A GIS-based assessment of coal-based hydrogen infrastructure deployment in the state of Ohio

Johnson, Nils; Yang, Christopher; Ogden, Joan M.

doi:10.1016/j.ijhydene.2008.06.069

Cited by 43 publications

(12 citation statements)

References 22 publications

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“…4 respectively. This matches well to daily hydrogen consumption estimates used in the literature [13,17]. For the entire year of 2011, 64 different drivers filled at the UCI station.…”

Section: Station Configurationsupporting

confidence: 86%

Quantitative analysis of a successful public hydrogen station

Brown

Stephens-Romero

Samuelsen

2012

International Journal of Hydrogen Energy

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IntroductionGlobal climate change, air quality concerns, and the geopolitical and economic instability associated with petroleum fuel are driving manufacturers, and society, to find alternatives to gasoline and diesel powered automobiles. Hydrogen powered fuel cell vehicles offer a potential solution to all three major problems because hydrogen can be generated and consumed with little or no carbon footprint, pollutant emissions, or oil usage. The California Hydrogen Highway was the first large-scale governmental foray into the provision of hydrogen infrastructure and provided the needed refueling capacity for early vehicle deployment programs [1]. Thanks in part to the efforts of California and the availability of fueling stations, automakers have made remarkable advances in fuel cell vehicle development and are projecting commercialization in the 2015 timeframe [2]. As this deadline approaches, attention turns to establish a sufficient refueling infrastructure for early consumers. This reality is reinforced by the California Energy Commission's allocation of $22 million for hydrogen station funding for 2010, and another $14 million designated for 2011 [3]. A significant and globally researched part of this potential infrastructure rollout is fueling station placement [4e7]. The initial California Hydrogen Highway plan envisioned wellspaced fueling stations positioned along major transportation corridors [1]. Current planning for initial commercialization focuses on a "cluster" approach whereby several stations are clustered around the geographic fuel cell vehicle deployment regions [8,9]. An early cluster is developing in Orange County, California centered on the existing University of California (UC) Irvine station that will include a Shell Hydrogen station in Newport Beach that is undergoing initial fueling trials as of December 2011, Available online at www.sciencedirect.com journal h om epa ge: www.elsev ier.com/locate/he i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 7 ( 2 0 1 2 ) 1 2 7 3 1 e1 2 7 4 0

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“…4 respectively. This matches well to daily hydrogen consumption estimates used in the literature [13,17]. For the entire year of 2011, 64 different drivers filled at the UCI station.…”

Section: Station Configurationsupporting

confidence: 86%

Quantitative analysis of a successful public hydrogen station

Brown

Stephens-Romero

Samuelsen

2012

International Journal of Hydrogen Energy

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“…More recently, researchers have begun to include more complete systems into the scope of their models. For example, infrastructure models have been developed for China [75], Europe [76], Germany [77], Great Britain [78], South Korea [79], and the United States [80][81][82]. For a recent literature review aimed at optimizing hydrogen infrastructure see Agnolucci and McDowall [83].…”

Section: Fleet Size and MIXmentioning

confidence: 99%

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

et al. 2016

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Current logistics and transportation (L&T) systems include heterogeneous fleets consisting of common internal combustion engine vehicles as well as other types of vehicles using "green" technologies, e.g., plug-in hybrid electric vehicles and electric vehicles (EVs). However, the incorporation of EVs in L&T activities also raise some additional challenges from the strategic, planning, and operational perspectives. For instance, smart cities are required to provide recharge stations for electric-based vehicles, meaning that investment decisions need to be made about the number, location, and capacity of these stations. Similarly, the limited driving-range capabilities of EVs, which are restricted by the amount of electricity stored in their batteries, impose non-trivial additional constraints when designing efficient distribution routes. Accordingly, this paper identifies and reviews several open research challenges related to the introduction of EVs in L&T activities, including: (a) environmental-related issues; and (b) strategic, planning and operational issues associated with "standard" EVs and with hydrogen-based EVs. The paper also analyzes how the introduction of EVs in L&T systems generates new variants of the well-known Vehicle Routing Problem, one of the most studied optimization problems in the L&T field, and proposes the use of metaheuristics and simheuristics as the most efficient way to deal with these complex optimization problems.

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“…There are few works that have applied SA to such models, all of them have used the OAT perturbation method and analyzed and discussed preestablished scenarios resulting in qualitative analyses. Johnson et al (2008) studied the geographical sensitivity (economies of scale). A set of five demand penetration scenarios and several pathways for the SA related to the transportation type (pipeline and tanker trucks) and capacity (size and capacity of tanker truck liquefiers) has been considered; the pipeline costs and liquefier capacity were found as the most sensitive parameter.…”

Section: Sensitivity Analysis For Hscmentioning

confidence: 99%

“…A sensitivity analysis of the demand parameter is carried out in various HSC SAs (e.g., Ren and Gao (2010), Yang and Ogden (2013), Murthy Konda et al (2011), Johnson et al (2008)). The demand in the HSC models has been studied only in scenario-based analysis (Ball and Wietschel 2009).…”

Section: Demandmentioning

confidence: 99%

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Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model

Robles

Almaraz

Azzaro‐Pantel

2017

Process Integr Optim Sustain

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Hydrogen is one of the most promising energy carriers in the quest for a more sustainable energy mix. In this paper, a model of the hydrogen supply chain (HSC) based on energy sources, production, storage, transportation, and market has been developed through a MILP formulation (Mixed Integer Linear Programming). Previous studies have shown that the start-up of the HSC deployment may be strongly penalized from an economic point of view. The objective of this work is to perform a sensitivity analysis to identify the major parameters (factors) and their interaction affecting an economic criterion, i.e., the total daily cost (TDC) (response), encompassing capital and operational expenditures. An adapted methodology for this SA is the design of experiments through the Factorial Design and Response Surface methods. Six key parameters are chosen (demand, capital change factor (CCF), storage and production capital costs (SCC, PCC), learning rate (LR), and unit production cost (UPC)). The demand is the factor that is by far the most significant parameter that strongly conditions the TDC optimization criterion, the second most significant parameter being the capital change factor. To a lesser extent, the other influencing factors are PCC and LR. The main interactions are found between demand, CCF, UPC, and SCC. The discussion has also shown that the calculation of UPC has to be improved taking into account the contribution of the fixed, electricity, and feedstock costs instead of being considered as a fixed parameter only depending on the size of the production unit. As any change that could occur relative to demand or CCF could strongly affect the response variable, more effort is also needed to find the more consistent way to model demand uncertainty in HSC design, especially since a long horizon time is considered for hydrogen deployment.

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A GIS-based assessment of coal-based hydrogen infrastructure deployment in the state of Ohio

Cited by 43 publications

References 22 publications

Quantitative analysis of a successful public hydrogen station

Quantitative analysis of a successful public hydrogen station

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model

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