Discussion/Overview

Generic Graph

The system model used for the LossLC combination can be seen below:

Optimization Results

The LossLC 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)

The LossLC results are generated using the skript from below and are as follows:

Integrated Global Results

IGR [€ or t_CO2]	cllp	fine	omf	ppsa
capex (ppcd)	0	0	0	0
costs (sim)	202437193	202259102	202259102	202259102
emissions (sim)	441567	443159	443159	443159
opex (ppcd)	202437194	202259103	202259103	202259103

The Integrated Global Result bar plots are created using the code below.

Installed Capacity

Capacity [MW or MWh]	cllp	fine	omf	ppsa
BHKW	[13513.696969697, 8576.0]	[13513.697, 8576.0]	[8576.0, 13513.69697]	[13513.69697, 8576.0000001923]
Biogas	variable	variable	variable	variable
Biogas plant	25987	25987	25987	25987
Car charging Station	3473	3473	3473	3473
Coal Supply	102123	102123	102123	102123
Coal Supply Line	variable	variable	variable	variable
Commercial Demand	41969	41969	41969	41969
District Heating	variable	variable	variable	variable
District Heating Demand	50000	50000	50000	50000
Gas Station	45325	45325	45325	variable
Gaspipeline	variable	variable	variable	variable
GuD	26742	26742	26742	26742
HKW	[61273.96, 24509.584]	[61273.998, 24509.599]	[61273.96, 24509.6]	[61273.96, 24509.584]
HKW2	43913	43913	43913	43913
High Voltage Grid	variable	variable	variable	variable
High Voltage Transfer Grid	variable	0	variable	72141
Household Demand	36177	36177	36177	36177
Industrial Demand	63093	63093	63093	63093
Low Voltage Grid	variable	variable	variable	variable
Low Voltage Transfer Grid	variable	54703	variable	54703
Medium Voltage Grid	variable	variable	variable	variable
Offshore Wind Power	18760	18760	18760	18760
Onshore Wind Power	66599	66599	66599	66599
Power to Heat	50000	50000	50000	50000
Solar Panel	99605	99605	99605	99605
Solar Thermal	14343	14343	14343	14343

Medium Voltage Grid Summed Loads

Load-Medium Voltage Grid [MW]	cllp	fine	omf	ppsa
Car charging Station	37026	37026	37026	37026
High Voltage Transfer Grid	-867448	-871089	-871089	-871089
High Voltage Transfer Grid	0	0	0	0
Industrial Demand	1229008	1229008	1229008	1229008
Low Voltage Transfer Grid	-111125	-111125	-111125	-111125
Low Voltage Transfer Grid	640388	644028	644028	644028
Onshore Wind Power	-1099866	-1099866	-1099866	-1099866
Power to Heat	172017	172017	172017	172017

Computational Ressources Used

The computational results are generated using the respective estimation scripts as well as the subsequent plotting scripts.

Timings Results

Time [s]	cllp	fine	ppsa	omf
reading	0.6	0.5	0.4	0.4
parsing	0.5	0.5	0.4	0.4
transformation	3.0	0.3	0.8	0.0
optimization	2.4	1.0	0.7	0.6
post_processing	6.5	1.1	0.7	0.8
result	12.9	3.4	3.0	2.4

Memory Results

Memory [MB]	cllp	fine	ppsa	omf
reading	1.0	0.0	0.0	0.0
parsing	0.0	0.0	0.0	0.0
transformation	34.0	13.0	2.0	0.0
optimization	16.0	3.0	2.0	2.0
post_processing	21.0	1.0	1.0	1.0
result	72.0	18.0	4.0	3.0

Key Conclusions

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

2. None of the 4 aims formulated, with regards to gird focused model behaviour, are specifically addressed with this 'Lossless Commitment' model scenario combination. It however lays the foundation for the Transformer Commitment/Expansion which directly address all of these aims.

3. Even on a relatively complex model-scenario-combination modelling ideal grid behaviours it could be shown that the optimal solutions found, using the softwares Calliope, Fine, Oemof and Pypsa through tessif, deviate by less than 1% relative to each other.

Reference

• Tim Hanke, Mathias Ammon, und Kristin Abel-Günther. Entwickeln eines netzbasierten Energiesystemmodells zum Vergleich von Free und Open Source Energiesystemmodellierungssoftware in Python, 2022