Discussion/Overview

Generic Graph

The system model used for the TransC/E Combinatinos can be seen below:

Optimization Results

The most relevant CompCnE results are listed below. By convention, tessif uses dynamic dimensioning to allow for different scales of amount of energy transferred. The current conventions can be seen/adjusted via tessif.frused.configurations and are as follows for the results below:

• MW – for energy flows and installed power capacities

• MWh – for amounts of energy and installed storage capacities

• EUR – for costs

• t_CO2 – for emissions (tonns CO2 equivalent)

Commitment

The CompC results generated using the using the respective script, are as follows:

Integrated Global Results

IGR [€ or t_CO2]	cllp	fine	omf	ppsa
capex (ppcd)	0	0	0	0
costs (sim)	688509346	688283345	688509325	688509325
emissions (sim)	6778376	6542108	6815007	6838219
opex (ppcd)	688509352	688283344	688509325	688509325

Installed Capacities

Capacity [MW or MWh]	cllp	fine	omf	ppsa
Battery	100	100	100	100
Biogas CHP	[250.0, 200.0]	[200.0, 250.0]	[200, 250]	[250.0, 200.0]
Biogas Line	variable	variable	variable	variable
Biogas Supply	0	0	0	0
Combined Cycle PP	600	600	600	600
El Demand	1526	1526	1526	1526
Gas Line	variable	variable	variable	variable
Gas Station	357	340	357	variable
Hard Coal CHP	[300.0, 300.0]	[300.0, 300.0]	[300, 300]	[300.0, 300.0]
Hard Coal PP	500	500	500	500
Hard Coal Supply	1912	1912	1912	750
Hard Coal Supply Line	variable	variable	variable	variable
Heat Demand	399	399	399	399
Heat Plant	450	450	450	450
Heat Storage	50	50	50	50
Heatline	variable	variable	variable	variable
Lignite Power Plant	500	500	500	500
Lignite Supply	1250	1250	1250	variable
Lignite Supply Line	variable	variable	variable	variable
Offshore Wind Turbine	150	150	150	150
Onshore Wind Turbine	1100	1100	1100	1100
Power To Heat	100	100	100	100
Powerline	variable	variable	variable	variable
Solar Panel	1100	1100	1100	1100

Powerline Results

TODO intro text here

Summed Loads

Load-Powerline [MW]	cllp	fine	omf	ppsa
Battery	-59184	-59142	-59142	-242275
Battery	69736	69681	69681	290281
Biogas CHP	-66772	-66470	-66470	-73816
Combined Cycle PP	-11270	-11512	-11512	-18191
El Demand	9809506	9809506	9809506	9809506
Hard Coal CHP	0	0	0	0
Hard Coal PP	0	0	0	0
Lignite Power Plant	0	0	0	0
Offshore Wind Turbine	-480373	-479668	-479668	-733059
Onshore Wind Turbine	-10089236	-10090424	-10090424	-9880008
Power To Heat	1069849	1070091	1070091	1075836
Solar Panel	-242253	-242060	-242060	-228272

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Heatline Results

TODO intro text here

Summed Heat Loads

Load-Heatline [MW]	cllp	fine	omf	ppsa
Biogas CHP	-83465	-83088	-83088	-92271
Hard Coal CHP	0	0	0	0
Heat Demand	1116162	1116162	1116162	1116162
Heat Plant	0	0	0	0
Heat Storage	-110220	-109928	-109928	-150745
Heat Storage	136673	136245	136245	191931
Power To Heat	-1059150	-1059390	-1059390	-1065077

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Expansion

The CompC.congestions results generated using the using the respective script, are as follows:

Integrated Global Results

IGR [€ or t_CO2]	cllp	fine	omf	ppsa
capex (ppcd)	41554917514	41554976118	41554977878	1132235997
costs (sim)	42289121225	42289118279	42289118239	2062636560
emissions (sim)	250000	250000	250000	5014819
opex (ppcd)	734204228	734140364	734140364	874603138

Installed Capacities

Capacity [MW or MWh]	cllp	fine	omf	ppsa
Battery	2104	2104	2104	100
Biogas CHP,”[649.6665625, 519.73325]”,”[516.213, 645.266]”,”[516.21323, 645.26654]”,”[250.0, 200.0]”
Biogas Line	variable	variable	variable	variable
Biogas Supply	1299	1290	1290	500
Combined Cycle PP	600	600	600	600
El Demand	1526	1526	1526	1526
Gas Line	variable	variable	variable	variable
Gas Station	847	852	852	variable
Hard Coal CHP,”[300.0, 300.0]”,”[300.0, 300.0]”,”[300.0, 300.0]”,”[630.73628, 630.73628]”
Hard Coal PP	500	500	500	500
Hard Coal Supply	0	0	0	1576
Hard Coal Supply Line	variable	variable	variable	variable
Heat Demand	399	399	399	399
Heat Plant	450	450	450	450
Heat Storage	11357	11272	11272.2.3391	113391.86
Heatline	variable	variable	variable	variable
Lignite Power Plant	500	500	500	500
Lignite Supply	0	0	0	variable
Lignite Supply Line	variable	variable	variable	variable
Offshore Wind Turbine	2347	2346	2346	150
Onshore Wind Turbine	15776	15787	15787	1100
Power To Heat	576	576	576	99
Powerline	variable	variable	variable	variable
Solar Panel	3811	3809	3809	1100

Powerline Results

TODO intro text here

Summed Loads

Load-Powerline [MW]	cllp	fine	omf	ppsa
Battery	-59184	-59142	-59142	-16594
Battery	69736	69681	69681	19032
Biogas CHP	-66772	-66470	-66470	-901176
Combined Cycle PP	-11270	-11512	-11512	-460928
El Demand	9809506	9809506	9809506	9809506
Hard Coal CHP	0	0	0	-5252800
Hard Coal PP	0	0	0	0
Lignite Power Plant	0	0	0	0
Offshore Wind Turbine	-480373	-479668	-479668	-412888
Onshore Wind Turbine	-10089236	-10090424	-10090424	-1959677
Power To Heat	1069849	1070091	1070091	0
Solar Panel	-242253	-242060	-242060	-824472

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Heatline Results

TODO intro text here

Summed Heat Loads

Load-Heatline [MW]	cllp	fine	omf	ppsa
Biogas CHP	-83465	-83088	-83088	-1126470
Hard Coal CHP	0	0	0	-5252800
Heat Demand	1116162	1116162	1116162	1116162
Heat Plant	0	0	0	0
Heat Storage	-110220	-109928	-109928	0
Heat Storage	136673	136245	136245	5263108
Power To Heat	-1059150	-1059390	-1059390	0

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Modified_Expansion

The CompE results generated using the using the respective script, are as follows:

Integrated Global Results

IGR [€ or t_CO2]	cllp	fine	omf	ppsa
capex (ppcd)	41554917514	41554976118	41554977878	36904768288
costs (sim)	42289121225	42289118279	42289118239	37727776780
emissions (sim)	250000	250000	250000	265508
opex (ppcd)	734204228	734140364	734140364	823007841

Installed Capacities

Capacity [MW or MWh]	cllp	fine	omf	ppsa
Battery	2104	2104	2104	100
Biogas CHP,”[649.6665625, 519.73325]”,”[516.213, 645.266]”,”[516.21323, 645.26654]”,”[250.0, 200.0]”
Biogas Line	variable	variable	variable	variable
Biogas Supply	1299	1290	1290	500
Combined Cycle PP	600	600	600	600
El Demand	1526	1526	1526	1526
Gas Line	variable	variable	variable	variable
Gas Station	847	852	852	variable
Hard Coal CHP,”[300.0, 300.0]”,”[300.0, 300.0]”,”[300.0, 300.0]”,”[630.73628, 630.73628]”
Hard Coal PP	500	500	500	500
Hard Coal Supply	0	0	0	1576
Hard Coal Supply Line	variable	variable	variable	variable
Heat Demand	399	399	399	399
Heat Plant	450	450	450	450
Heat Storage	11357	11272	11272.2.3391	113391.86
Heatline	variable	variable	variable	variable
Lignite Power Plant	500	500	500	500
Lignite Supply	0	0	0	variable
Lignite Supply Line	variable	variable	variable	variable
Offshore Wind Turbine	2347	2346	2346	150
Onshore Wind Turbine	15776	15787	15787	1100
Power To Heat	576	576	576	99
Powerline	variable	variable	variable	variable
Solar Panel	3811	3809	3809	1100

Powerline Results

TODO intro text here

Summed Loads

Load-Powerline [MW]	cllp	fine	omf	ppsa
Battery	-59184	-59142	-59142	-242275
Battery	69736	69681	69681	290281
Biogas CHP	-66772	-66470	-66470	-73816
Combined Cycle PP	-11270	-11512	-11512	-18191
El Demand	9809506	9809506	9809506	9809506
Hard Coal CHP	0	0	0	0
Hard Coal PP	0	0	0	0
Lignite Power Plant	0	0	0	0
Offshore Wind Turbine	-480373	-479668	-479668	-733059
Onshore Wind Turbine	-10089236	-10090424	-10090424	-9880008
Power To Heat	1069849	1070091	1070091	1075836
Solar Panel	-242253	-242060	-242060	-228272

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Heatline Results

TODO intro text here

Summed Heat Loads

Load-Heatline [MW]	cllp	fine	omf	ppsa
Biogas CHP	-83465	-83088	-83088	-92271
Hard Coal CHP	0	0	0	0
Heat Demand	1116162	1116162	1116162	1116162
Heat Plant	0	0	0	0
Heat Storage	-110220	-109928	-109928	-150745
Heat Storage	136673	136245	136245	191931
Power To Heat	-1059150	-1059390	-1059390	-1065077

Inflows are negative, outflows positive. Connected zero-flow nodes are not shown:

Computationel Ressources Used

Among the Comp combinations the CompE scenario is the most time consuming. Due to the relatively long timeframe optimized, Tessif added ressource consumption is negligable:

Timing Results

Time [s]	cllp	fine	ppsa	omf
reading	0.2	0.2	0.2	0.2
parsing	0.3	0.2	0.2	0.2
transformation	6.7	0.1	1.9	0.0
optimization	880.7	676.4	316.1	1405.8
post_processing	6.1	3.5	2.5	3.1
result	894.0	680.5	321.0	1409.4

Memory Results

Memory [MB]	cllp	fine	ppsa	omf
reading	3.0	3.0	3.0	3.0
parsing	1.0	1.0	1.0	1.0
transformation	62.0	16.0	3.0	1.0
optimization	3984.0	979.0	707.0	690.0
post_processing	36.0	18.0	13.0	15.0
result	4087.0	1017.0	727.0	710.0

Advanced Graphs

Following sections show the advanced graph representations of the three model-scenario-combinations showing the greatest differences, i.e the Expansion and the Modified-Expansion combinations. Since result variation in between softwares others then Pypsa is low, only the Oemof graph is shown for the Expansion combinations.

To facilitate inter software comparison, the advanced graphs below, are drawn relative to the installed capacity and net energy flow of the demand component "El Demand".

Expansion

Oemof

PyPSA

The Integrated Global Results indicate, that the PyPSA results differ significantly. An initial attempt to relate node size to the installed capacity and net energy flow of the demand component "El Demand" fails, since the resulting size of the Heat Storage component is too large. Thus the advanced system visualization below is plotted relating node size to the installed capacity of the Heat Storage component.

Modified_Expansion

Modifying the PyPSA system model scenario combination, leads to optimization results closer to that of the other softwares. The advanced graph below is therfor again drawn relative to the installed capacity and net energy flow of the demand component "El Demand".

PyPSA

Key Observations

Comparing the above advanced graph visulaizations, three main differences are easily observed between the three scenarios:

1. The non-modified expansion combination of PyPSA differs largely. The "Heat Storage" component is used extensively indicating that the "Hard Coal CHP" component is used to provide power and electricity, while the component is used to store uneeded heat.

2. The advanced graph visualization of the modified PyPSA expansion combination resembles that of Oemof much closer in comparison to the non-modified variation.

3. For the optimal solution Onshore, Solar, Offshore and Heat Storage are used the most having relatively large installed capacities compared to the compartively low characteristic value / capacity factor.

Key Conclusions

1. The Key Goal could be served in the sense of developing a reference supply system model in conjunction with two relevant and contemporary scenario formulations to test out the modelling softwares Calliope, Fine, Oemof and Pypsa.

2. All of the 5 aims (Thesis-> Method -> Modelling -> MSC Selection ) formulated, with regards to component focused model behaviour, were successfully addressed:

   1. Integration of volatile renewable energy sources into an existing system:

      • The components Solar Panel, Onshore Wind Turbine and Offshore Wiind Turbine represent succesfully integrated, volatile renewable energy sources, of which the maximum power produced is constraint via hourly resolved load profiles as discussed in Reimer, Ammon in Subsection - 3.2.4 and Subsection - 3.2.5

   2. Integration of energy storage technologies into an existing system:

      • The components Battery and Heat Storage represent successfully integrated electrical and thermal energy storage components respectively. They are parameterized in accordance to contemporary tecnical specifications as discussed in Reimer, Ammon Subsection - 3.2.10

   3. Year-round, hourly-resolved energy demands based on ambient climatic conditions:

      • The components El Demand and Heat Demand represent succesfully modeled, hourly-resolved energy demands based on real world considerations as laid out in Reimer, Ammon Subsection 3.2.1

   4. Cost optimally dispatching energy sources to meet the system’s demand:

      • The use case is succesfully modelled via the CompC combination of which the generic graph representation can be seen above.

      • The results are evaluated using the codes shown in the respective code section and shown above.

   5. Reaching emission-goals cost optimally on given constraints, potentially expanding or adding certain low-emission components.

      • The use case is succesfully modelled via the CompE combination of which the generic graph representation can be seen above.

      • The results are evaluated using the codes shown in the respective code section and shown above.

3. In addition to that following insights were gained with regards to the softwares used:

   1. Given the same input it is possible, but not necessarily directly implied, to produce the exact same results on relatively large and complex energy supply system models for all softwares investigated.

   2. Emission constraint expansion problems reveal software specific differences more clearly in comparison to pure commitment problems.

   3. Emission allocation differs between softwares. Leading to potentially large differences as demonstrated by the unmodified expansion results.

      The possibility to assign emissions to storage and sector coupling components, in particular, varies significantly between softwares.

      See also Reimer, Ammon - Subsections 4.3.2 and 4.3.3 for an in detail discussed comparison between oemof and PyPsa in this regard.

   4. Storage parameterization varies significantly between softwares. These constitute mainly of:

      • Initial State of Charge

      • Emission allocation – Differences beeing in both, the possibility to allocate emissions in the first place, and the option to which energy flow allocation is possible (inflow, outflow or both).

      • Cost allocation – Difference beeing wheter the costs are allocatable energy flow specific (inflow, outflow or both)l or only to State of Charge differences between first and final time step.

   5. Internal represtation differences in cost and emission allocation can be compnensated without actually altering the defacto interpreation of the input, if so desired. Further more this alteration is done relatively simple using tessif, as is demonstrated in the respective code snippet below.

   6. Tessif facilitates comparison, by allowing straight forward energy supply sytem model creation, transformation, optimization, post-processing, result comparison and visualization as demonstrated by the code with which the abov results were generated.

   7. On comparitevly medium to large timeframes, like the above 8760 hourly steps, Tessif introduced need of computational ressources is negligable (see figures in section Computationel Ressources Used).

      Comparing the computational ressourcess needed between softwares on the above model-scenario-combinations, it seems as though tessif-pypsa is generally more efficient than tessif-fine, which is more efficient than tessif-calliope, which in turn is more efficient than tessif-oemof.

References

• Max Reimer, Mathias Ammon, und Kristin Abel-Günther. Entwicklung eines Komponenten basierten Szenarios zum Vergleich von Free and Open Source Energiesystemmodellierungssoftware in Python, 2022

• Max Reimers Tessif Documentation