## 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

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**Impact of forecasting on energy system optimization.**/ Peterssen, Florian; Schlemminger, Marlon; Lohr, Clemens et al.

In: Advances in Applied Energy, Vol. 15, 100181, 09.2024.

Research output: Contribution to journal › Article › Research › peer review

*Advances in Applied Energy*, vol. 15, 100181. https://doi.org/10.1016/j.adapen.2024.100181

*Advances in Applied Energy*,

*15*, Article 100181. https://doi.org/10.1016/j.adapen.2024.100181

}

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 -