全能源系统投资的能源回报:在比利时的应用。

Biophysical economics and sustainability Pub Date : 2022-01-01 Epub Date: 2022-10-19 DOI:10.1007/s41247-022-00106-0
Jonathan Dumas, Antoine Dubois, Paolo Thiran, Pierre Jacques, Francesco Contino, Bertrand Cornélusse, Gauthier Limpens
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引用次数: 3

摘要

规划能源系统的去化石化,同时保持对丰富的初级能源资源的获取,是一个涉及经济、技术、环境和社会各方面的重要的多目标问题。然而,在能源系统模型中,大多数长期政策都将系统成本作为减少碳足迹的主要指标。本文首次开发了一种新颖的方法,通过在全能源系统优化模型中添加社会和经济方面的替代指标,即能源投资回报率(EROI)。此外,我们进行了全球敏感性分析,以确定驱动EROI不确定性的主要参数。该方法在2035年比利时能源系统的几个温室气体(GHG)排放目标中得到了说明。然而,它可以适用于任何世界或国家的能源系统。当温室气体排放量减少80%时,主要有三个方面的结果:(1)EROI从8.9降低到3.9;(ii)进口可再生天然气(甲烷)占系统一次能源结构的60%;(iii)敏感性分析显示,该燃料驱动67%的EROI变化。这些结果提出了如何在不产生不利社会经济影响的情况下实现气候目标的问题,表明了在能源系统模型中考虑EROI的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Energy Return on Investment of Whole-Energy Systems: Application to Belgium.

The Energy Return on Investment of Whole-Energy Systems: Application to Belgium.

The Energy Return on Investment of Whole-Energy Systems: Application to Belgium.

The Energy Return on Investment of Whole-Energy Systems: Application to Belgium.

Planning the defossilization of energy systems while maintaining access to abundant primary energy resources is a non-trivial multi-objective problem encompassing economic, technical, environmental, and social aspects. However, most long-term policies consider the cost of the system as the leading indicator in the energy system models to decrease the carbon footprint. This paper is the first to develop a novel approach by adding a surrogate indicator for the social and economic aspects, the energy return on investment (EROI), in a whole-energy system optimization model. In addition, we conducted a global sensitivity analysis to identify the main parameters driving the EROI uncertainty. This method is illustrated in the 2035 Belgian energy system for several greenhouse gas (GHG) emissions targets. Nevertheless, it can be applied to any worldwide or country energy system. The main results are threefold when the GHG emissions are reduced by 80%: (i) the EROI decreases from 8.9 to 3.9; (ii) the imported renewable gas (methane) represents 60 % of the system primary energy mix; (iii) the sensitivity analysis reveals this fuel drives 67% of the variation of the EROI. These results raise questions about meeting the climate targets without adverse socio-economic impact, demonstrating the importance of considering the EROI in energy system models.

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