Hydrogen refueling station networks for heavy-duty vehicles in future power systems

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is to use alternatively fueled vehicles (AFV). Heavy-duty vehicles (HDV) emit a large share of GHG emissions in the transport sector and are therefore the subject of growing attention from global regulators. Fuel cell and green hydrogen technologies are a promising option to decarbonize HDVs, as their fast refueling and long vehicle ranges are consistent with current logistic operational requirements. Moreover, the application of green hydrogen in transport could enable more effective integration of renewable energies (RE) across different energy sectors. This paper explores the interplay between HDV Hydrogen Refueling Stations (HRS) that produce hydrogen locally and the power system by combining an infrastructure location planning model and an electricity system optimization model that takes grid expansion options into account. Two scenarios – one sizing refueling stations to support the power system and one sizing them independently of it – are assessed regarding their impacts on the total annual electricity system costs, regional RE integration and the levelized cost of hydrogen (LCOH). The impacts are calculated based on locational marginal pricing for 2050. Depending on the integration scenario, we find average LCOH of between 4.83 euro/kg and 5.36 euro/kg, for which nodal electricity prices are the main determining factor as well as a strong difference in LCOH between north and south Germany. Adding HDV-HRS incurs power transmission expansion as well as higher power supply costs as the total power demand increases. From a system perspective, investing in HDV-HRS in symbiosis with the power system rather than independently promises cost savings of around seven billion euros per annum. We therefore conclude that the co-optimization of multiple energy sectors is important for investment planning and has the potential to exploit synergies.     

Increasing urban tram system efficiency,with battery storage and electric vehicle charging

This paper examines the possible placement of Energy Storage Systems (ESS) on an urban tram system for the purpose of exploring potential increases in operating efficiency through the examination of different locations for battery energy storage. Further, the paper suggests the utilisation of Electric Vehicle (EV) batteries at existing Park and Ride (P&R) sites as a means of achieving additional energy storage at these locations. The study achieves this through MATLAB modelling utilising captured GPS data and publically available information. This study examines the scenario of uni-directional substations with no interconnection between the overhead catenary for both directions of travel, and discusses the trade-offs between ESS size and required current limits.The results show the savings in both energy and basic CO₂ emissions alongside the discussion of Return on Investment (RoI) that can be achieved through the potential installation of ESS at identified ideal locations along the tram network. Moreover, this may be extended to the use of EVs as stationary ESS sited at the existing P&R facilities. Further, the model may also be used to inform future infrastructure upgrades and potential improvements to air quality within urban environments.     

Real-world gaseous and particle emissions of a Bi-fuel gasoline/CNG Euro 6 passenger car

The objective of the present study is the assessment of the environmental impact of a bivalent (bi-fuel) vehicle, running either on gasoline or compressed natural gas (CNG). To that aim, a Euro 6 passenger car was tested under various real-world driving conditions. In order to cover the full range of conventional powertrains currently in the market, the tests were also repeated on a Euro 6 diesel passenger car. Both cars were driven in two routes, the first complying with the regulation limits and the second going beyond them. Carbon monoxide (CO), nitrogen oxides (NO_x) and particle number (PN) emissions were recorded using a Portable Emissions Measurement System (PEMS). Apart from the aggregated emission levels, in g/km, the exact emission location along the route was also assessed. Natural gas proved beneficial for CO and PN emissions, the level of which always remained below the respective legislation limits. On the other hand, under the dynamic driving conditions with gasoline, the relevant limits were exceeded. Cold start, occurring at the beginning of the urban part, and motorway driving were identified as major contributors to total emissions, especially in gasoline mode. However, the application of natural gas was associated with a penalty in NO_x emissions, which were significantly increased as compared to gasoline. Local peaks within the urban part were identified in CNG mode. In any case, the diesel vehicle was by far the highest NO_x emitter.     

Emission rates of intermodal rail/road and road-only transportation in Europe: A comprehensive simulation study

Intermodal rail/road transportation combines advantages of both modes of transport and is often seen as an effective approach for reducing the environmental impact of freight transportation. This is because it is often expected that rail transportation emits less greenhouse gases than road transportation. However, the actual emissions of both modes of transport depend on various factors like vehicle type, traction type, fuel emission factors, payload utilization, slope profile or traffic conditions. Still, comprehensive experimental results for estimating emission rates from heavy and voluminous goods in large-scale transportation systems are hardly available so far. This study describes an intermodal rail/road network model that covers the majority of European countries. Using this network model, we estimate emission rates with a mesoscopic model within and between the considered countries by conducting a large-scale simulation of road-only transports and intermodal transports. We show that there are high variations of emission rates for both road-only transportation and intermodal rail/road transportation over the different transport relations in Europe. We found that intermodal routing is more eco-friendly than road-only routing for more than 90% of the simulated shipments. Again, this value varies strongly among country pairs.     

California’s Low Carbon Fuel Standard: Modeling financial least-cost pathways to compliance in Northwest California

The transition to low-carbon transportation fuels plays a key role in ongoing efforts to combat climate change. This analysis seeks to optimize potential alternative fuel portfolios that would lead to a 10% reduction in fuel carbon intensity by 2020 as required under California’s Low Carbon Fuel Standard (LCFS).We present a novel, probabilistic modeling approach for evaluating alternative fuel portfolios based on their marginal greenhouse gas (GHG) abatement costs. Applied to a case study region in Northwest California, our model enables us to quantify the financial cost of GHG reduction via each fuel pathway, as well as for a portfolio deployed to meet the LCFS target. It also enables us to explore the sensitivity of the alternative fuel portfolio, evaluating the impact of fluctuating prices, fuel carbon intensities, and technology penetrations on the makeup of the portfolio and on the average cost of GHG abatement.We find that battery electric vehicles play a critical role, as they offer the lowest-financial-cost significant abatement in almost all plausible scenarios. However, electric vehicles alone will not be sufficient to reach the target; low-carbon biofuels can be expected to play a role in the achievement of 2020 Low Carbon Fuel Standard targets.     

Incentivizing alternative fuel vehicles: the influence of transport policies,attitudes and perceptions

This paper aims to evaluate the influence of policies, attitudes and perceptions when incentivizing alternative fuel vehicles. The impact of possible policies such as gasoline taxes increases, purchase price subsidies, tax exemptions, and increases in fuel recharging station availability for alternative fuelled vehicles is evaluated using hybrid choice models. The models also allow assessing the sensitivity of latent variables (i.e., attitudes and perceptions) in the car purchase behaviour. The models are estimated using data from a stated choice survey collected in five Colombian cities. The latent variables are obtained from the rating of statements related to the transport system, environmental concern, vehicle preferences, and technology. The modelling approach includes regression between latent variables. Results show that environmental concern and the support for green transport policies have a positive influence on the intention to purchase alternative fuel vehicles. Meanwhile, people who reveal to be car-dependent prefer to buy standard fuelled vehicles. The analysis among cities shows similar trends in individual behaviour, although there are differences in attribute sensitivities. The policy scenario analysis revealed high sensitivity to capital cost and the need for extensive investments in refuelling stations for alternative fuel vehicles to become attractive. Nevertheless, all policies should not only be directed at infrastructure and vehicles but also be focused on user awareness and acceptance of the alternative fuel vehicles. The analysis suggests that in an environmentally conscious market, people prefer alternative fuels. However, if the transport policies support private transport, the market shares of alternative fuel vehicles will decrease.     

Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany

In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness-to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).     

A constraint programming model with time uncertainty for cooperative flight departures

The lack of a proper integration of strategic Air Traffic Management decision support tools with tactical Air Traffic Control interventions usually generates a negative impact on the Reference Business Trajectory adherence, and in consequence affects the potential of the Trajectory-Based Operations framework. In this paper, a new mechanism relaying on Reference Business Trajectories as a source of data to reduce the amount of Air Traffic Controller interventions at the tactical level while preserving Air Traffic Flow Management planned operations is presented. Artificial Intelligence can enable Constraint Programming as it is a powerful paradigm for solving complex, combinatorial search problems. The proposed methodology takes advantage of Constraint Programming and fosters adherence of Airspace User’s trajectory preferences by identifying tight interdependencies between trajectories and introducing a new mechanism to improve the aircraft separation at concurrence events considering time uncertainty. The underlying philosophy is to capitalize present degrees of freedom between layered Air Traffic Management planning tools, when sequencing departures at the airports by considering the benefits of small time stamp changes in the assigned Calculated Take-Off Time departures and to enhance Trajectory-Based Operations concepts.