fix bug for urban_fraction factor, rewrite pattern for defining country, location and temporal resolution, add more comments for endogenously retrofitting
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lisazeyen
parent
63f1e99c8b
commit
e511dac148
@ -1288,17 +1288,25 @@ def add_heat(network):
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print("adding retrofitting endogenously")
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# resample heat demand to not overestimate retrofitting
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heat_demand_r = heat_demand.resample(opts[1]).mean()
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# costs and floor area per country
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retro_cost = pd.read_csv(snakemake.input.retro_cost_energy,
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# resample heat demand temporal 'heat_demand_r' depending on in config
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# specified temporal resolution, to not overestimate retrofitting
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hours = list(filter(re.compile(r'^\d+h$', re.IGNORECASE).search, opts))
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if len(hours)==0:
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hours = [n.snapshots[1] - n.snapshots[0]]
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heat_demand_r = heat_demand.resample(hours[0]).mean()
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# retrofitting data 'retro_data' with 'costs' [EUR/m^2] and heat
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# demand 'dE' [per unit of original heat demand] for each country and
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# different retrofitting strengths [additional insulation thickness in m]
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retro_data = pd.read_csv(snakemake.input.retro_cost_energy,
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index_col=[0, 1], skipinitialspace=True,
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header=[0, 1])
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# heated floor area [10^6 * m^2] per country
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floor_area = pd.read_csv(snakemake.input.floor_area, index_col=[0, 1])
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network.add("Carrier", "retrofitting")
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# share of space heat demand
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# share of space heat demand 'w_space' of total heat demand
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w_space = {}
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for sector in sectors:
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w_space[sector] = heat_demand_r[sector + " space"] / \
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@ -1310,47 +1318,53 @@ def add_heat(network):
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for name in network.loads[network.loads.carrier.isin([x + " heat" for x in heat_systems])].index:
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ct = name[:2]
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node = name[:5]
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node = network.buses.loc[name, "location"]
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ct = pop_layout.loc[node, "ct"]
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f = urban_fraction[node] if "urban" in name else (urban_fraction[node])
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# weighting 'f' depending on the size of the population at the node
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f = urban_fraction[node] if "urban" in name else (1-urban_fraction[node])
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if f == 0:
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continue
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# get sector (residential/services) or "tot" for "urban central"
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# get sector name ("residential"/"services"/or both "tot" for urban central)
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sec = [x if x in name else "tot" for x in sectors][0]
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# get square meters at node urban/rural
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square_metres_node = ((pop_layout.loc[node].fraction
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# get floor aread at node and region (urban/rural) in m^2
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floor_area_node = ((pop_layout.loc[node].fraction
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* floor_area.loc[ct, "value"] * 10**6).loc[sec] * f)
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# total heat demand at node
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demand = (network.loads_t.p_set[name].resample(opts[1])
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# total heat demand at node [MWh]
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demand = (network.loads_t.p_set[name].resample(hours[0])
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.mean())
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# space heat demand at node
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# space heat demand at node [MWh]
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space_heat_demand = demand * w_space[sec][node]
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# normed time profile of space heat demand 'space_pu' (values between 0-1),
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# p_max_pu/p_min_pu of retrofitting generators
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space_pu = (space_heat_demand / space_heat_demand.max()).to_frame(name=node)
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# maximum heat savings at node
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dE = retro_cost.loc[(ct, sec), ("dE")]
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# get p_nom_max for different retrofitting strengths
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# minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
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dE = retro_data.loc[(ct, sec), ("dE")]
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# get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
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dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
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# convert costs per m^2 to costs per MW
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capital_cost = retro_cost.loc[(ct, sec), ("cost")] * square_metres_node / \
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# convert costs Euro/m^2 -> Euro/MWh
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capital_cost = retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
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((1 - dE) * space_heat_demand.max())
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# number of possible retrofitting measures 'strengths' (set in list at config.yaml 'l_strength')
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# given in additional insulation thickness [m]
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# for each measure, a retrofitting generator is added at the node
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strengths = retro_data.columns.levels[1]
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strengths = retro_cost.columns.levels[1]
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# check that stronger retrofitting has higher costs per MWh than lower retrofitting
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# check that ambitious retrofitting has higher costs per MWh than moderate retrofitting
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if (capital_cost.diff() < 0).sum():
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print(
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"warning, costs are not linear for ", ct, " ", sec)
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s = capital_cost[(capital_cost.diff() < 0)].index
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strengths = strengths.drop(s)
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# reindex back to hourly resolution
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# reindex normed time profile of space heat demand back to hourly resolution
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space_pu = (space_pu.reindex(index=heat_demand.index)
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.fillna(method="ffill"))
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# add for each retrofitting strength a generator with heat generation profile following the profile of the heat demand
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for strength in strengths:
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network.madd('Generator',
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[node],
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@ -1358,8 +1372,7 @@ def add_heat(network):
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bus=name,
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carrier="retrofitting",
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p_nom_extendable=True,
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p_nom_max=dE_diff[strength] * space_heat_demand.max(),
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dE=dE_diff[strength],
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p_nom_max=dE_diff[strength] * space_heat_demand.max(), # maximum energy savings for this renovation strength
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p_max_pu=space_pu,
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p_min_pu=space_pu,
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country=ct,
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