651 research outputs found

    Modeling the deployment of plug-in hybrid and electric vehicles and their effects on the Australian National Electricity Market.

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    The development of hybrid and fully electric vehicles could deliver significant reductions of emissions from the Australian transportation sector by shifting its major energy source from internal combustion to electricity. This shift towards the the use of electricity shifts the point source emissions to one which has a lower emissions intensity. Changes in load behaviour as a result of the consumer uptake of these vehicles will have significant consequences for network and central planners for the future of Australia’s electricity supply industry. This paper investigates the effects on the security of supply of energy during these previously unseen demand patterns, while also examining changes to spot market prices and changes in emissions rates. The simulation results indicate that wholesale prices during the off-peak period will increase slowly over time with controlled charging. While uncontrolled charging increases the incidence of extreme price events and a considerable number of hours with un-served energy within the network. This increase in spot prices may have consequences for regulated retail electricity tariffs. We also discuss the implementation of possible changes to the retail tariff structure to accommodate the charging of these vehicles.Electricity Markets, Hybrid Vehicle, Transportation Economics.

    Carbon Free Boston: Transportation Technical Report

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    Part of a series of reports that includes: Carbon Free Boston: Summary Report; Carbon Free Boston: Social Equity Report; Carbon Free Boston: Technical Summary; Carbon Free Boston: Buildings Technical Report; Carbon Free Boston: Waste Technical Report; Carbon Free Boston: Energy Technical Report; Carbon Free Boston: Offsets Technical ReportOVERVIEW: Transportation connects Boston’s workers, residents and tourists to their livelihoods, health care, education, recreation, culture, and other aspects of life quality. In cities, transit access is a critical factor determining upward mobility. Yet many urban transportation systems, including Boston’s, underserve some populations along one or more of those dimensions. Boston has the opportunity and means to expand mobility access to all residents, and at the same time reduce GHG emissions from transportation. This requires the transformation of the automobile-centric system that is fueled predominantly by gasoline and diesel fuel. The near elimination of fossil fuels—combined with more transit, walking, and biking—will curtail air pollution and crashes, and dramatically reduce the public health impact of transportation. The City embarks on this transition from a position of strength. Boston is consistently ranked as one of the most walkable and bikeable cities in the nation, and one in three commuters already take public transportation. There are three general strategies to reaching a carbon-neutral transportation system: • Shift trips out of automobiles to transit, biking, and walking;1 • Reduce automobile trips via land use planning that encourages denser development and affordable housing in transit-rich neighborhoods; • Shift most automobiles, trucks, buses, and trains to zero-GHG electricity. Even with Boston’s strong transit foundation, a carbon-neutral transportation system requires a wholesale change in Boston’s transportation culture. Success depends on the intelligent adoption of new technologies, influencing behavior with strong, equitable, and clearly articulated planning and investment, and effective collaboration with state and regional partners.Published versio

    Development of a power monitoring and control system to provide demand side management of electric vehicle charging activity.

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    Due to the recent inflow of Electric Vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. Increased electricity consumption due to EV charging activity results in higher electricity costs due to differences in the billing structures between residential and C&I electric ratepayers. Therefore, it is beneficial to the EVSE charging provider to minimize charging activity around peak demand periods which would result in lower electrical costs overall. A solution is developed that can provide this control without creating a nuisance to electric vehicle owners since EV charging demand is somewhat inelastic due to range anxiety. The primary objective of the research detailed in this dissertation is to develop a novel demand side management system for monitoring the peak demand of commercial time-of-day electric ratepayers that cost effectively predicts and controls electric vehicle charging during peak demand periods. This objective is achieved, therefore confirming the hypothesis that such a system can provide cost and demand benefits to forward-thinking commercial electric ratepayers that provide daytime charging capabilities. This work proposes and evaluates a novel Power Monitoring and Control System (PMCS) that can be implemented at C&I EV charging locations to minimize or eliminate the negative impacts of charging electric vehicles at the workplace in C&I environments. Operation of the PMCS begins by forecasting electrical demand in advance of every 15 minute demand interval throughout the day. The forecast is generated using an artificial neural network and a number of input data streams. Electrical demand has been shown to correlate well with weather data such as temperature and dew point. Therefore, using those measurements along with a date and time stamp, and historical electrical demand measurements, a highly accurate forecast for the following 15-minute demand interval was achieved. From that forecast, the number of EV charging stations that may be active, without the chance of creating new electrical demand peaks, is calculated. Finally, the forecast is then used to properly schedule EV charging activity so that electrical demand peaks can be avoided but charging activity is maximized. The avoidance of charging activity at or near peaks in electrical demand results in lower total electric costs associated with the charging process. The final design was implemented in an EV charging testbed at the University of Louisville and data was collected to verify the operation and performance of the PMCS. With a properly designed scheduling and prioritization control algorithm, increases in electrical demand and associated costs are limited to the error in the forecasting algorithm used for predicting electrical demand levels. The final design of the forecasting algorithm results in a mean absolute percent error of 0.02% to 0.08% in the electrical demand forecast. This corresponds to approximately 3 to 10 kVA of error in electrical demand. Taking this error into account, total cost of charging several EVs is reduced by nearly 90%. Furthermore, for scenarios where there are several more electric vehicles requiring charge than there are charging stations available, several scheduling algorithms are presented in an attempt to minimize the total processing time required for completing all charging transactions

    Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.

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    To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on the environmental implications of these emerging systems is often limited by using aggregated travel pattern data to characterize personal mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect several key factors that determine potential environmental impacts, including charging behaviors, connection needs between different transportation modes, and car/ride sharing potentials. Therefore, to better understand these systems and inform decision making, travel patterns at the individual level need to be considered. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefits of integrating individual travel patterns into environmental assessments through three case studies (vehicle electrification, charging station siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification study indicate that individual travel patterns can impact the environmental performance of fleet electrification. When battery cost exceeds 200/kWh,vehicleswithgreaterbatteryrangecannotcontinuouslyimprovetravelelectrificationandcanreduceelectrificationrate.Atthecurrentbatterycostof200/kWh, vehicles with greater battery range cannot continuously improve travel electrification and can reduce electrification rate. At the current battery cost of 400/kWh, targeting subsidies to vehicles with battery range around 90 miles can achieve higher electrification rate. The public charging station siting case demonstrates that individual travel patterns can better estimate charging demand and guide charging infrastructure development. Charging stations sited according to individual travel patterns can increase electrification rate by 59% to 88% compared to existing sites. Lastly, the ride sharing case shows that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides in Beijing can reduce total travel distance and air emissions by 33% with 10-minute travel time deviation tolerance. Only minimal tolerance to travel time change (4 minutes) is needed from the riders to enable significant ride sharing (sharing 60% of the trips and saving 20% of travel distance). In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems.PhDNatural Resources and Environment and Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113510/1/caih_1.pd

    The impact of fiscal incentives on electric vehicle adoption in Europe: a dynamic approach

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    Monetary incentives have been used worldwide to decrease high upfront costs of electric vehicles (EVs) and foster their adoption, but few empirical studies have analysed the effectiveness of such policies in Europe. The present thesis investigates the influence of consumer-side fiscal incentives on European EV adoption figures in 2010-2019 through a generalised method of moments (GMM) model. By exploiting the dynamic nature of the dependent variable, and controlling for socio-economic and attitudinal country-level data, I showed that the incentive design and the strength of network externalities in the country are critical determinants for the impact of monetary incentives

    Mass introduction of electric passenger vehicles in Brazil: impact assessment on energy use, climate mitigation and on charging infrastructure needs for several case studies

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    Mobility has proved to be a major challenge for human development, especially in urban centers worldwide, where more displacement is required, since fossil fuels consumption is increasing as well as greenhouse gas (GHG) emissions, causing air quality degradation and global warming. The predicted population increase in cities tends to increase the demand for mobility and to further exacerbate those impacts. Therefore, sustainable transport is key for the future of mobility, and electric vehicle (EV) has emerged as a recognized sustainable option. However, there are many electric vehicle barriers diffusion. This research aims to contribute to the diffusion of EV in Brazil, by assessing: 1) whether EV is a more sustainable technology when compared with ethanol vehicle; 2) the impacts of the expansion of electric mobility on CO2 emissions, in Sao Paulo; 3) how to overcome the barriers for the charging infrastructure deployment at the municipality level, in Sao Paulo, Rio de Janeiro and Belo Horizonte; and 4) key challenges and opportunities from the mass adoption of EV in Brazil. A plethora of different methods were used, including scenario analysis, multi-criteria decision methods, geographic information systems and SWOT analysis. Main results point to EV as the best technology to mitigate passenger transport related CO2 emissions in Brazil, due to its low carbon footprint. In Sao Paulo, this option could reduce around 11 MtCO2 by 2030 and save 6,200 billion USD in energy with the replacement of 20 percent of gasoline cars with EV. To meet 1 percent of EV's market share, Sao Paulo, Rio de Janeiro and Belo Horizonte together will need around 6,500 charging stations concentrated in around 1/3 of their territories (level 2). Brazil may likely have up to 10 percent of EV penetration by 2030, with the diffusion taking place mostly in southeastern municipality. Ethanol, lack of electric mobility public policy, non-urbanized like subnormal agglomerates, and risk areas, like flood hazard, are major obstacles for EV diffusion in Brazil

    Study and Implementation of Compact Modeling Techniques for the Energy Analysis and Optimization of Complex Systems

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    L'abstract è presente nell'allegato / the abstract is in the attachmen
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