Details
| Original language | English |
|---|---|
| Pages (from-to) | 144-154 |
| Number of pages | 11 |
| Journal | Renewable energy |
| Volume | 198 |
| Early online date | 8 Aug 2022 |
| Publication status | Published - Oct 2022 |
Abstract
Energy system models with high resolutions for time and space can contribute to establishing technically feasible and economically viable energy system designs with high shares of renewable energy. However, the spatial concentration of renewables in cost-optimal spatially-distributed model results is a known effect which provides solutions with low public acceptance and often in contrast to political goals. Existing approaches focus on either cost-optimality or equity, whereas integrated methods to establish energy system designs with low costs and a fair distribution of renewable energy are scarce. In this study, we analyze the spatial concentration in a cost-optimal energy system model to identify the most relevant parameters for renewable distribution. Furthermore, two model extensions are presented to overcome this concentration. First, we examine the standard approach of regional average solar and onshore wind energy yields. We find that the intra-regional energy yield distribution can reduce extreme renewable distributions. Second, we consider quadratic environmental costs as the result of space resistance due to land consumption by renewable energy capacities. This approach sets incentives for the partial utilization of capacity potential, thereby reducing spatial concentration of renewable energy. Both model extensions highlight the importance of the renewable energy potential.
Keywords
- Energy system modeling, Energy system optimization, Environmental costs, Renewable energy, Space resistance, Spatial concentration
ASJC Scopus subject areas
Sustainable Development Goals
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In: Renewable energy, Vol. 198, 10.2022, p. 144-154.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Spatial concentration of renewables in energy system optimization models
AU - Lohr, C.
AU - Schlemminger, M.
AU - Peterssen, F.
AU - Bensmann, A.
AU - Niepelt, R.
AU - Brendel, R.
AU - Hanke-Rauschenbach, R.
N1 - Funding Information: This work was supported by the Ministry of Science and Culture of Lower Saxony (Niedersächsiches Ministerium für Wissenschaft und Kultur (MWK)) (grant no VWZN3770 and 74ZN1596 )
PY - 2022/10
Y1 - 2022/10
N2 - Energy system models with high resolutions for time and space can contribute to establishing technically feasible and economically viable energy system designs with high shares of renewable energy. However, the spatial concentration of renewables in cost-optimal spatially-distributed model results is a known effect which provides solutions with low public acceptance and often in contrast to political goals. Existing approaches focus on either cost-optimality or equity, whereas integrated methods to establish energy system designs with low costs and a fair distribution of renewable energy are scarce. In this study, we analyze the spatial concentration in a cost-optimal energy system model to identify the most relevant parameters for renewable distribution. Furthermore, two model extensions are presented to overcome this concentration. First, we examine the standard approach of regional average solar and onshore wind energy yields. We find that the intra-regional energy yield distribution can reduce extreme renewable distributions. Second, we consider quadratic environmental costs as the result of space resistance due to land consumption by renewable energy capacities. This approach sets incentives for the partial utilization of capacity potential, thereby reducing spatial concentration of renewable energy. Both model extensions highlight the importance of the renewable energy potential.
AB - Energy system models with high resolutions for time and space can contribute to establishing technically feasible and economically viable energy system designs with high shares of renewable energy. However, the spatial concentration of renewables in cost-optimal spatially-distributed model results is a known effect which provides solutions with low public acceptance and often in contrast to political goals. Existing approaches focus on either cost-optimality or equity, whereas integrated methods to establish energy system designs with low costs and a fair distribution of renewable energy are scarce. In this study, we analyze the spatial concentration in a cost-optimal energy system model to identify the most relevant parameters for renewable distribution. Furthermore, two model extensions are presented to overcome this concentration. First, we examine the standard approach of regional average solar and onshore wind energy yields. We find that the intra-regional energy yield distribution can reduce extreme renewable distributions. Second, we consider quadratic environmental costs as the result of space resistance due to land consumption by renewable energy capacities. This approach sets incentives for the partial utilization of capacity potential, thereby reducing spatial concentration of renewable energy. Both model extensions highlight the importance of the renewable energy potential.
KW - Energy system modeling
KW - Energy system optimization
KW - Environmental costs
KW - Renewable energy
KW - Space resistance
KW - Spatial concentration
UR - http://www.scopus.com/inward/record.url?scp=85136099618&partnerID=8YFLogxK
U2 - 10.1016/j.renene.2022.07.144
DO - 10.1016/j.renene.2022.07.144
M3 - Article
AN - SCOPUS:85136099618
VL - 198
SP - 144
EP - 154
JO - Renewable energy
JF - Renewable energy
SN - 0960-1481
ER -