by Karl-Kiên Cao;

Description: Within the research project BEAM-ME (2016-2019, funded by the German Ministry of Economic Affairs and Energy) several approaches to reduce total computing times of Energy System Optimiztaion Models (ESOMs) were examined. This data contains the model instances that were used to assess the achievable speed-up when such approaches where applied to an ESOM developed at the German Aerospace Center - REMix: The speed-up approaches where applied to two versions of the model for: (1) Dispatch optimization "disp" and (2) Expansion planning of energy storage and electrictiy transmission capacities "exp". The examined approaches are: Spatial Aggregation "spatial", Temporal Aggregation (Down-sampling) "temp", Rolling horizon dispatch "rh", Temporal zooming (heuristic decomposition on temporal scale) "temp_zoom". The uploaded data set contains so called dump files. These files can be executed by the modeling language GAMS and consist of both all input data and REMix source code. As result binary data files named results.gdx will be obtained in a folder named __output. From these files all accuracy indicators can be derived. In addition, a logging file and a listing file will be obtained which contain most of the data to be evaluated as performance indicators. Each speed-up approach is characterized by its parameters which are: Spatial aggregation - spatial resolution (number of discrete regions), Temporal aggregation - temporal resolution (size of temporally averaged time steps), Rolling horizon dispatch - number of intervals the time horizon is decomposed into and overlap size between two subsequent time intervals, Temporal zooming - number of intervals, temporal resolution of a presequently executed model run used for generating basic model inputs, and number of threads distinguished into number of parallel sub-model runs and number of available threads for solver (barrier algorithm) parallelization.

Disciplines: 4.0.8.1 → Operations research → Mathematical optimization; 5.15.13.8.2 → Energy policy|Energy → Renewable energy policy|Renewable energy; 4.4.6.3 → Systems engineering → Systems analysis

Keywords: Energy systems analysis; Energy system optimization models; Linear programming; Mathematical decomposition; REMix;

DOI: 10.23728/b2share.3717dab82cbb4de0a02726ab3ff7702e

PID: 11304/0cff951a-fe8b-43a9-8849-db1bdd5106e6