Applying endogenous learning models in energy system optimization

TL;DR

This paper reviews methodologies for modeling technological learning effects, especially in hydrogen production, to improve energy system optimization for a zero-emission future.

Contribution

It provides a comprehensive overview of endogenous learning models and their application to low-carbon energy technologies, highlighting learning rates for future energy system modeling.

Findings

Summarizes common methodologies for modeling technological learning.

Focuses on hydrogen production technologies and their learning effects.

Provides learning rate estimates for relevant low-carbon technologies.

Abstract

Conventional energy production based on fossil fuels causes emissions which contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, an endeavor that requires an accurate modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions. The focus is on learning effects in hydrogen production technologies due to their importance in a low-carbon energy system, as well as the application of endogenous learning in energy system models. Finally, we present an overview of the learning rates of relevant low-carbon technologies required to model future energy systems.