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

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 De composition-based L ong-term o perational op timization 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.

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