bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

fix typos

Browse Source
This commit is contained in:
Fabian 2022-11-18 09:08:07 +01:00
parent 45cda4e2cd
commit df4eb85a19
10 changed files with 24 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
doc/data.csv
View File

@ -23,7 +23,7 @@ Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown
Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,
District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,

Can't render this file because it has a wrong number of fields in line 27.

2
doc/installation.rst
View File

@ -54,7 +54,7 @@ The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>
xarray version >= 0.15.1, you will need the latest master branch of
atlite version 0.0.2.
You can create an enviroment using the environment.yaml file in pypsa-eur/envs:
You can create an environment using the environment.yaml file in pypsa-eur/envs:
.. code:: bash

2
doc/release_notes.rst
View File

@ -28,7 +28,7 @@ incorporates retrofitting options to hydrogen.
* New rule ``cluster_gas_network`` that clusters the gas transmission network
data to the model resolution. Cross-regional pipeline capacities are aggregated
(while pressure and diameter compability is ignored), intra-regional pipelines
(while pressure and diameter compatibility is ignored), intra-regional pipelines
are dropped. Lengths are recalculated based on the regions' centroids.
* With the option ``sector: gas_network:``, the existing gas network is

2
doc/spatial_resolution.rst
View File

@ -15,7 +15,7 @@ The total number of nodes for Europe is set in the ``config.yaml`` file
under ``clusters``. The number of nodes can vary between 37, the number
of independent countries / synchronous areas, and several
hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve
with a commerical solver like Gurobi.
with a commercial solver like Gurobi.
Not all of the sectors are at the full nodal resolution, and some

6
scripts/add_existing_baseyear.py
View File

@ -165,11 +165,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
# drop assets which are already phased out / decomissioned
# drop assets which are already phased out / decommissioned
phased_out = df_agg[df_agg["DateOut"]<baseyear].index
df_agg.drop(phased_out, inplace=True)
# calculate remaining lifetime before phase-out (+1 because assumming
# calculate remaining lifetime before phase-out (+1 because assuming
# phase out date at the end of the year)
df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
@ -251,7 +251,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
# existing capacities are split evenly among regions in every country
inv_ind = [i for i in inv_busmap[ind]]
# for offshore the spliting only inludes coastal regions
# for offshore the splitting only includes coastal regions
inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]

4
scripts/build_industry_sector_ratios.py
View File

@ -618,7 +618,7 @@ def nonmetalic_mineral_products():
# (c) clinker production (kilns),
# (d) Grinding, packaging.
# (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
# Temperatures above 1400C are required for procesing limestone and sand into clinker.
# Temperatures above 1400C are required for processing limestone and sand into clinker.
# Everything (except current electricity and heat consumption and existing biomass)
# is transformed into methane for high T.
@ -1110,7 +1110,7 @@ def non_ferrous_metals():
# Aluminium secondary route
# All is coverted into secondary route fully electrified.
# All is converted into secondary route fully electrified.
sector = "Aluminium - secondary production"

16
scripts/build_retro_cost.py
View File

@ -33,7 +33,7 @@ The basic equations:
E_space = H_losses - H_gains
Heat losses constitute from the losses through heat trasmission (H_tr [W/m²K])
Heat losses constitute from the losses through heat transmission (H_tr [W/m²K])
(this includes heat transfer through building elements and thermal bridges)
and losses by ventilation (H_ve [W/m²K]):
@ -71,7 +71,7 @@ import xarray as xr
# thermal conductivity standard value
k = 0.035
# strenght of relative retrofitting depending on the component
# strength of relative retrofitting depending on the component
# determined by historical data of insulation thickness for retrofitting
l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]},
index=["Roof", "Wall", "Floor"])
@ -89,8 +89,8 @@ tau_H_0 = 30
# constant parameter alpha_H_0 [-] according to EN 13790 seasonal method
alpha_H_0 = 0.8
# paramter for solar heat load during heating season -------------------------
# tabular standard values table p.8 in documenation
# parameter for solar heat load during heating season -------------------------
# tabular standard values table p.8 in documentation
external_shading = 0.6 # vertical orientation: fraction of window area shaded [-]
frame_area_fraction = 0.3 # fraction of frame area of window [-]
non_perpendicular = 0.9 # reduction factor, considering radiation non perpendicular to the glazing[-]
@ -279,7 +279,7 @@ def prepare_building_stock_data():
def prepare_building_topology(u_values, same_building_topology=True):
"""
reads in typical building topologies (e.g. average surface of building elements)
and typical losses trough thermal bridging and air ventilation
and typical losses through thermal bridging and air ventilation
"""
data_tabula = pd.read_csv(snakemake.input.data_tabula,
@ -585,7 +585,7 @@ def map_to_lstrength(l_strength, df):
def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor):
"""
calculates total annual heat losses Q_ht for different insulation thiknesses
calculates total annual heat losses Q_ht for different insulation thicknesses
(l_strength), depening on current insulation state (u_values), standard
building topologies and air ventilation from TABULA (data_tabula) and
the accumulated difference between internal and external temperature
@ -790,7 +790,7 @@ def sample_dE_costs_area(area, area_tot, costs, dE_space, countries,
# drop not considered countries
cost_dE = cost_dE.reindex(countries,level=0)
# get share of residential and sevice floor area
# get share of residential and service floor area
sec_w = area_tot.value / area_tot.value.groupby(level=0).sum()
# get the total cost-energy-savings weight by sector area
tot = (cost_dE.mul(sec_w, axis=0).groupby(level="country_code").sum()
@ -863,7 +863,7 @@ if __name__ == "__main__":
data_tabula = prepare_building_topology(u_values)
# costs for retrofitting -------------------------------------------------
cost_retro, window_assumptions, cost_w, tax_w = prepare_cost_retro(country_iso_dic)
# temperature dependend parameters
# temperature dependent parameters
d_heat, temperature_factor = prepare_temperature_data()

2
scripts/helper.py
View File

@ -25,7 +25,7 @@ def override_component_attrs(directory):
Returns
-------
Dictionary of overriden component attributes.
Dictionary of overridden component attributes.
"""
attrs = Dict({k : v.copy() for k,v in component_attrs.items()})

4
scripts/plot_summary.py
View File

@ -401,7 +401,7 @@ def plot_carbon_budget_distribution(input_eurostat):
ax1.plot(emissions, color='black', linewidth=3, label=None)
#plot commited and uder-discussion targets
#plot committed and uder-discussion targets
#(notice that historical emissions include all countries in the
# network, but targets refer to EU)
ax1.plot([2020],[0.8*emissions[1990]],
@ -427,7 +427,7 @@ def plot_carbon_budget_distribution(input_eurostat):
ax1.plot([2050],[0.125*emissions[1990]],'ro',
marker='*', markersize=12, markerfacecolor='black',
markeredgecolor='black', label='EU commited target')
markeredgecolor='black', label='EU committed target')
ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
facecolor='white', frameon=True)

6
scripts/prepare_sector_network.py
View File

@ -1382,7 +1382,7 @@ def add_land_transport(n, costs):
def build_heat_demand(n):
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
# copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
@ -1724,7 +1724,7 @@ def add_heat(n, costs):
# minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
dE = retro_data.loc[(ct, sec), ("dE")]
# get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
# get additional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
# convert costs Euro/m^2 -> Euro/MWh
capital_cost = retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
@ -2565,7 +2565,7 @@ def set_temporal_aggregation(n, opts, solver_name):
if m is not None:
n = average_every_nhours(n, m.group(0))
break
# representive snapshots
# representative snapshots
m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
if m is not None:
sn = int(m[1])