Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 8 May 2019 / Approved: 10 May 2019 / Online: 10 May 2019 (10:25:50 CEST)

How to cite: Kannengießer, T.; Hoffmann, M.; Kotzur, L.; Stenzel, P.; Markewitz, P.; Schütz, 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. Preprints 2019, 2019050116 (doi: 10.20944/preprints201905.0116.v1). Kannengießer, T.; Hoffmann, M.; Kotzur, L.; Stenzel, P.; Markewitz, P.; Schütz, 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. Preprints 2019, 2019050116 (doi: 10.20944/preprints201905.0116.v1).

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. Especially in microgrid optimisation problems with multiple buildings and energy systems with a number of rival supply, distribution and storage technologies, methods are sought to reduce this complexity. In this paper, we present a new 2-Level Approach to MILP energy system models that determine 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 extract 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, while a significantly higher accuracy is reached in comparison to the common time series aggregation approach.

Subject Areas

MILP; district optimization; energy system model; time series aggregation; typical periods

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