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 transit...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13117 |
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| author | Hasret Sahin Abebe Asfaw Solomon Arman Aghahosseini Christian Breyer |
| author_facet | Hasret Sahin Abebe Asfaw Solomon Arman Aghahosseini Christian Breyer |
| author_sort | Hasret Sahin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b2866ebc82124c44b7b5805b70ed1d80 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-b2866ebc82124c44b7b5805b70ed1d802025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142706272210.1049/rpg2.13117The impact of spatial representation in energy transition modelling on systemwide energy return on investmentHasret Sahin0Abebe Asfaw Solomon1Arman Aghahosseini2Christian Breyer3School of Energy Systems LUT University Lappeenranta FinlandSchool of Energy Systems LUT University Lappeenranta FinlandSchool of Energy Systems LUT University Lappeenranta FinlandSchool of Energy Systems LUT University Lappeenranta FinlandAbstract 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.https://doi.org/10.1049/rpg2.13117environmental managementpower system planningRenewable Energy Policyrenewable energy sources |
| spellingShingle | Hasret Sahin Abebe Asfaw Solomon Arman Aghahosseini Christian Breyer The impact of spatial representation in energy transition modelling on systemwide energy return on investment IET Renewable Power Generation environmental management power system planning Renewable Energy Policy renewable energy sources |
| title | The impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| title_full | The impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| title_fullStr | The impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| title_full_unstemmed | The impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| title_short | The impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| title_sort | impact of spatial representation in energy transition modelling on systemwide energy return on investment |
| topic | environmental management power system planning Renewable Energy Policy renewable energy sources |
| url | https://doi.org/10.1049/rpg2.13117 |
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