TY  - JOUR
AU  - Baumgärtner, Nils
AU  - Shu, David Yang
AU  - Bahl, Björn
AU  - Hennen, Maike
AU  - Hollermann, Dinah Elena
AU  - Bardow, André
TI  - DeLoop: Decomposition-based Long-term operational optimization of energy systems with time-coupling constraints
JO  - Energy
VL  - 198
SN  - 0360-5442
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2021-00504
SP  - 117272 -
PY  - 2020
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000527569500029
DO  - DOI:10.1016/j.energy.2020.117272
UR  - https://juser.fz-juelich.de/record/889896
ER  -