%0 Journal Article
%A Baumgärtner, Nils
%A Shu, David Yang
%A Bahl, Björn
%A Hennen, Maike
%A Hollermann, Dinah Elena
%A Bardow, André
%T DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints
%J Energy
%V 198
%@ 0360-5442
%C Amsterdam [u.a.]
%I Elsevier Science
%M FZJ-2021-00504
%P 117272 -
%D 2020
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000527569500029
%R 10.1016/j.energy.2020.117272
%U https://juser.fz-juelich.de/record/889896