%0 Journal Article
%A Kannengießer, Timo
%A Hoffmann, Maximilian
%A Kotzur, Leander
%A Stenzel, Peter
%A Schuetz, Fabian
%A Peters, Klaus
%A Nykamp, Stefan
%A Stolten, Detlef
%A Robinius, Martin
%T Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System
%J Energies
%V 12
%N 14
%@ 1996-1073
%C Basel
%I MDPI
%M FZJ-2019-03894
%P 2825 -
%D 2019
%X 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
%F PUB:(DE-HGF)16
%9 Journal Article
%U <Go to ISI:>//WOS:000478999400181
%R 10.3390/en12142825
%U https://juser.fz-juelich.de/record/863978