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

Timo Kannengießer, Maximilian Hoffmann, Leander Kotzur, Peter Stenzel, Fabian Schuetz, Klaus Peters, Stefan Nykamp, Detlef Stolten and Martin Robinius
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Timo Kannengießer: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
Maximilian Hoffmann: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
Leander Kotzur: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
Peter Stenzel: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
Fabian Schuetz: Westnetz GmbH, Florianstraße 15-21, 44139 Dortmund, Germany
Klaus Peters: Westnetz GmbH, Florianstraße 15-21, 44139 Dortmund, Germany
Stefan Nykamp: Innogy SE, Kruppstraße 5, 45128 Essen, Germany
Detlef Stolten: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
Martin Robinius: Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany

Energies, 2019, vol. 12, issue 14, 1-27

Abstract: 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.

Keywords: MILP; district optimization; energy system model; time series aggregation; typical periods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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