1,721,003 research outputs found
PEVs Market Penetration and Impact on Fuel Taxes
No full textIn this era of fossil fuels dependency and concern about Greenhouse Gas (GHG) emissions, the scientific community is putting great effort to curb and rationalize energy consumption.
The transportation sector represents the biggest final use of oil in the U.S.. Of the almost 3 Gallons a day of oil consumed by each American, 50% goes to transporting people and goods from one place to another. Moreover, oil accounts for 43% of U.S. overall GHG emissions.
Plug-in Electric Vehicles (PEVs) are nowadays seen as a concrete solution to reduce the oil consumed in the transportation sector in the 2030-2050 timeframe, since they are able to power a substantial portion of daily travels with electricity.These advance vehicles could allow displacing a large fraction of gasoline and diesel use. Technical, economical and environmental aspects related to these innovative vehicles are extensively studied by researcher throughout the world.
In particular, this paper proposes a comparative analysis of the total PEV market penetration and sample numerical simulations are proposed for a case study. Strong emphasis has been given to study the impact of these vehicles on motor fuel taxes, a topic that has not been extensively investigated in the open literature. These taxes account not only for oil externalities, but are used to maintain the U.S. surface transportation structure. Consuming less gasoline per mile travelled will lead to reduced revenue collected from gasoline taxation, meaning that other mechanisms or funds that could augment the current means for funding and financing highway and transit infrastructure have to be found
Personal Transportation Energy Consumption
No full textThis paper centers on the estimation of the total energy consumption for personal transportation in the United States, to include fossil fuel and/or electricity consumption, depending on vehicle type. The bottom-up sector-based estimation method introduced here contributes to a computational tool under development at The Ohio State University for assisting decision making in energy policy, pricing, and investment. In this work, driving patterns are classified into two categories: commuting to work, and driving for leisure and
shopping. For commuting, distribution of distance data is available in the literature. Leisure/shopping driving durations are estimated using activity patterns for a driving population, modeled using a heterogeneous Markov chain. A backward vehicle dynamic simulator is used to compute energy consumption for different vehicle types. Key findings of the current study include: (i) Independent of the total number of miles driven annually, the higher the vehicle electrification the lower the total primary energy consumption. (ii) With the modeling in this work, the percentage of trips that purely electric vehicles are unable to complete varies from 7% to 13% for driving distances up to 20000 miles per year. The percentage increases significantly for driving distances over that threshold, owing to intrinsic limitations of the battery
Residential Power Demand Prediction and Modelling
No full textThe U.S. energy mix is highly weighted toward fossil fuels and concerns about fossil fuel reliance have increased pressure on policy-makers to reduce this dependence. Energy-saving and advanced technologies, such as renewable-energy based systems, Plug-in Electric Vehicles (PEVs), energy storage devices, controllable appliances and distributed cogeneration are often suggested as parts of the key to face this crisis.
Possible advantages in terms of energy and cost savings with respect to integrated energy systems and household management can be investigated by modelling the interactions between different sub-system components, namely power grid, household power demand prediction, renewable energy source, energy storage unit and PEV.
This paper presents a model to simulate the power demand of a single household consisting of multiple individuals. Activity patterns for the individuals are modelled using a heterogeneous Markov chain, and the total power consumption of the household is computed based on the activity patterns, lighting and cold appliances consumption. Using data collected by the U.S. Census Bureau, a case study for typical U.S. consumers has been developed. The data have been used to conduct an in-sample validation of the modelled activities. The results show highly realistic patterns and capture annual and diurnal variations, load fluctuations, and diversity between households, location, and different household sizes.
The model developed can serve as a tool to evaluate the impact of different energy technologies, such as low-power appliances, automated appliance or domestic control systems, and assess population behaviour and predisposition toward energy saving
Energy consumption of residential HVAC systems: a simple physically-based model
No full textThe residential sector accounted for almost a quarter of the total 2009 energy consumption in the United States, namely 21.2 out of 94.6 Quadrillion BTU of primary energy. Space conditioning end-use includes Heating, Ventilation and Air Conditioning (HVAC) and represents the most significant residential energy consumption.
This paper proposes a simple, physically-based model to simulate electric and heating energy consumption of residential HVAC systems during the year, starting from a limited set of household characteristic parameters. The model relies on fundamental principles of thermodynamics and heat transfer applied to a control volume including solely the air present in the household.
The model has been implemented in MATLAB® and its flexibility allows different scenarios to be easily simulated and the impact of different technology adoptions to be assessed. A validation against actual metered residential load data provided by American Electric Power is provided
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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