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| Home > Publications database > From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition |
| Home > Publications database > From peak power prices to seasonal storage: Long-term operational optimization of energy systems by time-series decomposition |
| Contribution to a conference proceedings/Contribution to a book | FZJ-2020-02339 |
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2019
Elsevier
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/B978-0-12-818634-3.50118-1
Abstract: 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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