Reviewing the complexity of endogenous technological learning for energy system modeling

IF 13 Q1 ENERGY & FUELS
Johannes Behrens , Elisabeth Zeyen , Maximilian Hoffmann , Detlef Stolten , Jann M. Weinand
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引用次数: 0

Abstract

Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.
回顾能源系统建模中内生技术学习的复杂性
可再生能源技术或电解槽等能源系统组件的投资成本受技术进步的驱动而不断降低。文献中提出了各种方法来捕捉模型内生的技术学习。本综述论证了投资成本与产量之间的非线性关系,这导致了非凸优化问题,并讨论了考虑技术进步的概念。虽然迭代求解方法往往能找到依赖次优技术组合的未来能源系统设计,但实现全局最优的精确求解方法对计算要求很高。大多数研究忽略了重要的系统方面,如部门整合或详细的空间、时间和技术分辨率,以保持模型的可解决性,这同样扭曲了技术学习的影响。应用时空聚合、分解方法或技术聚类等方法可以改善这种情况。本综述揭示了这些方法的潜力,并指出了整合内生技术学习的重要考虑因素。我们提出了一种更综合的方法,用于处理整合技术学习时的计算复杂性,旨在保持模型的可行性。此外,我们还指出了当前建模实践中存在的重大差距,并提出了未来的研究方向,以提高能源系统模型的准确性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
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