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@@ 381-433 (lines=53) @@
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]
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def import_ch4_demandTS():
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"""Import from the PyPSA-eur-sec run the timeseries of
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residential rural heat per neighbor country.
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This timeserie is used to calculate:
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- the global (yearly) heat demand of Norway (that will be supplied by CH4)
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- the normalized CH4 hourly resolved demand profile
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Parameters
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----------
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None.
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Returns
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-------
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Norway_global_demand: Float
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Yearly heat demand of Norway in MWh
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neighbor_loads_t: pandas.DataFrame
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Normalized CH4 hourly resolved demand profiles per neighbor country
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"""
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cwd = Path(".")
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target_file = (
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cwd
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/ "data_bundle_egon_data"
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/ "pypsa_eur_sec"
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/ "2022-07-26-egondata-integration"
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/ "postnetworks"
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/ "elec_s_37_lv2.0__Co2L0-1H-T-H-B-I-dist1_2050.nc"
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)
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network = pypsa.Network(str(target_file))
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# Set country tag for all buses
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network.buses.country = network.buses.index.str[:2]
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neighbors = network.buses[network.buses.country != "DE"]
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neighbors = neighbors[
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(neighbors["country"].isin(countries))
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& (neighbors["carrier"] == "residential rural heat")
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].drop_duplicates(subset="country")
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neighbor_loads = network.loads[network.loads.bus.isin(neighbors.index)]
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neighbor_loads_t_index = neighbor_loads.index[
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neighbor_loads.index.isin(network.loads_t.p_set.columns)
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]
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neighbor_loads_t = network.loads_t["p_set"][neighbor_loads_t_index]
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Norway_global_demand = neighbor_loads_t[
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"NO3 0 residential rural heat"
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].sum()
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for i in neighbor_loads_t.columns:
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neighbor_loads_t[i] = neighbor_loads_t[i] / neighbor_loads_t[i].sum()
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return Norway_global_demand, neighbor_loads_t
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def import_power_to_h2_demandTS():
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@@ 436-483 (lines=48) @@
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return Norway_global_demand, neighbor_loads_t
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def import_power_to_h2_demandTS():
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"""Import from the PyPSA-eur-sec run the timeseries of
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industry demand heat per neighbor country and normalize it
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in order to model the power-to-H2 hourly resolved demand profile.
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Parameters
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----------
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None.
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Returns
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-------
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neighbor_loads_t: pandas.DataFrame
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Normalized CH4 hourly resolved demand profiles per neighbor country
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"""
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cwd = Path(".")
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target_file = (
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cwd
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/ "data_bundle_egon_data"
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/ "pypsa_eur_sec"
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/ "2022-07-26-egondata-integration"
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/ "postnetworks"
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/ "elec_s_37_lv2.0__Co2L0-1H-T-H-B-I-dist1_2050.nc"
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)
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network = pypsa.Network(str(target_file))
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# Set country tag for all buses
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network.buses.country = network.buses.index.str[:2]
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neighbors = network.buses[network.buses.country != "DE"]
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neighbors = neighbors[
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(neighbors["country"].isin(countries))
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& (
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neighbors["carrier"] == "residential rural heat"
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) # no available industry profile for now, using another timeserie
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] # .drop_duplicates(subset="country")
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neighbor_loads = network.loads[network.loads.bus.isin(neighbors.index)]
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neighbor_loads_t_index = neighbor_loads.index[
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neighbor_loads.index.isin(network.loads_t.p_set.columns)
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]
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neighbor_loads_t = network.loads_t["p_set"][neighbor_loads_t_index]
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for i in neighbor_loads_t.columns:
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neighbor_loads_t[i] = neighbor_loads_t[i] / neighbor_loads_t[i].sum()
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return neighbor_loads_t
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def insert_ch4_demand(global_demand, normalized_ch4_demandTS):
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openego /
eGon-data
