TY  - JOUR
AU  - Kotzur, Leander
AU  - Nolting, Lars
AU  - Hoffmann, Maximilian
AU  - Groß, Theresa
AU  - Smolenko, Andreas
AU  - Priesmann, Jan
AU  - Büsing, Henrik
AU  - Beer, Robin
AU  - Kullmann, Felix
AU  - Singh, Bismark
AU  - Praktiknjo, Aaron
AU  - Stolten, Detlef
AU  - Robinius, Martin
TI  - A modeler's guide to handle complexity in energy systems optimization
JO  - Advances in applied energy
VL  - 4
SN  - 2666-7924
CY  - [Amsterdam]
PB  - Elsevier ScienceDirect
M1  - FZJ-2021-03364
SP  - 100063 -
PY  - 2021
AB  - Determining environmentally- and economically-optimal energy systems designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models by applying various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review different approaches for handling complexity. We first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for energy system modelers who encounter computational limitations.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001022694400008
DO  - DOI:10.1016/j.adapen.2021.100063
UR  - https://juser.fz-juelich.de/record/894712
ER  -