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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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  • Timo Kannengießer

    (Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
    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)

  • Maximilian Hoffmann

    (Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany
    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)

  • 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
    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)

  • Martin Robinius

    (Institute of Energy and Climate Research, Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich, D-52425 Jülich, Germany)

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.

Suggested Citation

  • Timo Kannengießer & Maximilian Hoffmann & Leander Kotzur & Peter Stenzel & Fabian Schuetz & Klaus Peters & Stefan Nykamp & Detlef Stolten & Martin Robinius, 2019. "Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System," Energies, MDPI, vol. 12(14), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2825-:d:250651
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    References listed on IDEAS

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    7. Yokoyama, Ryohei & Takeuchi, Kotaro & Shinano, Yuji & Wakui, Tetsuya, 2021. "Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method," Energy, Elsevier, vol. 228(C).
    8. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).

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