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Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System

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Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
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Chair for Fuel Cells, RWTH Aachen University, c/o Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany
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Westnetz GmbH, Florianstraße 15-21, 44139 Dortmund, Germany
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Innogy SE, Kruppstraße 5, 45128 Essen, Germany
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Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2825; https://doi.org/10.3390/en12142825
Received: 2 July 2019 / Revised: 18 July 2019 / Accepted: 19 July 2019 / Published: 22 July 2019
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. View Full-Text
Keywords: MILP; district optimization; energy system model; time series aggregation; typical periods MILP; district optimization; energy system model; time series aggregation; typical periods
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Kannengießer, T.; Hoffmann, M.; Kotzur, L.; Stenzel, P.; Schuetz, F.; Peters, K.; Nykamp, S.; Stolten, D.; Robinius, M. Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System. Energies 2019, 12, 2825.

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