Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany

To reach ambitious CO₂ mitigation targets, the transport sector has to become nearly emission-free and the most promising option for passenger cars are battery electric vehicles (BEV) powered using renewable energy. Despite their important benefits, BEV still face technological barriers, mainly their limited battery range and the limited availability of public fast-charging infrastructure. These factors are hindering the diffusion of electric vehicles (EV). The question of how to address these technical barriers has been widely analyzed in the literature, but so far there has been no cost-efficiency comparison of longer battery ranges and more widespread fast-charging infrastructure that evaluates them both technically and economically. This paper aims to find cost-efficient ways to address limited battery ranges and availability of public fast-charging infrastructure. We focus on German passenger cars that are licensed to commercial owners, since these are an important first market for EV. Our results indicate that fast-charging infrastructure is very cost-efficient as it enables significant proportions of BEV in the fleet at low infrastructure density. The technically feasible maximum BEV shares in the commercial sector can only be achieved with longer battery ranges. However, longer battery ranges are currently associated with comparatively high additional costs

Estimating Emission Control Costs: A Comparison of the Approaches Implemented in the EC-EFOM-ENV and the IIASA-RAINS Models

The paper introduces two major model approaches to estimate emission control costs and develops a methodology to introduce results of energy flow optimization models (such as EFOM-ENV) into models for integrated assessment of acidification control strategies (such as the RAINS model). Based on a reference scenario for West Germany, national cost curves for reductions of SO2 and NOx emissions derived by both the EFOM-ENV and the RAINS model are compared. It is shown that -- as long as changes in the energy structure are excluded as means for reducing emissions -- results obtained from these models are comparable and the reasons for differences can be traced back to different input assumptions. However, as soon as energy conservation and fuel-substitution are utilized to reduce emissions, the simplified approach implemented in the RAINS model results in an overestimation of emission control costs

Economic impacts of hydrogen as an energy carrier in european countries

The two objectives of this paper are to identify possible sectoral shifts and employment effects due to the application of hydrogen in the energy system for selected European countries till 2030. This is based on assumptions about the market penetration of hydrogen as an energy carrier, an analysis of the competitiveness of EU countries in this technology field and input-output model calculations. The analysis showed that the introduction of hydrogen leads to significant shifts between economic sectors and, as a policy recommendation, it is concluded that the required workforce skills in hydrogen technologies should be available in time in order to be properly prepared for this. Some employment gains are possible for the EU Member States analysed if the introduction of hydrogen does not result in significant changes in export/import flows. However, the lead market analysis also showed that the competitiveness of EU countries varies significantly and that, viewed as a whole, Europe is in danger of falling behind its main competitors. This may lead to job losses because the industry branches affected-automotive and plant manufacturers - represent key sectors for the EU. One policy goal, therefore, especially for countries with a large share of automobile and plant manufacturing, is to aim to be a lead market for hydrogen and fuel cells

Supply risks associated with lithium-ion battery materials

One possibility for electrification of road transport consists of battery electric vehicles in combination with carbon-free sources of electricity. It is highly likely that lithium-ion batteries will provide the basis for this development. In the present paper, we use a recently developed, semi-quantitative assessment scheme to evaluate the relative supply risks associated with the elements used in the functional materials of six different lithium-ion battery types. Eleven different indicators in four supply risk categories are applied to each element; the weighting of the indicators is determined by external experts within the framework of an Analytic Hierarchy Process. The range of supply risk values on the elemental level is distinctly narrower than in our previous work on photovoltaic materials. The highest values are obtained for lithium and cobalt; the lowest for aluminium and titanium. Copper, iron, nickel, carbon (graphite), manganese and phosphorous form the middle group. We then carry out the assessment of the six battery types, to give comparative supply risks at the technology level. For this purpose the elemental supply risk values are aggregated using four different methods. Due to the small spread at the elemental level the supply risk values in all four aggregation methods also lie in a narrow range. Removing lithium, aluminium and phosphorous from the analysis, which are present in all types of battery, improves the situation. For aggregation with the simple arithmetic mean, an uncertainty analysis shows that only lithium-iron phosphate has a measurably lower supply risk compared to the other battery types. For the “cost-share” aggregation using seven elements, lithium cobalt oxide has a substantially higher supply risk than most other types

Simulation of current pricing-tendencies in the German electricity market for private consumption

The German electricity market for private consumption is characterized by increasing prices and low participation of the consumers. This prompts us to investigate the interdependencies between the customers' engagement in the market and the suppliers' pricing strategies. Based on an analysis of the German retail market, an agent-based simulation model is developed. Whereas the behaviour of private customers is calibrated on field data, the suppliers learn to maximize profits with a feedback-learning heuristic. The simulation results show a tendency of rising prices, which are created without the assumption of tacit collusion among suppliers. We conclude that in Germany the Current market pressure of private customers may not be a sufficient incentive for suppliers to lower electricity prices