Applying Endogenous Learning Models in Energy System Optimization

Abstract

Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems

“…1.2. Reviewing the impact of maximum investment rates While they can be a point of interest in best practices papers [2] and papers with a strong review element [43,44], maximum investment rates have comparatively received far less attention from model or case study focussed papers 4 . Investment constraints such as technology growth rates or market shares are typically used as calibration techniques to ensure models propose credible investment pathways.…”

“…Note that this concept may be referred by a variety of other terms, such as "build rates"[45], "implementation speed constraints"[43], or "generation growth caps"[46] …”

A scenario analysis of potential long-term impacts of COVID-19 on the Tunisian electricity sector

“…Also the technological learning of renewables, especially wind and solar PV, has proven to be quite uncertain and often underestimated. This study could be subject to this weakness, which can be addressed in future work in different ways: by updating the assumptions, possible thanks to the availability of the dataset and the open source model infrastructure; by incorporating technology learning according to methods discussed in literature [ 21 ]. On the technology portfolio, aspects requiring immediate attention would be re-assessment of the potential of biomass in the electricity mix and refining of the data base for batteries and other storage solutions.…”

Impact of endogenous learning curves on maritime transition pathways

“…However, in modern IAMs and energy system models, the concept of implementing fully endogenous experience curves concerning the costs of technologies is excluded due to computational feasibility [21,22]. Incorporating future cost estimates that endogenously depend on investments in a certain technology turns future cost estimates from a parameter into a variable in the optimization model.…”