resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
dirpath = building.download_data(
"https://zenodo.org/record/804244/files/Hydro_Inflow.zip",
unzip_file="Hydro_Inflow/")
files = os.listdir(dirpath)
x = pd.DataFrame()
reservoir_elements = building.read_elements('reservoir.csv')
ror_elements = building.read_elements('run-of-river.csv')
ror_elements.reset_index(inplace=True)
ror_elements.set_index(ror_elements['bus'].str[:2], inplace=True)
ror_elements.index.name = 'iso-country-code'
_embed = lambda x: json.dumps({'summed_max': x})
for f in files:
if any(c in f for c in config['countries']):
df = pd.read_csv(os.path.join(dirpath, f))
# select one year
df = df.loc[df['Year'] == config['weather_year']]
demand_mapper = {'Demand (GWh)': 'demand'}
types = [
'volatile-generator', 'dispatchable-generator', 'pumped-storage',
'reservoir', 'run-of-river', 'demand'
]
mappers = dict(
zip(types, [
volatile_mapper, dispatchable_mapper, storage_mapper, reservoir_mapper,
ror_mapper, demand_mapper
]))
element_dfs = dict(
zip(types, [
building.read_elements('volatile-generator.csv'),
building.read_elements('dispatchable-generator.csv'),
building.read_elements('pumped-storage.csv'),
building.read_elements('reservoir.csv'),
building.read_elements('run-of-river.csv'),
building.read_elements('demand.csv')
]))
elements = dict(zip(types, [{}, {}, {}, {}, {}, {}]))
for country in df.index:
for element_type, mapper in mappers.items():
for tech_key, tech in mapper.items():
element_name = tech + '-' + country
if element_name in element_dfs[element_type].index:
'Profiles for Run of River (ROR) components. The profile is assumed' +
' to be constant during the year.')
resource.descriptor['title'] = 'ROR profiles'
resource.descriptor['sources'] = [{'title': 'Assumption'}]
resource.commit()
if resource.valid:
resource.save('resources/' + resource.name + '.json')
year = str(config['weather_year'])
countries = config['countries']
sequences_df = pd.DataFrame(index=building.timeindex())
elements = building.read_elements('run-of-river.csv')
for c in countries:
# get sequence name from elements edge_parameters (include re-exp to also
# check for 'elec' or similar)
sequence_name = elements.at[
elements.index[elements.index.str.contains(c)][0], 'profile']
sequences_df[sequence_name] = 0.65
sequences_df.index = building.timeindex()
path = building.write_sequences('ror-profiles.csv', sequences_df)
create_resource(path)
'wind_onshore', 'wind_offshore', 'pv_rooftop', 'ccgt', 'gt',
'large_hydro_(ror)', 'biogas', 'biomass', 'battery', 'pumped_storage',
'coal_ccs'
]
def annuity(capex, n, wacc):
return capex * (wacc * (1 + wacc)**n) / ((1 + wacc)**n - 1)
df = pd.read_csv('archive/techno-economic-assumptions.csv', index_col=0)
df.drop('source (c,l,e)', axis=1, inplace=True)
types = config['sources']
element_dfs = {t: building.read_elements(t + '.csv') for t in types}
buses = config['buses']
data = dict(zip(types, [{}, {}, {}]))
df.index = df.index.str.replace(' ', '_')
for idx, row in df.iterrows():
if idx in considered_techs:
for b in buses:
element_name = idx + '_' + b
element = dict(row)
# add tech specific data
if row['type'] == 'dispatchable':
"fields": "bus",
"reference": {
"resource": "bus",
"fields": "name"
}
}]
resource.commit()
resource.descriptor
if resource.valid:
resource.save('resources/' + resource.name + '.json')
config = building.get_config()
buses = building.read_elements('bus.csv')
buses.index.name = 'bus'
elements = pd.DataFrame(buses.index)
elements['type'] = 'excess'
elements['name'] = 'excess-' + elements['bus'].str[:2]
elements['marginal_cost'] = 0
elements.set_index('name', inplace=True)
path = building.write_elements('excess.csv', elements)
create_resource(path)
suffix = '_load_old'
year = str(config['demand_year'])
countries = config['countries']
columns = [c + suffix for c in countries]
timeseries = raw_data[year][columns]
demand_total = timeseries.sum()
demand_profile = timeseries / demand_total
sequences_df = pd.DataFrame(index=demand_profile.index)
elements = building.read_elements('demand.csv')
for c in countries:
# get sequence name from elements edge_parameters (include re-exp to also
# check for 'elec' or similar)
sequence_name = elements.at[
elements.index[elements.index.str.contains(c)][0], 'profile']
sequences_df[sequence_name] = demand_profile[c + suffix].values
sequences_df.index = building.timeindex()
path = building.write_sequences('demand-profiles.csv', sequences_df)
create_resource(path)