TY  - JOUR
AU  - Kannengießer, Timo
AU  - Hoffmann, Maximilian
AU  - Kotzur, Leander
AU  - Stenzel, Peter
AU  - Schuetz, Fabian
AU  - Peters, Klaus
AU  - Nykamp, Stefan
AU  - Stolten, Detlef
AU  - Robinius, Martin
TI  - Reducing Computational Load for Mixed Integer Linear Programming: An Example for a District and an Island Energy System
JO  - Energies
VL  - 12
IS  - 14
SN  - 1996-1073
CY  - Basel
PB  - MDPI
M1  - FZJ-2019-03894
SP  - 2825 -
PY  - 2019
AB  - 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
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000478999400181
DO  - DOI:10.3390/en12142825
UR  - https://juser.fz-juelich.de/record/863978
ER  -