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100 1 _ |a Kannengießer, Timo
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245 _ _ |a Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System
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520 _ _ |a The complexity of Mixed-Integer Linear Programs (MILPs) increases with the number of nodes in energy system models. An increasing complexity constitutes a high computational load that can limit the scale of the energy system model. Hence, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determines the system design through a combination of continuous and discrete decisions. On the first level, data reduction methods are used to determine the discrete design decisions in a simplified solution space. Those decisions are then fixed, and on the second level the full dataset is used to ex-tract the exact scaling of the chosen technologies. The performance of the new 2-Level Approach is evaluated for a case study of an urban energy system with six buildings and an island system based on a high share of renewable energy technologies. The results of the studies show a high accuracy with respect to the total annual costs, chosen system structure, installed capacities and peak load with the 2-Level Approach compared to the results of a single level optimization. The computational load is thereby reduced by more than one order of magnitude
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700 1 _ |a Hoffmann, Maximilian
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700 1 _ |a Kotzur, Leander
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700 1 _ |a Stenzel, Peter
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700 1 _ |a Schuetz, Fabian
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700 1 _ |a Peters, Klaus
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700 1 _ |a Stolten, Detlef
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700 1 _ |a Robinius, Martin
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773 _ _ |a 10.3390/en12142825
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