The impact of spatial representation in energy transition modelling on systemwide energy return on investment

Abstract Adopting aggregation techniques in power sector modelling led to disregarding the key characteristics of regions in terms of resource use, which may not completely capture the bottlenecks in the energy transition. This study provides a holistic approach to estimate its impact on the transition of the European power system from the perspective of energy return on investment (EROI) by using six energy transition scenarios based on three different spatial representations. The findings indicate that EROI trends are highly dependent on the spatial representation, technology selection and energy mix. Further additional capacities of complementary technologies along with an upsurge in renewable capacities drive EROI values down. Disregarding the physical distances in the energy modelling results in large EROI enhancement due to the artificial smoothing effect of the aggregation method. EROI values of the aggregated scenarios remain between 18 and 24 by 2050. In the case of 20 independent sub‐regions, the lowest EROI is obtained at about 14 by 2050, due to the limitation on optimal resource utilisation. Interconnection of the sub‐regions, which represents the best proximation to the real situation, increases the EROI to 17 by 2050.

Authors

Institutions

Publication Details

Journal
IET Renewable Power Generation
Published
2024-10-01
DOI
https://doi.org/10.1049/rpg2.13117
Citations
2
Primary Topic
Global Energy and Sustainability Research
Type
article
Field-Weighted Citation Impact
0.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact of spatial representation in energy transition modelling on systemwide energy return on investment

Arman Aghahosseini, Hasret Sahin, A.A. Solomon, Christian Breyer
2 citations
IET Renewable Power Generation
Global Energy and Sustainability Research
0.20
article

The impact of spatial representation in energy transition modelling on systemwide energy return on investment

Arman Aghahosseini, Hasret Sahin, A.A. Solomon, Christian Breyer
article en
2 citations

Abstract

Abstract Adopting aggregation techniques in power sector modelling led to disregarding the key characteristics of regions in terms of resource use, which may not completely capture the bottlenecks in the energy transition. This study provides a holistic approach to estimate its impact on the transition of the European power system from the perspective of energy return on investment (EROI) by using six energy transition scenarios based on three different spatial representations. The findings indicate that EROI trends are highly dependent on the spatial representation, technology selection and energy mix. Further additional capacities of complementary technologies along with an upsurge in renewable capacities drive EROI values down. Disregarding the physical distances in the energy modelling results in large EROI enhancement due to the artificial smoothing effect of the aggregation method. EROI values of the aggregated scenarios remain between 18 and 24 by 2050. In the case of 20 independent sub‐regions, the lowest EROI is obtained at about 14 by 2050, due to the limitation on optimal resource utilisation. Interconnection of the sub‐regions, which represents the best proximation to the real situation, increases the EROI to 17 by 2050.

IET Renewable Power GenerationVol. 18(14)
Lappeenranta-Lahti University of Technology (FI)
Academy of Finland
Decent work and economic growth
Openalex Percentile: Top 58%
Global Energy and Sustainability Research
0.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.