This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.1016/B978-0-12-818634-3.50118-1 in citations.
From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition
From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition
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 t...
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Personal Name(s): | Baumgärtner, Nils |
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Shu, David Yang / Bahl, Björn / Hennen, Maike / Bardow, André (Corresponding author) | |
Contributing Institute: |
Modellierung von Energiesystemen; IEK-10 |
Imprint: |
Amsterdam [u.a.]
Elsevier
2019
|
Physical Description: |
703 - 708 |
DOI: |
10.1016/B978-0-12-818634-3.50118-1 |
Conference: | 29th European Symposium on Computer Aided Process Engineering, Eindhoven (The Netherlands), 2019-06-16 - 2019-06-19 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Assessment of Energy Systems – Addressing Issues of Energy Efficiency and Energy Security |
Series Title: |
Computer Aided Chemical Engineering
46 |
Publikationsportal JuSER |
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. |