Estimating future costs of power-to-gas – a component-based approach for technological learning

Abstract

Technological learning is a major aspect in the assessment of potential cost reductions for emerging energy technologies. Since the evaluation of experience curves requires the observation of production costs over several magnitudes of produced units, an early estimation of potential future technology implementation costs often presumes a certain degree of maturity. In this paper, we propose a calculation model for learning curves on the component or production process level, which allows to incorporate experience and knowledge on cost reduction potentials on a low level. This allows interchangeability between similar technologies, which is less feasible on a macro level. Additionally, the model is able to consider spill-over effects from concurrent technology usages for the inclusion of peripheral standard components for the assessment in an overall system view. The application of the model to the power-togas technology, especially water electrolysis, has shown, that the results are comparable to conventional approaches at the stack level, while providing transferability between different cell designs. In addition, the investigations made at the system level illustrate that the consideration of spill-over effects can be a relevant factor in the evaluation of cost reduction potentials, especially for technologies in an early commercial state with low numbers of cumulative productions.

Projecting cost development for future large-scale power-to-gas implementations by scaling effects

“…To evaluate the individual learning curves of the investigated PtG technologies, we used a component-based approach described in preceding paper dedicated to technological learning effects of electrolysis systems [4]. The model allows an investigation of learning effects on a component or manufacturing process/material level to evaluate aggregated effects for the overall system, described by Eq.…”

“…• For the investigated electrolysis technologies, the calculation parameters defined in [4] were used for the calculations executed in this paper. The methanation systems were also set up including 4 modules each in the learning curve model: (i) Reactor, (ii) Electric Installation & Control, (iii) Gas Conditioning and (iv) Balance of Plant.…”

“…The Reactor module, which represented the core part of the methanation system, was further separated to additional Learning Components (cf. [4]) to treat different learning rates for the Reactor itself (incl. vessel and catalyst carrier), the Heat Management and the Catalyst (representing the catalytic coating in catalytic methanation).…”

“…Thema et al [3] analyzed cost development for electrolysis and methanation projects from 1988 onwards and provided cost projections up to 2050, assuming a continuous exponential decrease. Another approach is the estimation of future cost reduction according to technological learning [4]. Nicodemus [5] investigated the impact of policy support on technological learning for PV powered PEM electrolysis resulting in the production costs of < 3 $/kg H2 by 2030.…”

“…For a holistic examination of future production costs for renewable SNG both aspects have a significant impact on overall cost reductions. Hence, our study investigates both scaling effects using component-based approach that considers the differences between common technologies and takes care of spillover effects from concurrent usage [4]. To allow this comprehensive analysis of production-related learning effects, we also include a literature review on future demand potentials for renewable gases to evaluate the according demands on cumulative productions for PtG technology components.…”