Details
| Original language | English |
|---|---|
| Article number | 100181 |
| Number of pages | 10 |
| Journal | Advances in Applied Energy |
| Volume | 15 |
| Early online date | 14 Jul 2024 |
| Publication status | Published - Sept 2024 |
Abstract
Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.
Keywords
- Energy system optimization, Forecasting, Linear program, National energy system model, Priority list
ASJC Scopus subject areas
Sustainable Development Goals
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Advances in Applied Energy, Vol. 15, 100181, 09.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Impact of forecasting on energy system optimization
AU - Peterssen, Florian
AU - Schlemminger, Marlon
AU - Lohr, Clemens
AU - Niepelt, Raphael
AU - Hanke-Rauschenbach, Richard
AU - Brendel, Rolf
N1 - Publisher Copyright: © 2024 The Author(s)
PY - 2024/9
Y1 - 2024/9
N2 - Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.
AB - Linear programs are frequently employed to optimize national energy system models, which are used to find a minimum-cost energy system. For the operation, they assume perfect forecasting of the weather and demands over the whole optimization horizon and can therefore perfectly fit the energy systems’ design and operation. Therefore, they will yield lower costs than any real energy system that only has partial forecasting available. We compare linear programming with a priority list, a heuristic operation strategy which uses no forecasting at all, in a model of a climate-neutral German energy system. We find a 28% more expensive energy system under the priority list. Optimizing the same energy system model with both strategies envelopes the cost and design of any energy system that has partial forecasting. We demonstrate this by incorporating some rudimentary forecasting into a modified priority list, which actually reduces the gap to 22%. This is thus an approach to find an upper bound for how much a linear program possibly underestimates the costs of a real energy system in Germany in regard to imperfect forecasting. We also find that the two approaches differ mainly in the dimensioning and operation of energy storage. The priority list yields 63% less batteries, 73% less thermal storage and 54% more hydrogen storage. The use of renewables and other components in the system is very similar.
KW - Energy system optimization
KW - Forecasting
KW - Linear program
KW - National energy system model
KW - Priority list
UR - http://www.scopus.com/inward/record.url?scp=85198966743&partnerID=8YFLogxK
U2 - 10.1016/j.adapen.2024.100181
DO - 10.1016/j.adapen.2024.100181
M3 - Article
AN - SCOPUS:85198966743
VL - 15
JO - Advances in Applied Energy
JF - Advances in Applied Energy
M1 - 100181
ER -