DOI: 10.5281/zenodo.17702891
title: Mixed-Integer Energy System Optimization Models for Germany: Seven Spatial Aggregations with Monte Carlo–Sampled UC, Dispatch, and Expansion Scenarios

description: This dataset provides seven sets of mixed-integer linear energy system optimization models (ESOMs) for the German power system, generated with the REMix framework.

All models are formulated as mixed-integer programs (MIPs) that jointly optimize unit commitment, dispatch, and capacity expansion of renewable technologies, natural-gas-fired plants, storage units, and transmission lines across 8,760 hourly time steps (= 1 year; exception for miso_244k). Renewable generation from wind and solar is modeled with continuous variables.

The full-resolution system comprises 488 nodes (477 within Germany and 11 neighboring countries) and 643 lines. The six aggregation levels (miso_244k has reduced horizon compared to miso_47M) reduce this spatial detail to varying degrees, resulting in models of different sizes (see Table 2 for details) and computational difficulty, ranging from large-scale MIP instances to more compact variants. Together, these datasets provide a scalable benchmark suite for energy system planning under uncertainty.

Each set has been obtained using a different spatial aggregation of the complete system (see Table 1 for details). Each set includes multiple Monte Carlo–sampled instances to account for uncertainties in weather data and techno-economic parameters. The models follow the mathematical description of the expAll models in Wetzel et al. (2025) for the underlying LP part. Additionally, the following integer constraints are enforced:

 - Units under the column “discrete units” are modeled with integer variables enforcing integral expansion decisions. Lignite, coal, and OCGT units are included but cannot be expanded.

 - The unit commitment formulation enforces partial load constraints with two activities for CCGT, Coal, Lignite. Minimum up- and downtime constraints are enforced for CCGT, Coal, and Lignite.

- Lithium-ion storage and pumped-storage power plants are modeled with continuous variables and do not involve discrete expansion decisions.

Investment structure.Capacity expansion decisions are modeled using static investment variables defined for the full optimization horizon. Any added capacity is assumed to be available for the entire modeled year; the model does not include commissioning times or multi-period investment stages. The transmission topology is fixed and can only be expanded in capacity, technologies are not bound to their initial topology.

Transmission modeling.AC transmission lines are represented using a DC load-flow approximation with a B–θ (angle) formulation. HVDC lines are modeled using a capacity-constrained transport representation. No N-1 contingencies or security-constrained unit-commitment features are included in these instances.

Additional documentation on the remix modeling can be found under the REMix homepage.




set
discrete units
spatial aggregation (nodes)
number of lines
number of nodes


miso_244k*
CCGT, Coal, Lignite, HVAC, HVDC
Germany: 10; NRW** 32; Neighbors: 1
78
43


miso_2M
CCGT, HVAC, HVDC
Germany: 1; Neighbors: 9
9
10


miso_4M
CCGT, Coal, Lignite, HVAC, HVDC
Germany: 2; Neighbors: 1
3
3


miso_47M
CCGT, Coal, Lignite, HVAC, HVDC
Germany: 10; NRW: 32; Neighbors: 1
78
43


miso_82M
CCGT, Coal, Lignite, HVAC, HVDC
NRW: 130; Neighbors: 1
191
131


remix_105M
CCGT, Coal, Lignite, HVAC, HVDC
Germany: 10; NRW: 88; Neighbors: 11
167
109


remix_243M
CCGT, Coal, Lignite, HVAC, HVDC
Germany: 6; NRW: 253; Neighbors: 11
343
270




Table 1: model information.* miso_244k models only a period of 48 hours.** NRW = North Rhine-Westphalia (a German state).




set
number of instances
number of constraints
number of variables
number of binaries/integers
number of non-zeros


miso_244k
100
73,518
67,386
5,445
244,293


miso_2M
100
928,790
880,606
35,052
2,829,975


miso_4M
96
1,279,124
1,103,903
157,692
4,590,593


miso_47M
100
16,313,022
13,597,290
972,477
47,310,667


miso_82M
20
24,698,935
24,356,961
867,465
82,773,747


remix_105M
100
33,194,746
28,455,474
3,364,138
105,960,838


remix_243M
100
77,498,047
66,714,360
7,726,975
243,495,063




Table 2: model sizes.