Add n largest district heating systems as subnodes

[cpschau]

In this pull request, we introduce subnodes to represent the n largest district heating networks in Germany, as identified from the open Fernwärmeatlas dataset (available upon request at Fernwärmeatlas under CC BY 4.0 license). Largest means the amount of annual district heating feed-in. The number of these largest networks that are modeled explicitly can be determined using the configuration parameter sector:district_heating:add_subnodes. These district heating subnodes are integrated into existing clusters (referred to as mother nodes), which continue to represent the remaining smaller district heating systems within their respective regions.

The annual feed-in data of the district heating systems, as recorded in the dataset, is used to assign urban central heat loads to the subnodes, with a corresponding reduction in the load of the mother nodes.

Existing CHP generation capacities from the MaStR database are allocated to these new subnodes based on their geolocation. For mapping, the NUTS3 regions corresponding to the district heating systems are used. Through a modification in the add_existing_baseyear function within the pypsa-eur subworkflow, a CHP plant is either added to a subnode or remains in a mother node. Additionally, other infrastructure components, such as links and storage facilities, are connected to the subnodes, offering investment options for the system optimization.

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⚠️ Config changes detected!

Results may differ due to these changes:

diff --git a/config/config.yaml b/config/config.yaml
index 619f308..1d895fe 100644
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -4,7 +4,7 @@
 
 # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#run
 run:
-  prefix: 20240814limitseq
+  prefix: 20242008_dh_subnodes_off
   name:
   # - CurrentPolicies
   - KN2045_Bal_v4
@@ -222,6 +222,7 @@ sector:
       2040: 0.6
       2045: 0.8
       2050: 1.0
+    add_subnodes: false
   central_heat_vent: true
   co2_spatial: true
   biomass_spatial: true

Variables comparison

	MAPE
Secondary Energy|Gases|Biomass	2982966.88%
Final Energy|Industry|Gases|Biomass	2858548.62%
Final Energy|Gases|Biomass	2321177.93%
Final Energy|Industry excl Non-Energy Use|Gases|Biomass	2321177.86%
Final Energy|Non-Energy Use|Gases|Biomass	1632094.17%
Emissions|CO2|Energy|Production|From Gases	44885.82%
Capacity Additions|Heat|Resistive heater	8516.97%
Secondary Energy|Heat|Oil	1684.71%
Primary Energy|Oil|Heat	1583.39%
Secondary Energy|Electricity|Oil	1035.48%
Capacity|Hydrogen|Reservoir	1002.31%
Capacity Additions|Hydrogen|Reservoir	1000.74%
Primary Energy|Oil|Electricity	766.75%
Investment|Energy Supply|Heat|Resistive heater	58.39%
Primary Energy|Biomass|Gases	22.54%

MAPE: Mean Absolute Percentage Error
Threshold: MAPE > 5%
Only variables reaching the threshold are shown. Find the equivalent plot for all of them below.

Variables changed plots

Main branch	Feature branch

General Plots comparison

Main branch	Feature branch

General Files comparison

	Numeric	Status	NMAE	MAPE
csvs/curtailment.csv	72.2%	⚠️Changed	0.05	2.10e+02%
ariadne/exported_variables_full.xlsx		⚠️ NaN mismatch
ariadne/exported_variables.xlsx		⚠️ NaN mismatch
csvs/price_statistics.csv	57.1%	✅ Almost equal	0.09	0.9%
csvs/nodal_cfs.csv	63.5%	✅ Almost equal	0.01	65.4%
csvs/cfs.csv	71.8%	✅ Almost equal	0.04	2.71e+02%
csvs/nodal_capacities.csv	64.2%	✅ Almost equal	0.00	3.49e+06%
csvs/cumulative_cost.csv	84.7%	✅ Almost equal	0.08	0.0%
csvs/nodal_supply_energy.csv	59.1%	✅ Almost equal	0.00	1.85e+07%
csvs/capacities.csv	72.2%	✅ Almost equal	0.01	5.73e+04%
csvs/energy.csv	72.6%	✅ Almost equal	0.02	1.26e+05%
csvs/costs.csv	64.7%	✅ Almost equal	0.02	1.06e+05%
csvs/supply.csv	64.3%	✅ Almost equal	0.00	2.43e+04%
csvs/supply_energy.csv	64.3%	✅ Almost equal	0.02	1.35e+05%
csvs/metrics.csv	54.5%	✅ Almost equal	0.07	0.5%
csvs/market_values.csv	76.7%	✅ Almost equal	0.05	1.7%
csvs/prices.csv	81.3%	✅ Almost equal	0.00	0.8%
csvs/nodal_costs.csv	57.8%	✅ Almost equal	0.00	9.99e+06%
csvs/weighted_prices.csv		✅ Equal

MAPE: Mean Absolute Percentage Error
NMAE: Mean Absolute Error on Min-Max Normalized Data
Status Threshold: NMAE > 0.05 and MAPE > 5%

Comparing add_subnodes (c081ff4) with main (1eef9c6).
Branch is 15 commits ahead and 0 commits behind.
Last updated on 2024-08-27 12:04:51 CEST.