentially be adapted by researchers looking at energy issues in other nations. It presents a novel fossil-fuel based justification for targeting road transport – acknowledging energy use of fossil fuel as the originator of many global and local problems, the importance of non-energy use of petroleum products and subsequent conflicts of interest for use, and a fossil fuel dependency based well-to-wheel assessment for UK road transport for the two energy pathways: electricity and petroleum products. It presents a method for the recalculation and ranking of top energy use/users using national energy use statistics that better highlights the importance of the electricity industry. It also presents the first publicly documented method for the direct consultation and extraction of vehicle-focused statistics from the people-focused National Travel Survey database, including a travel behaviour and household income-based assessment of plug-in vehicle eligibility, used to scale up to national estimates for battery electric and plug-in electric hybrid vehicle (BEV and PHEV) national population sizes. The work presented here is meant to allow the reader to perceive the potential benefits of using several resources in combination. It details the Parry Tool, a framework for doing so, and where necessary provides methods for data analysis to suit. It should however be noted that methods were kept as simple as possible so as to be easily followed by non-specialists and researchers entering the field from other disciplines. Methods are also predominantly data-exploratory in nature: strong conclusions therefore should not be drawn. Rather, the work here should be seen as a guideline for future work that may more rigorously study these combined topics and the impacts they may have upon plug-in vehicle ownership, usage behaviour, impacts of recharging upon the national network and the design of mitigation measures to cope with this new demand.

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Busuladzic, Ishak, and Marcus Tjäder. "Performance Indicators for Smart Grids : An analysis of indicators that measure and evaluate smart grids." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48902.

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Sweden has developed ambitious goals regarding energy and climate politics. One major goal is to change the entire electricity production from fossil fuels to sustainable energy sources, this will contribute to Sweden being one of the first countries in the world with non-fossil fuel in the electricity sector. To manage this, major changes need to be implemented and difficulties on the existing grid will occur with the expansion of digitalization, electrification and urbanization. By using smart grids, it is possible to deal with these problems and change the existing electricity grid to use more distributed power generation, contributing to flexibility, stability and controllability. The goal with smart grids is to have a sustainable electricity grid with low losses, security of supply, environmental-friendly generation and also have choices and affordable electricity for customers. The purpose of this project is to identify and evaluate several indicators for a smart grid, how they relate and are affected when different scenarios with different technologies are implemented in a test system. Smart grid indicators are quantified metrics that measure the smartness of an electrical grid. There are five scenarios where all are based on possible changes in the society and electricity consumption, these scenarios are; Scenario A – Solar power integration, Scenario B – Energy storage integration, Scenario C – Electric vehicles integration, Scenario D – Demand response and Scenario E – Solar power, Energy storage, Electric vehicles and Demand response integration. A model is implemented in MATLAB and with Monte Carlo simulations expected values, standard deviation and confidence interval were gained. Four selected indicators (Efficiency, capacity factor, load factor and relative utilization) was then analyzed. The results show that progress on indicators related to all smart grid characteristics is needed for the successful development of a smart grid. In scenario C, all four selected indicators improved. This shows that these indicators could be useful for promoting the integration of electric vehicles in an electricity grid. In Scenario A, solar power integration contributed to all indicators deteriorate, this means that, technical solutions that can stabilize the grid are necessary to implement when integrating photovoltaic systems. The load factor is a good indicator for evaluating smart grids. This indicator can incentivize for an even load and minimize the peak loads which contributes to a flexible and efficient grid. With the capacity factor, the utilization and free capacity can be measured in the grid, but it can counteract renewable energy integration if the indicator is used in regulation.

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Hamid, Qazi. "Energy demand management of electric vehicles." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/23900.

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The aim of this thesis is to investigate novel recharging schemes for energy demand management (DM) of electric vehicles (EVs). While there has been a lot of work highlighting the importance of energy DM of EVs, most of the reported works do not expand on suggesting how such a DM system may be implemented. In this thesis the focus is on two aspects of DM system implementation. At the instantaneous control time scale, an alternative mechanism for frequency regulation with the aim of neutralising sudden changes in output power of electric generators is presented. At the recharge planning time scale, the aim is to avoid congestion and undesirable voltage drops in the distribution system, and a novel approach is presented that can improve voltage profiles. The problem of considering both voltage congestion and frequency regulation in a composite DM framework is also addressed. At the instantaneous control time scale, a novel distributed recharging rate controller is presented that is based on non-linear control and that yields a real time and distributed solution. This controller minimises communication overheads and allows EVs to join and leave at arbitrary times. From the perspective of recharging rate allocation, the controller achieves a Pareto efficient allocation which is also proportionally fair. The proposed controller is then applied to a system with a single, isolated, and unregulated synchronous machine and it is shown that the frequency can be used as proxy to the imbalance between produced and consumed electric power and hence communication overhead can be eliminated in such cases. A protocol is also discussed that can modify the controller and can implement the modified controller in a multi-machine system. Simulation is used to show the frequency regulation and fairness of recharging rates of EVs when the protocol and the modified controller are used. Subsequently, the integration of the recharging rate controller with the legacy protection system is also discussed. At the recharge planning time scale, the problem of congestion in the distribution system is addressed. Most of published literature on distribution system voltage issues deals with control of various network elements, for instance, on-load tap changers or banks of shunt capacitors on the distribution feeders. In this thesis, a complementary approach is presented that can also improve voltage profile by scheduling EV load in such a manner that undesirable voltage drops are avoided or their severity is diminished. In this context, a novel approach is presented for recharging EVs in the same geographic neighbourhood that share the same secondary circuits when recharging. The approach is based on a numerical method called Smoothed Particle Hydrodynamics (SPH) that has been previously used by other researchers to solve the equations of fluid dynamics. The characteristics of the method used for the proposed approach as well as its performance in terms of improvement in the reduction of voltage drops and its adaptation to elastic and non-elastic loads is highlighted via simulation. Finally, the approach is extended to also provide a frequency control reserve service.

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Regett, Anika [Verfasser], Ulrich [Akademischer Betreuer] Wagner, Ulrich [Gutachter] Wagner, and Liselotte [Gutachter] Schebeck. "Development of Instruments for a Circular Energy Economy : Potential of the Circular Economy to Reduce the Critical Resource Demand and Climate Impact of Electric Vehicle Batteries / Anika Regett ; Gutachter: Ulrich Wagner, Liselotte Schebeck ; Betreuer: Ulrich Wagner." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1213026229/34.

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VITOLO, ROBERTO. "Reduction of fuel consumption and pollutant emissions from vehicles: implementation of low-temperature diesel combustion and development of an advanced central tire inflation system." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2755753.

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Goutham, Mithun. "Machine learning based user activity prediction for smart homes." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595493258565743.

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20

Conley, John Jason. "The role of power and energy demands in hybrid vehicles." Morgantown, W. Va. : [West Virginia University Libraries], 2002. http://etd.wvu.edu/templates/showETD.cfm?recnum=2589.

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Thesis (M.S.)--West Virginia University, 2002.
Title from document title page. Document formatted into pages; contains xi, 100 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 69-72).

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21

Iacobucci, Riccardo. "Shared Autonomous Electric Vehicles: potential for Power Grid integration." Kyoto University, 2018. http://hdl.handle.net/2433/235105.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(エネルギー科学)
甲第21385号
エネ博第373号
新制||エネ||73(附属図書館)
京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻
(主査)教授手塚哲央, 教授下田宏, 准教授 MCLELLAN,Benjamin
学位規則第4条第1項該当

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22

Riccardo, Iacobucci. "Shared Autonomous Electric Vehicles: potential for Power Grid integration." Doctoral thesis, Kyoto University, 2009. http://hdl.handle.net/2433/235105.

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京都大学
0048
新制・課程博士
博士(エネルギー科学)
甲第21385号
エネ博第373号
新制||エネ||73(附属図書館)
京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻
(主査)教授手塚哲央, 教授下田宏, 准教授 MCLELLAN,Benjamin
学位規則第4条第1項該当
Doctor of Energy Science
Kyoto University
DFAM

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23

Tsivras, Sotirios-Ilias. "Load Demand Forecasting : A case study for Greece." Thesis, Högskolan i Gävle, Energisystem och byggnadsteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-29841.

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It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption. This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets. By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030. On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run. Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles. The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time. A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.

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24

Ekström, Amelie, and Jessica Wänlund. "Nätanslutning av en framtida elväg : En kartläggning av anslutningsmöjligheter för E4an mellan Gävle och Stockholm." Thesis, Uppsala universitet, Elektricitetslära, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-439941.

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The transport sector accounts for a third of Sweden’s total greenhouse gas emissions where cars and heavy trucks dominate the use of fossil fuels. The Swedish government is now intensifying the work for an electrified transport sector where electric roads could be an important part. Electric roads enable heavy vehicles to charge their batteries while driving, which is expected to contribute to environmentally friendly and time-efficient freight transports. To implement electric roads, availability of electric power along the electric roads will be required. This study presents a plan for connecting an electric road to the electricity grid in the electricity network area of Vattenfall Eldistribution. From the results, the idea was to present general conclusions from the experiences of the study, that could contribute in further implementation of electric roads. The road that has been selected for the study was the E4 between Gävle and Stockholm. A model for calculating the power demand along the electric road has been modeled and connection possibilities to transformer stations has been investigated. The analysis was based on three scenarios where different degrees of strengthening of the existing electricity network were assumed. In addition, a forecast for 2030 and a cost estimation for each scenario has been carried out. The result of the study indicates that for road sections close to larger cities, there are a larger number of connection options in comparison to rural areas. Furthermore, the designed solution in the study required strengthening of the electricity grid and the investment cost was 362 million Swedish crowns.

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Wänlund, Jessica, and Amelie Ekström. "Nätanslutning av en framtida elväg : En kartläggning av anslutningsmöjligheter för E4an mellan Gävle och Stockholm." Thesis, Uppsala universitet, Institutionen för fysik och astronomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-439942.

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The transport sector accounts for a third of Sweden’s total greenhouse gas emissions where cars and heavy trucks dominate the use of fossil fuels. The Swedish government is now intensifying the work for an electrified transport sector where electric roads could be an important part. Electric roads enable heavy vehicles to charge their batteries while driving, which is expected to contribute to environmentally friendly and time-efficient freight transports. To implement electric roads, availability of electric power along the electric roads will be required. This study presents a plan for connecting an electric road to the electricity grid in the electricity network area of Vattenfall Eldistribution. From the results, the idea was to present general conclusions from the experiences of the study, that could contribute in further implementation of electric roads. The road that has been selected for the study was the E4 between Gävle and Stockholm. A model for calculating the power demand along the electric road has been modeled and connection possibilities to transformer stations has been investigated. The analysis was based on three scenarios where different degrees of strengthening of the existing electricity network were assumed. In addition, a forecast for 2030 and a cost estimation for each scenario has been carried out. The result of the study indicates that for road sections close to larger cities, there are a larger number of connection options in comparison to rural areas. Furthermore, the designed solution in the study required strengthening of the electricity grid and the investment cost was 362 million Swedish crowns.

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26

Joševski, Martina Verfasser], Dirk [Akademischer Betreuer] [Abel, and Jakob Lukas [Akademischer Betreuer] Andert. "Predictive energy management of hybrid electric vehicles with uncertain torque demand forecast for on-road operation / Martina Joševski ; Dirk Abel, Jakob Lukas Andert." Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1189332698/34.

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27

Mattlet, Benoit. "Potential benefits of load flexibility: A focus on the future Belgian distribution system." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/271127/5/contratBM.pdf.

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Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

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Choma, Ernani Francisco. "Avaliação de impactos ambientais da oferta e demanda de energia para automóveis no Brasil utilizando avaliação do ciclo de vida." Universidade Tecnológica Federal do Paraná, 2014. http://repositorio.utfpr.edu.br/jspui/handle/1/1355.

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CAPES
Veículos elétricos (VEs) são vistos como uma das potenciais soluções para os problemas ambientais associados com os veículos de combustão interna (VCIs). VEs, no entanto, também causam impactos ambientais ao longo do ciclo de vida e aumento no consumo de eletricidade, sendo também necessário incluí-los no planejamento energético. Estudos de Avaliação do Ciclo de Vida (ACV) realizados para avaliar os impactos ambientais de VEs em outros países mostram que a fonte de eletricidade é significativa para determinar se estes apresentam melhores resultados que VCIs. O caso brasileiro, contudo, pode apresentar resultados diferentes, em função da matriz elétrica diferente e da possibilidade de utilização de etanol pelos VCIs. O objetivo geral deste estudo é identificar como atender a demanda por transporte de pessoas por automóveis com menor impacto ambiental, para diferentes categorias de impacto, no Brasil. Para realizar o estudo, foram realizadas quatro etapas: (i) definir dados gerais, como o ano de projeção; (ii) identificar a frota de automóveis e a respectiva demanda energética e opções de atendimento; (iii) realizar ACV por veículo; e (iv) avaliar o impacto ambiental da frota, comparando e selecionando opções de menor impacto. Foram considerados dois horizontes de tempo: 2022 e 2030. Foi utilizada a base de dados de inventário do ciclo de vida (ICV) do ecoinvent v.3.01, com adaptações para o contexto brasileiro utilizando dados de literatura. Especificamente na definição da fonte de eletricidade, utilizou-se a abordagem decisional de ICV para 2022, enquanto que, para 2030, foram definidas possíveis tecnologias marginais/incrementais. Utilizou-se o método de avaliação do impacto do ciclo de vida CML 2000 v.2.05, sendo que para 2030 foram selecionadas três categorias (depleção de recursos abióticos; aquecimento global; depleção da camada de ozônio), em conjunto com resultados de ICV para ocupação da terra. Para 2030, utilizou-se, também, em análise de sensibilidade, o método EDIP 2003 v.1.04. Esses métodos estão disponíveis no software SimaPro v.8.0.2, utilizado para os cálculos. Os resultados para 2022 mostraram que, com recarga em horários de menor demanda, o VE é melhor para algumas categorias e o VCI melhor para outras. Já para 2030, contudo, uma inclusão de aproximadamente 35% de VEs na frota pode reduzir bastante os impactos para as três categorias do método CML e para o indicador único do método EDIP, embora cause aumento significativo na área ocupada. Deste modo, esses resultados poderiam ser comparados com outros tipos de ocupação da terra que visem reduzir impactos ambientais. Conclui-se que os VEs têm potencial de grande redução de impactos ambientais, de forma que poderiam ser alvos de políticas públicas que visem reduzir tais impactos. Entre as incertezas do estudo incluem-se: a simplificação na identificação das fontes de energia; a utilização de parâmetros médios para os veículos, em parte relativos apenas a veículos novos; a definição da tecnologia marginal/incremental apenas para a eletricidade; a não consideração de outras tecnologias, como VCIs a etanol de segunda e terceira geração; e a utilização de dados de inventário do presente ou passado para estimar condições futuras. Estas poderiam ser tratadas em trabalhos futuros.
Electric Vehicles (EVs) are seen as a potential solution for the environmental problems associated with Internal Combustion Engine Vehicles (ICEVs). EVs, on the other hand, also cause environmental impacts throughout their life cycles and increases in the demand for electricity, so that they need to be included in the energy planning. Life Cycle Assessment (LCA) studies carried out to assess the environmental impacts of EVs in other countries, indicated that the electricity source is significant to determine whether these present better results than ICEVs or not. For Brazil, however, results might be different, due to the different electricity mix and to the possibility of ICEVs to use sugarcane ethanol. The main purpose of this study is to analyze how to meet the demand for passenger transport by automobiles in Brazil with a smaller environmental impact, for different impact categories. In order to attain this objective, four steps were executed: (i) to define general data, such as the year of projection; (ii) to identify the automobile fleet and the respective energy demand and fulfillment options; (iii) to perform LCA, per vehicle; and (iv) to assess the environmental impact of the fleet, comparing and selecting options with smaller impact. Two time horizons were considered: 2022 and 2030. The ecoinvent database v.3.01 was used as the Life Cycle Inventory (LCI) data source, with some adaptations for the Brazilian context using literature data. Specifically for the identification of the electricity source, the decisional LCI approach was used, for 2022, while, for 2030, possible marginal/incremental technologies were identified. The Life Cycle Impact Assessment (LCIA) method CML 2000 v.2.05 was used, from which three categories were chosen for 2030 (abiotic resource depletion; climate change; and ozone layer depletion), together with LCI results for land occupation. The single score of the LCIA method EDIP 2003 v.1.04 was used in a sensitivity analysis. These methods are available in SimaPro 8.0.2, used for calculations. The results for 2022 showed that, with battery charge during off-peak hours, the EV is better in some impact categories, while the ICEV is better in others. For 2030, however, an EV market penetration of approximately 35% can significantly reduce the impacts for the three CML categories and for the EDIP single score, albeit causing a significant increase in land occupation. These results, therefore, can be compared with other types of land occupations which aim to reduce environmental impacts. It was concluded that EVs have good environmental impacts reduction potentials, in a way that they could be targeted by public policies that address such impacts. Among the uncertainties of this study are included: the simplified identification of energy sources; the use of average parameters for the transportation sector, in part associated only with new vehicles; the definition of the marginal/incremental technology only for electricity; the fact that other vehicle technologies, such as ICEVs powered by second and third generation ethanol; and the use of present or past LCI data to assess future conditions. These could be dealt with in future studies.

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Maninnerby, Henrik. "Simulering av ett elnät med hänsyn till förnybar energi : En studie av möjliga lösningar på problemet med fler elbilar i elnätet." Thesis, Karlstads universitet, Institutionen för ingenjörsvetenskap och fysik (from 2013), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-68306.

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The purpose of this thesis is to simulate different scenarios where all vehicles are replaced by electric vehicles in the future. An addition of electric vehicles to the electric grid is not easy to handle, especially not if the consumers choose to charge their vehicles with higher power. As can be seen in the results of this report, flexible demand through power management is a good option to reduce overall power consumption. Of course, customers will have to agree to that their consumption is controlled in this manner. Using solar cells during the summer works well, but it’s harder wintertime as the largest electric demands arise during the night, when there is close to no sunlight. However, in the case of batteries, they may be helpful. Possibly by letting the solar cells charge the battery during the day and then use it to help charge the electric vehicle during the night. Unfortunately, it was not possible to include this scenario as the used software was unable to handle batteries in that way. By completely replacing the heat source, in this case with district heating, and thus releasing available power in the transformer, turned out to be the best option. In this way, virtually all predicted amounts of electric cars could be charged with optional charging strength between 3.7 and 11 kW. As for the T422 transformer, there is no choice but to replace it if a larger number of electric vehicles wants to start charging there. At present, it can handle a maximum of 10-20 electric vehicles that charge at the lowest power as there is only about 50 kW available there winter time. District heating cannot be applied either as the connected customers do not use electric heating.
Detta arbete har till syfte att simulera olika scenarier i ett område där samtliga fordon ersätts av elbilar i framtiden. En tillkomst av elbilar i elnätet är inte lätt att hantera, speciellt inte om kunderna i nätet väljer att ladda med högre effekt. Vilket kan ses i resultatet i denna rapport, är flexibel förbrukning genom effektstyrning ett bra alternativ för att sänka den totala effektförbrukningen. Fast det gäller då givetvis att kunderna går med på att förbrukningen styrs på detta sätt. Att använda solceller fungerar bra sommartid, men vintertid är det värre, då det största elbehovet uppstår på natten, när det inte är lika stor solljustillgång. I samband med batterier kan de dock vara till hjälp. Möjligen genom att låta solcellerna ladda upp batteriet under dagen och sedan använda det för att hjälpa till att ladda elbilen under natten. Dessvärre var det inte möjligt att simulera detta scenario på grund av brister i mjukvaran som användes. Att helt ersätta värmekällan, i detta fall med fjärrvärme, och därmed frigöra effekt i transformatorn visade sig däremot vara det bästa alternativet. På så sätt kunde i stort sett samtliga mängder elbilar laddas med valfri laddningsstyrka mellan 3,7 och 11 kW. Vad gäller transformator T422, ses inget annat val än att byta ut den om ett flertal elbilar ska börja ladda där. I nuläget klarar den av högst 10-20 elbilar som laddar på lägsta effekt eftersom det endast finns ca 50 kV tillgängligt vintertid. Fjärrvärme kan inte användas där heller som komplement då de tillkopplade kunderna inte använder elektrisk uppvärmning.

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VALLERO, GRETA. "Green Mobile Networks: from 4G to 5G and Beyond." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2960753.

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31

Said, Dhaou. "Modèles et protocoles pour les interactions des véhicules électriques mobiles avec la grille." Thèse, Université de Sherbrooke, 2014. http://hdl.handle.net/11143/6055.

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Dans de proches années, les véhicules électriques (VEs) vont faire leur apparition massive sur les marchés. Cela peut avoir un impact important sur le fonctionnement des réseaux d’électricité actuels qui devront ajuster leur fonctionnement à la nouvelle demande massive d'électricité provenant des VEs. Par contre, les VEs peuvent aussi être vus comme une nouvelle opportunité dans le futur marché d’électricité. En effet, une décharge/recharge intelligente peut permettre aux VEs d’être un support de stockage d’électricité important, valable, et permanent dont la capacité croit en fonction du nombre des VEs. Ce projet a comme objectifs de : (1) proposer un schéma d’interaction V2G (Vehicle-to-Grid) intégrant des techniques permettant de : (a) adapter le fonctionnement de la grille aux contraintes temporelles et spatiales relatives au processus de recharge des VEs dans un milieu résidentiel. On cherchera à satisfaire de différentes demandes en puissance des VEs branchés au secteur sans trop stresser la grille intelligente, (b) optimiser les opérations de chargement/déchargement entre les VEs et la grille dans les deux sens. (2) Proposer de nouveaux schémas de communication sans fil, entre les VEs et la grille intelligente loin des bornes de recharge, qui soient basés sur les standards de communications véhiculaires (VANETs) ainsi que sur d’autres standards de communication à grande échelle. On introduira des techniques d’accès à la grille intelligente pour négocier le coût de recharge/décharge des batteries, le temps d’attente du service, les emplacements et aussi pour planifier la motivation du consommateur afin de favoriser la stabilité de la grille.

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32

Gomes, Isaías da Luz Ramos. "Microrrede no âmbito da energia 4.0 face a incerteza e risco e ao mercado de eletricidade." Doctoral thesis, Universidade de Évora, 2021. http://hdl.handle.net/10174/29789.

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Esta tese incide sobre o tema da gestão e planeamento de operação de uma microrrede no âmbito da Energia 4.0 por parte do novo agente de mercado, dito de agregador da microrrede. O agregador da microrrede tem como objetivo a maximização do lucro esperado, resultante da participação em mercado de eletricidade. A microrrede inclui fontes de produção distribuída, nomeadamente, microturbinas, eólica, fotovoltaica, armazenamento de energia, veículos elétricos, ditas de microfontes, e o uso de energia, dito de carga. O planeamento é fundamental não só para a microrrede ser um sistema sustentável de energia, mas também para a participação de fontes de energia renováveis integradas em microrrede numa matriz energética mais ampla. É necessário face à incerteza e risco e ao mercado de eletricidade a tomada de decisões apoiadas por um sistema de suporte e informação, que constitui o sistema de suporte de gestão da microrrede, que é vital para a sustentabilidade deste agente de mercado. O sistema de suporte de gestão da microrrede em estudo é baseado na formulação de um problema de programação estocástica linear inteira mista que depende do conhecimento dos processos estocásticos que descrevem os parâmetros incertos. Os parâmetros incertos são descritos por um conjunto de cenários plausíveis apropriados à representação dos eventos que ocorrem na microrrede. Atendendo ao elevado grau de incerteza e às limitações computacionais os cenários são reduzidos a um número razoável de cenários representativos. Ainda, o sistema de suporte de gestão da microrrede permite ao agregador da microrrede apresentar propostas de licitação confiáveis ao considerar o risco na tomada de decisão. Por fim, casos de estudo são simulados com intuito de avaliar o desempenho da microrrede e validar a metodologia em que é baseado o sistema de suporte de gestão da microrrede nesta tese; Abstract: Microgrid under the scope of Energy 4.0 facing uncertainty and risk and electricity markets This thesis focuses on the management and operation planning of a microgrid within the scope of Energy 4.0 by the new market agent, said to be the microgrid aggregator. The microgrid aggregator's goal is to maximize the expected profit, resulting from the participation in the electricity market. The microgrid includes sources of distributed production, namely, microturbines, wind, photovoltaic, energy storage, electric vehicles, called micro-sources, and the use of energy, said to be the load. Planning is fundamental not only for the microgrid to be a sustainable energy system but also for the participation of renewable energy sources integrated into the microgrid in a broader energy matrix. It is necessary for the facing of uncertainty and risk and the electricity market to make decisions supported by a support and information system, which constitutes the microgrid support management system, which is vital for the sustainability of this market agent. The microgrid support management system under study is based on the formulation of a mixed-integer linear stochastic programming problem that depends on knowledge of the stochastic processes that describe the uncertain parameters. The uncertain parameters are described by a set of plausible scenarios suitable to represent the events that occur in the microgrid. Given the high degree of uncertainty and the computational limitations, the scenarios are reduced to a reasonable number of representative scenarios. Also, the microgrid support management system allows the microgrid aggregator to present reliable bidding proposals taking into account risk in decision making. Finally, case studies are simulated to assess the performance of the microgrid and validate the methodology on which the microgrid support management system in this thesis is based.

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33

Liu, Yizao. "Essays on fuel efficiency and vehicle demand dynamics." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-05-3154.

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Reducing automobile-based gasoline consumption has been a major U.S. public policy issue recently. A key driving force behind policymakers' desire is the concern of environmental externalities and national security. Currently, there are three public policies towards reducing automobile gasoline consumption: raising federal gasoline tax, raising the Corporate Average Fuel Economy (CAFE) Standards and vehicle scrappage subsidies of government to retirement of old vehicles. My research studies the effectiveness of these policies in the United States. Among all polices, economists often argue that higher gasoline tax would be more effective in improving fuel economy efficiency. In my first chapter, I ask how gasoline prices influence households' automobile replacement decisions and thus market fuel economy efficiency, which is measured by average mileage per gallon in a city. I specify and estimate a structural dynamic model of consumer preference for new and used vehicles following the methodology proposed by Gowrisankaran and Rysman (2009). Since gasoline costs accounts for 65% of total operating costs, the current and future gasoline price must need to be taken into consideration for rational forward-looking consumers when they are making vehicle choices. Besides, the replacement decision for vehicles is dynamic as well: facing depreciation as the automobile ages and the improving features for new products, consumers need to decide whether to replace the vehicle in the current period or later. Therefore, a dynamic model of consumer choice would be crucial to correct policy evaluation of fuel economy efficiency, while previous literature fails to consider the dynamics. By taking dynamics into consideration, I am able to capture the inherent dynamic nature of a forward-looking consumer's decision, with rational expectation on the evolution of vehicle attributes and retail gasoline prices. I estimate the model using a rich dataset combing vehicle registration data on different cities, vehicle characteristic data, average gasoline price, etc. Although a high gasoline tax is never put in practice in the U.S. and may not be political feasible, I further conduct an experiment of raising gasoline tax to test how fuel economy efficiency is affected based on my model estimates. Experiments suggest that keeping a $4 gasoline price would result in a steady trend for a city's fleet fuel efficiency increase, while doubling current rate will only increase fuel efficiency in the first several years, but experience drops over time. The Corporate Average Fuel Economy (CAFE) are regulations in the United States that intended to improve the average fuel economy of cars and light trucks sold in the US. However, it is long been realized that with a more fuel efficiency car, consumers may be induced to drive more which partially offsets the original energy saving by the policy. Therefore, to assess the effectiveness of CAFE standards, it is crucial to ask: how fuel economy efficiency, which is measured by mileage per gallon (MPG), affects households' vehicle mileage traveled and its distribution. In my second chapter, I answer the question by estimating a structural model for joint determination of vehicle fuel efficiency choice and vehicle mileage traveled each year with a detailed micro-level data of National Household Travel Survey 2001. I further study the distributional effects on vehicle miles of fuel efficiency using instrumental quantile regression. Comparison on results and tests of weak instruments between my method and literature suggest that my model and choice of instruments provide consistent estimates, while using choice probabilities as instruments is not valid. My results support some earlier findings of rebound effects with a more precise quantitative estimation. In addition, I find new evidence that costs associated with raising CAFE standards vary across different quantiles of annual mileage driven and are especially high for those with below-average vehicle mileage driven. These findings also provide rationale in support of a tax on mileage, which is more effective in reducing gasoline consumptions, comparing to the costs of CAFE standards. My third chapter focus on 2009 CARS Program (Cash-for-Clunker). The 2009 CARS program attempted to boost the sale of new fuel efficient vehicles to replace old gas guzzlers. The program established a two-tier incentive system depending on whether buyers purchased a passenger vehicle or an SUV. The result is that many of the new purchased vehicles are indeed SUVs. The CARS program collected information about the old scrapped vehicles and linked it to the actual purchase of the new vehicles. It is thus possible to analyze the effect of preference inertia in choices by comparing the characteristics of old and new vehicles. The fact that effective prices that consumers face are determined by the mileage class of the old car also allows us to evaluate the distribution of valuation trade-offs between mileage and other characteristics such as size, performance, and vehicle class. My findings suggest that the 2009 Cash-for-Clunker is not very effective in terms of affecting consumers' choice of SUVs and big cars. For transactions under the program, consumers still prefer SUVs and large cars. The extra $1000 rebates actually increase consumers' tastes towards SUVs.
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Cheng, Pei-Hsuan, and 鄭沛軒. "Demand-side Management in Residential CommunityRealizing Sharing Economy with Bidirectional Plug-inElectric Vehicle and Renewable Energy." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/49452789741836014925.

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碩士
國立臺灣大學
資訊工程學研究所
105
In smart grids, demand-side management (DSM) is one of the important function since it can reduce the total electricity cost of each customer, meanwhile, alleviate the aggregate peak-to-average ratio (PAR) subject to real-time pricing (RTP) policy. On the other hand, while bidirectional charging/discharging Plug-in Electric vehicles (PEV)become more general, the capability of storing electrical energy for load shifting and energy sharing among users may take smart grid to a next level. On the view point of a community, we design a fairness strategy to share PEV’s battery with neighbors to reduce the total electricity cost and peak to average ratio (PAR). On the other hand, we try to utilize the PEVs which parked at the commercial building during working hour to fulfill part of the building’s power consumption. In our problem formulation, each home is assumed to be connected to a renewable energy resource (e.g., photovoltaic (PV) system), be equipped with an energy storage device, and have an optional PEV with the vehicle to grid (V2G) ability, and the formulation is in terms of a multi-objective optimization cooperative game to facilitate power sharing among neighbors. As for a commercial building, we rearrange each PEV’s charging/discharging operations to reduce the total electricity cost of the building. In simulation, we ask ten people to provide their daily activity profiles to represent the residence in the community and use the hourly load profile data from Office of Energy Efficiency & Renewable Energy (EERE) for commercial sector. The results show that the proposed DSM system not only reduces the electricity cost for each household but also reduces the PAR of the community. Moreover, for the commercial building, the proper rearrangement of PEV’s behavior also helps to reduce the total electricity cost and PAR.

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(6689618), Tingmingke Lu. "ESSAYS ON THE ECONOMICS OF MOTOR VEHICLE ENERGY EFFICIENCY." Thesis, 2019.

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The purpose of this dissertation is to study the effectiveness of public policies in generating fuel savings and emissions reductions. I focus on applying various empirical methods to analyze consumer responses to policy changes on both extensive and intensive margins. This dissertation consists of two chapters.

In the first chapter, I compare the effectiveness of fuel taxes and product taxes on reducing gasoline consumption of new car buyers. I employ a unified data source for vehicle choice and subsequent vehicle use to estimate a random effects logit demand model that explicitly accounts for vehicle use heterogeneity. My demand estimation suggests that new car buyers fully value the fuel-saving benefits from improved vehicle fuel efficiency when they initially purchase their cars. My policy simulations indicate that high-mileage drivers are more responsive to a change in fuel taxes than to a change in product taxes, even as low-mileage drivers are more responsive to product taxes. By capturing such heterogeneous consumer response to policies, I show that a counterfactual increase of the fuel tax is more effective than a revenue-equivalent product tax in reducing the total gasoline consumption of new car buyers. Further, when accounting for its effects on consumer response on both extensive and intensive margins, a change in fuel taxes has a clear advantage over a change in product taxes in reducing the consumption of gasoline even when the magnitude of tax increase is small. More importantly, a model not accounting for vehicle use heterogeneity understates the fuel saving effects of both policies and misleads us about the relative effectiveness when comparing different policies.

The second chapter explores how changes in the marginal cost of driving affect consumers decisions about passenger vehicle utilization, as measured by average daily miles traveled per vehicle. This intensive margin of consumer response has important implications for the effectiveness of usage-based policies, such as the fuel tax and the mileage tax, that designed to address externalities of driving. I estimate the elasticity of driving with respect to fuel cost per mile using a large panel data that covers 351 towns and cities in Massachusetts over 24 quarters. While most researchers in this literature apply fixed effects estimators to examine the elasticity of driving, I use a factor model econometric setup to account for unobserved common factors and regional heterogeneity. Residual diagnostics confirm that the factor model setup does a better job of removing the cross-section dependence than fixed effects estimators do. Given low consumer responsiveness to changes in the marginal cost of driving engendered by current usage-based policies, rights-based approaches like congestion charges might be better alternatives to influence vehicle utilization and vehicle ownership.

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(7241471), Michael J. Dziekan. "DESIGN OF A HYBRID HYDROGEN-ON-DEMAND AND PRIMARY BATTERY ELECTRIC VEHICLE." Thesis, 2021.

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In recent years lithium-ion battery electric vehicles and stored hydrogen electric vehicles have been developed to address the ever-present threat of climate change and global warming. These technologies have failed to achieve profitability at costs consumers are willing to bear when purchasing a vehicle. IFBattery, Inc. has developed a unique primary battery chemistry which simultaneously produces both electricity and hydrogen-on-demand while being both low cost and without carbon emissions. In order to determine the feasibility of the IFBattery chemistry for mobile applications, a prototype golf cart was constructed as the first public application of IFBattery technology. The legacy lead acid batteries of the prototype golf cart were replaced with an IFBattery chemistry tuned to primarily produce hydrogen-on-demand with supplemental electricity. Hydrogen produced by the IFBattery was purified and then fed into a hydrogen fuel cell where electricity was produced to power the vehicle. Electricity from the IFBattery was converted to the common voltage of the golf cart and also used to power the vehicle. Validation testing of the IFBattery powered golf cart demonstrated favorable results as an alternative to both lithium-ion battery and stored hydrogen technologies, and displayed potential for future applications.

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37

Kefayati, Mahdi. "Harnessing demand flexibility to minimize cost, facilitate renewable integration, and provide ancillary services." Thesis, 2014. http://hdl.handle.net/2152/25987.

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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.
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"Location of Refueling Stations for Alternative Fuel Vehicles Considering Driver Deviation Behavior and Uneven Consumer Demand: Model, Heuristics, and GIS." Doctoral diss., 2010. http://hdl.handle.net/2286/R.I.8784.

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abstract: Concerns about Peak Oil, political instability in the Middle East, health hazards, and greenhouse gas emissions of fossil fuels have stimulated interests in alternative fuels such as biofuels, natural gas, electricity, and hydrogen. Alternative fuels are expected to play an important role in a transition to a sustainable transportation system. One of the major barriers to the success of alternative-fuel vehicles (AFV) is the lack of infrastructure for producing, distributing, and delivering alternative fuels. Efficient methods that locate alternative-fuel refueling stations are essential in accelerating the advent of a new energy economy. The objectives of this research are to develop a location model and a Spatial Decision Support System (SDSS) that aims to support the decision of developing initial alternative-fuel stations. The main focus of this research is the development of a location model for siting alt-fuel refueling stations considering not only the limited driving range of AFVs but also the necessary deviations that drivers are likely to make from their shortest paths in order to refuel their AFVs when the refueling station network is sparse. To add reality and applicability of the model, the research is extended to include the development of efficient heuristic algorithms, the development of a method to incorporate AFV demand estimates into OD flow volumes, and the development of a prototype SDSS. The model and methods are tested on real-world road network data from state of Florida. The Deviation-Flow Refueling Location Model (DFRLM) locates facilities to maximize the total flows refueled on deviation paths. The flow volume is assumed to be decreasing as the deviation increases. Test results indicate that the specification of the maximum allowable deviation and specific deviation penalty functional form do have a measurable effect on the optimal locations of facilities and objective function values as well. The heuristics (greedy-adding and greedy-adding with substitution) developed here have been identified efficient in solving the DFRLM while AFV demand has a minor effect on the optimal facility locations. The prototype SDSS identifies strategic station locations by providing flexibility in combining various AFV demand scenarios. This research contributes to the literature by enhancing flow-based location models for locating alternative-fuel stations in four dimensions: (1) drivers' deviations from their shortest paths, (2) efficient solution approaches for the deviation problem, (3) incorporation of geographically uneven alt-fuel vehicle demand estimates into path-based origin-destination flow data, and (4) integration into an SDSS to help decision makers by providing solutions and insights into developing alt-fuel stations.
Dissertation/Thesis
Ph.D. Geography 2010

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39

Chen-YuChien and 簡振宇. "Energy Management and Demand Response of Electric Vehicles in a Smart Grid." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/53417956118301296144.

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碩士
國立成功大學
電機工程學系碩博士班
100
With the gradual increase in the penetration rate of electric vehicles, significant amount of burden can be added to the distribution system. Consequently, the problem of overloading of transformer may occur if large numbers of EVs are connected to the grid without proper control. Also, the reverse power problem may happen as the penetration rate of renewable energy increases in the future power system. This thesis proposes an optimal charging scheduling system for EV parking lots. By integrating the system with aggregators in a smart grid, a demand response control strategy on the distribution level can be executed. The topics of electricity tariff, battery degradation cost, and vehicle usage are considered in the system. Moreover, the EVs in the parking lot are used as operating reserve capacity to supply ancillary service for the power system to solve the problems of overloading and reverse power. An EV charging parking lot with 50 spaces is modeled and simulated with the Opal-RT system in this thesis to evaluate the feasibility of the proposed optimal charging scheduling strategy. The IEEE 13-node test system with the integration of scheduled household load distributed in each of the node is used as the base system. The load of the parking lot mentioned is combined with the test system to examine the problem of overloading and reverse power. When any of the problems occurs, the proposed DR strategy is initiated to achieve the goals of system stabilization for the utility and electricity payment minimization for end-users. Finally, the impact of different EV penetration rates to the load curve in Taiwan is also discussed in this thesis. With the use of the proposed energy management strategy, the tasks of system efficiency, reliability and safe operation can be assured.

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Gray, Adam Charles. "User-Constrained Algorithms for Aggregate Residential Demand Response Programs with Limited Feedback." Thesis, 2015. http://hdl.handle.net/1828/5937.

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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

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Behboodi, Kalhori Sahand. "Market-based demand response integration in super-smart grids in the presence of variable renewable generation." Thesis, 2017. http://hdl.handle.net/1828/7982.

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Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration. This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold. The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity. The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market. The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales.
Graduate
0791
sahandbehboodi@gmail.com

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Raykin, Leonid. "Impacts of Driving Patterns on Well-to-wheel Performance of Plug-in Hybrid Electric Vehicles." Thesis, 2012. http://hdl.handle.net/1807/42882.

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The well-to-wheel (WTW) environmental performance of plug-in hybrid electric vehicles (PHEVs) is sensitive to driving patterns, which vary within and across regions. This thesis develops and applies a novel approach for estimating specific regional driving patterns. The approach employs a macroscopic traffic assignment model linked with a vehicle motion model to construct driving cycles, which is done for a wide range of driving patterns. For each driving cycle, the tank-to-wheel energy use of two PHEVs and comparable non-plug-in alternatives is estimated. These estimates are then employed within a WTW analysis to investigate implications of driving patterns on the energy use and greenhouse gas emission of PHEVs, and the WTW performance of PHEVs relative to non-plug-in alternatives for various electricity generation scenarios. The results of the WTW analysis demonstrate that driving patterns and the electricity generation supply interact to substantially impact the WTW performance of PHEVs.

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Dissertations / Theses on the topic 'Vehicle energy demand'

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