This document discusses the importance of modelling energy systems with detailed representations of surrounding regions. It presents a case study comparing different levels of detail in modelling Germany's energy system within the context of surrounding countries like France, Poland, Austria and others. The study uses EnergyPLAN to model each country's system individually and also connected together. It finds that modelling with greater detail, representing each surrounding country separately rather than aggregated, reveals more nuanced results about how increased wind power in Germany may affect its own CO2 emissions versus the overall connected system's emissions. The level of geographic detail is important for policymakers and researchers to understand impacts across interconnected energy infrastructures.

1. Modelling an individual country within the
context of the surrounding energy systems – the
importance of detail
Jakob Zinck Thellufsen
Assistant Professor, Aalborg University

2. Introduction
• Modelling focus on a specific area
• Country
• City
• Region
• But how important are the surroundings?
• How does development of the energy systems
affect results?
2

3. Surrounding energy systems
3

4. Methodology
• Investigating two principal items
1) The impact of energy system development
2) How dissagregated the surroundings are
• Island mode
• One surrounding region
• Each surrounding country as one model
• Germany in the context of
• Netherlands, Belgium, France, Poland, Austria and Czech
Republic
4

5. The use of EnergyPLAN and
EPlanFlow
• We use consistent
models for each country
• Current state and with
increased wind
• All are built in
EnergyPLAN
• We use the add on
EPlanFlow to investigate
the flows in the
connected systems
Island Mode Case country
and 1
aggregated
region
(Aggregated
Surroundings)
Case country
and each
individual
country
(Detailed
Surroundings)
All reference
scenario
Only case
country
modelled
Model case
country and
aggregate to 1
region
Model all
countries
Case study
renewable, the
rest reference
Only case
country
modelled
Model case
country and
aggregate to 1
region
Model all
countries
All renewable Not relevant,
same as above
Model case
country and
aggregate to 1
region
Model all
countries
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6. EnergyPLAN
6

7. EPlanFlow
7
EnergyPLAN simulation of
each node
Economic simulation
Powerflow
optimisation of
response curves
from EnergyPLAN
EnergyPLAN simulation of
each node
Technical simulation
EnergyPLAN
scenario file for
each node
Time series of
demands and
renewable
production
Network
constraints
between nodes
0
10
20
30
40
50
60
70
0 20 40 60 80
0
10
20
30
40
50
60
70
0 20 40 60 80
Response curves for
every hour in every node
Time series file of
import/export exchange in
every node
-10
-8
-6
-4
-2
0
2
4
6
0 10 20 30
-10
-5
0
5
10
15
0 10 20 30

8. The connection
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FR
DE
PL
CZ
AT
BE
NL

9. The changes in wind
Country Reference [MW] Renewable [MW]
Germany 27201.50 271740.00
Netherlands 1992.32 50165.00
Belgium 353.46 353.46
Czech Republic 181.03 29439.00
Poland 979.50 63485.00
France 5178.48 5178.48
Austria 934.50 25725.00
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Country Reference [TWh] Renewable [TWh]
Germany 46.05 460.05
Netherlands 3.66 92.27
Belgium 0.65 0.65
Czech Republic 0.28 45.40
Poland 1.71 110.65
France 9.32 9.32
Austria 1.79 49.30

10. Results
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11. Results
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12. Results
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13. Conclusions
• In all cases does more wind lead to fewer CO2
emissions
• However, the fully detailed one shows that this might
happen in Germany
• The level of detail influences decisions
• The planner and researcher needs to know what
happens
• Increase system integration
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14. Thank you for your attention!
Questions?
Contact:
+45 99 40 82 89
jakobzt@plan.aau.dk
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