DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints

Nils Baumgärtner, David Shu, Björn Bahl, Maike Hennen, Dinah Elena Hollermann and André Bardow

Energy, 2020, vol. 198, issue C

Abstract: Long-term operational optimization of energy systems results in challenging, large-scale problems. These large-scale problems can be directly decomposed into smaller subproblems, in the absence of time-coupling constraints and variables. However, time-coupling is common in energy systems, e. g. due to (seasonal) energy storage and peak-power prices. To solve time-coupled long-term operational optimization problems, we propose the method DeLoop for the Decomposition-based Long-term operational optimization of energy systems with time-coupling. DeLoop calculates feasible solutions (upper bounds) by decomposing the operational optimization problem into smaller subproblems. The solutions of these subproblems are recombined to obtain a feasible solution for the original long-term problem. To evaluate the quality of the feasible solutions, DeLoop computes lower bounds by linear programming relaxation. DeLoop iteratively decreases the number of subproblems and employs the Branch-and-Cut procedure to tighten the bounds. In a case study of an energy system, DeLoop converges fast, outperforming a commercial state-of-the-art solver by a factor of 32.

Keywords: Large-scale MILP; Complicating constraints; Seasonal storage; Network connection fees; Emission targets; Time-coupling; Solution method (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:198:y:2020:i:c:s0360544220303790

DOI: 10.1016/j.energy.2020.117272

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