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| Hauptseite > Publikationsdatenbank > From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition > print |
| Hauptseite > Publikationsdatenbank > From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition > print |
| 001 | 877624 | ||
| 005 | 20240709081916.0 | ||
| 024 | 7 | _ | |a 10.1016/B978-0-12-818634-3.50118-1 |2 doi |
| 024 | 7 | _ | |a 1570-7946 |2 ISSN |
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| 100 | 1 | _ | |a Baumgärtner, Nils |0 P:(DE-HGF)0 |b 0 |
| 111 | 2 | _ | |a 29th European Symposium on Computer Aided Process Engineering |c Eindhoven |d 2019-06-16 - 2019-06-19 |w The Netherlands |
| 245 | _ | _ | |a From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2019 |b Elsevier |
| 300 | _ | _ | |a 703 - 708 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 490 | 0 | _ | |a Computer Aided Chemical Engineering |v 46 |
| 520 | _ | _ | |a Long-term operation of energy systems is a complex optimization task. Often, such long-term operational optimizations are solved by direct decomposing the problem into smaller subproblems. However, direct decomposition is not possible for problems with time-coupling constraints and variables. Such time-coupling is common in energy systems, e.g., due to peak power prices and (seasonal) energy storage. To efficiently solve coupled long-term operational optimization problems, we propose a time-series decomposition method. The proposed method calculates lower and upper bounds to obtain a feasible solution of the original problem with known quality. We compute lower bounds by the Branch-and-Cut algorithm. For the upper bound, we decompose complicating constraints and variables into smaller subproblems. The solution of these subproblems are recombined to obtain a feasible solution for the long-term operational optimization. To tighten the upper bound, we iteratively decrease the number of subproblems. In a case study for an industrial energy system, we show that the proposed time-series decomposition method converges fast, outperforming a commercial state-of-the-art solver. |
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| 773 | _ | _ | |a 10.1016/B978-0-12-818634-3.50118-1 |
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