Detailed assessment of global transport-energy models’ structures and projections

Yeh, Sonia; Mishra, Gouri Shankar; Fulton, Lew; Kyle, Page; McCollum, David; Miller, Joshua; Cazzola, P.; Teter, Jacob

doi:10.1016/j.trd.2016.11.001

Cited by 85 publications

(69 citation statements)

References 24 publications

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“…Historical trends and the baseline year often serve as references to develop future projections. However, the assumption of baseline year data can significantly differ between studies and can lead to different conclusions as found by Yeh et al (2017) [59]. Furthermore, a sensitivity analysis cannot quantify the uncertainty caused by random errors in the input data [49].…”

Section: Limitations Of a Deterministic Uncertainty Analysismentioning

confidence: 98%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

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Section: Limitations Of a Deterministic Uncertainty Analysismentioning

confidence: 98%

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Dreier¹,

Howells²

2019

Energies

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“…Annually, HDTs move 60% of the worldwide on-road freight demand, yet they represent only 7-9% of the global vehicle population [2], [3].…”

Section: Background and Motivationmentioning

confidence: 99%

Comparing alternative heavy-duty drivetrains based on GHG emissions, ownership and abatement costs: Simulations of freight routes in British Columbia

Lajevardi

Axsen

Crawford

2019

Transportation Research Part D: Transport and Environment

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Heavy-duty trucks (HDTs) are vital in delivering products to the consumers around the world and help maintain the quality of life. However, they are heavily depending on fossil diesel use, which causing global climate change as well as local air pollutions. Although they represent a small percentage of vehicle population, they emit more than 30% of GHGs in road transportation or 5% of global greenhouse gas (GHG) emissions. Furthermore, GHG emissions from this sector are expected to steadily grow due to economic growth, globalization, industrialization, online shopping, and fast delivery expectations. This study was focused on the Canadian province of British Columbia (BC) as a case study where HDTs are responsible for more than 4% of total provincial GHGs. BC, along with many regions around the world, has been committed to reduce its GHG emissions by 80% below 2007 levels by 2050. The goal of this study was to evaluate the potential of meeting this objective for BC HDTs using alternative drivetrain technologies. First, a component-level model was developed in Matlab to compute on-road energy consumption and CO2 emissions of compressed natural gas and diesel HDTs based on their physical parameters (e.g. mass) over several selected drive cycles. Results of the first contribution indicated a compressed natural gas (CNG) drivetrain emits 13-15% fewer GHG than a comparable diesel. Road grades of several main BC routes were included in the drive cycle simulations, which is an important factor that can increase the fuel consumption and CO2 emission by as much as 24% relative to a flat route assumption. In the second contribution, the physical energy consumption model was extended to compare 16 diverse drivetrain technologies, including a pure battery electric. The comparison metrics were also extended to well-to-wheel GHG emissions, total ownership costs (TOC) (including infrastructure), and abatement costs (based on incremental TOC cost over GHG emissions reduction), and cargo capacity impacts. The 16 considered drivetrains were distinguished by their fuel types, combustion technology, drivetrain architecture, and connection to the electricity grid (e.g. catenary vs fast charging stations). Next, the activity data of 1,616 HDTs operating in BC with sparse recording intervals was used to select 6 representative freight routes with different ranges of 120-950 km split into short and long haul routes. A combination of filtering and interpolation techniques was used to develop 1-Hz drive cycles compatible with the level of plug-in hybrid adoption. It was found infrastructure density increases the probability of meeting the 2050 target on both short and long haul HDTs. However, the increase in the probability is much higher for the short haul segment. Among various infrastructure roll-out scenarios, rapid deployment of hydrogen fueling stations was found to have the highest positive impact on GHG emissions reduction for both short and long haul markets. Both battery electric and hydrogen fuel cell drivetrains can succeed in...

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“…Finally, the third transport comparison paper makes the link between integrated assessment models and transport sector-only models (Yeh et al, 2017). In this paper the modelling frameworks, underlying data, assumptions intermediate results, and projections are compared.…”

Section: Model Comparison Studiesmentioning

confidence: 99%

A special issue on model-based long-term transport scenarios: Model comparison and new methodological developments to improve energy and climate policy analysis

Vuuren

Edelenbosch

McCollum

et al. 2017

Transportation Research Part D: Transport and Environment

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Detailed assessment of global transport-energy models’ structures and projections

Cited by 85 publications

References 24 publications

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Comparing alternative heavy-duty drivetrains based on GHG emissions, ownership and abatement costs: Simulations of freight routes in British Columbia

A special issue on model-based long-term transport scenarios: Model comparison and new methodological developments to improve energy and climate policy analysis

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